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Yu Q, Ascensao JA, Okada T, Boyd O, Volz E, Hallatschek O. Lineage frequency time series reveal elevated levels of genetic drift in SARS-CoV-2 transmission in England. PLoS Pathog 2024; 20:e1012090. [PMID: 38620033 PMCID: PMC11045146 DOI: 10.1371/journal.ppat.1012090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 04/25/2024] [Accepted: 03/03/2024] [Indexed: 04/17/2024] Open
Abstract
Genetic drift in infectious disease transmission results from randomness of transmission and host recovery or death. The strength of genetic drift for SARS-CoV-2 transmission is expected to be high due to high levels of superspreading, and this is expected to substantially impact disease epidemiology and evolution. However, we don't yet have an understanding of how genetic drift changes over time or across locations. Furthermore, noise that results from data collection can potentially confound estimates of genetic drift. To address this challenge, we develop and validate a method to jointly infer genetic drift and measurement noise from time-series lineage frequency data. Our method is highly scalable to increasingly large genomic datasets, which overcomes a limitation in commonly used phylogenetic methods. We apply this method to over 490,000 SARS-CoV-2 genomic sequences from England collected between March 2020 and December 2021 by the COVID-19 Genomics UK (COG-UK) consortium and separately infer the strength of genetic drift for pre-B.1.177, B.1.177, Alpha, and Delta. We find that even after correcting for measurement noise, the strength of genetic drift is consistently, throughout time, higher than that expected from the observed number of COVID-19 positive individuals in England by 1 to 3 orders of magnitude, which cannot be explained by literature values of superspreading. Our estimates of genetic drift suggest low and time-varying establishment probabilities for new mutations, inform the parametrization of SARS-CoV-2 evolutionary models, and motivate future studies of the potential mechanisms for increased stochasticity in this system.
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Affiliation(s)
- QinQin Yu
- Department of Physics, University of California, Berkeley, California, United States of America
| | - Joao A. Ascensao
- Department of Bioengineering, University of California, Berkeley, California, United States of America
| | - Takashi Okada
- Department of Physics, University of California, Berkeley, California, United States of America
- Department of Integrative Biology, University of California, Berkeley, California, United States of America
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, Japan
- RIKEN iTHEMS, Wako, Saitama, Japan
| | | | - Olivia Boyd
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Erik Volz
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Oskar Hallatschek
- Department of Physics, University of California, Berkeley, California, United States of America
- Department of Integrative Biology, University of California, Berkeley, California, United States of America
- Peter Debye Institute for Soft Matter Physics, Leipzig University, Leipzig, Germany
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2
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Chang S, Shin KS, Park B, Park S, Shin J, Park H, Jung IK, Kim JH, Bae SE, Kim JO, Baek SH, Kim G, Hong JJ, Seo H, Volz E, Kang CY. Strategy to develop broadly effective multivalent COVID-19 vaccines against emerging variants based on Ad5/35 platform. Proc Natl Acad Sci U S A 2024; 121:e2313681121. [PMID: 38408238 DOI: 10.1073/pnas.2313681121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 01/28/2024] [Indexed: 02/28/2024] Open
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron strain has evolved into highly divergent variants with several sub-lineages. These newly emerging variants threaten the efficacy of available COVID-19 vaccines. To mitigate the occurrence of breakthrough infections and re-infections, and more importantly, to reduce the disease burden, it is essential to develop a strategy for producing updated multivalent vaccines that can provide broad neutralization against both currently circulating and emerging variants. We developed bivalent vaccine AdCLD-CoV19-1 BA.5/BA.2.75 and trivalent vaccines AdCLD-CoV19-1 XBB/BN.1/BQ.1.1 and AdCLD-CoV19-1 XBB.1.5/BN.1/BQ.1.1 using an Ad5/35 platform-based non-replicating recombinant adenoviral vector. We compared immune responses elicited by the monovalent and multivalent vaccines in mice and macaques. We found that the BA.5/BA.2.75 bivalent and the XBB/BN.1/BQ.1.1 and XBB.1.5/BN.1/BQ.1.1 trivalent vaccines exhibited improved cross-neutralization ability compared to their respective monovalent vaccines. These data suggest that the developed multivalent vaccines enhance immunity against circulating Omicron subvariants and effectively elicit neutralizing antibodies across a broad spectrum of SARS-CoV-2 variants.
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Affiliation(s)
- Soojeong Chang
- Research & Development Center, Cellid Co., Ltd., Seoul 08826, Republic of Korea
| | - Kwang-Soo Shin
- Research & Development Center, Cellid Co., Ltd., Seoul 08826, Republic of Korea
| | - Bongju Park
- Research & Development Center, Cellid Co., Ltd., Seoul 08826, Republic of Korea
| | - Seowoo Park
- Research & Development Center, Cellid Co., Ltd., Seoul 08826, Republic of Korea
| | - Jieun Shin
- Research & Development Center, Cellid Co., Ltd., Seoul 08826, Republic of Korea
| | - Hyemin Park
- Research & Development Center, Cellid Co., Ltd., Seoul 08826, Republic of Korea
| | - In Kyung Jung
- Research & Development Center, Cellid Co., Ltd., Seoul 08826, Republic of Korea
| | - Jong Heon Kim
- Research & Development Center, Cellid Co., Ltd., Seoul 08826, Republic of Korea
| | - Seong Eun Bae
- Science Unit, International Vaccine Institute, Seoul 08826, Republic of Korea
| | - Jae-Ouk Kim
- Science Unit, International Vaccine Institute, Seoul 08826, Republic of Korea
| | - Seung Ho Baek
- National Primate Research Centre, Korea Research Institute of Bioscience and Biotechnology, Cheongju, Chungcheongbuk 28116, Republic of Korea
| | - Green Kim
- National Primate Research Centre, Korea Research Institute of Bioscience and Biotechnology, Cheongju, Chungcheongbuk 28116, Republic of Korea
| | - Jung Joo Hong
- National Primate Research Centre, Korea Research Institute of Bioscience and Biotechnology, Cheongju, Chungcheongbuk 28116, Republic of Korea
- Korea Research Institute of Bioscience and Biotechnology School of Bioscience, Korea University of Science & Technology, Daejeon 34141, Republic of Korea
| | - Hyungseok Seo
- Laboratory of Cell & Gene Therapy, Research Institute of Pharmaceutical Sciences, College of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea
| | - Erik Volz
- Department of Infectious Disease Epidemiology, Medical Research Council Centre for Global Infectious Disease Analysis, Imperial College London, London W2 1PG, United Kingdom
| | - Chang-Yuil Kang
- Research & Development Center, Cellid Co., Ltd., Seoul 08826, Republic of Korea
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3
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Abstract
The massive scale of the global SARS-CoV-2 sequencing effort created new opportunities and challenges for understanding SARS-CoV-2 evolution. Rapid detection and assessment of new variants has become one of the principal objectives of genomic surveillance of SARS-CoV-2. Because of the pace and scale of sequencing, new strategies have been developed for characterizing fitness and transmissibility of emerging variants. In this Review, I discuss a wide range of approaches that have been rapidly developed in response to the public health threat posed by emerging variants, ranging from new applications of classic population genetics models to contemporary synthesis of epidemiological models and phylodynamic analysis. Many of these approaches can be adapted to other pathogens and will have increasing relevance as large-scale pathogen sequencing becomes a regular feature of many public health systems.
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Affiliation(s)
- Erik Volz
- Department of Infectious Disease Epidemiology, MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.
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4
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Tsui JLH, McCrone JT, Lambert B, Bajaj S, Inward RP, Bosetti P, Tegally H, Hill V, Pena RE, Zarebski AE, Peacock TP, Liu L, Wu N, Davis M, Bogoch II, Khan K, Kall M, Abdul Aziz NIB, Colquhoun R, O’Toole Á, Jackson B, Dasgupta A, Wilkinson E, de Oliveira T, Connor TR, Loman NJ, Colizza V, Fraser C, Volz E, Ji X, Gutierrez B, Chand M, Dellicour S, Cauchemez S, Raghwani J, Suchard MA, Lemey P, Rambaut A, Pybus OG, Kraemer MU. Genomic assessment of invasion dynamics of SARS-CoV-2 Omicron BA.1. Science 2023; 381:336-343. [PMID: 37471538 PMCID: PMC10866301 DOI: 10.1126/science.adg6605] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 06/15/2023] [Indexed: 07/22/2023]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) now arise in the context of heterogeneous human connectivity and population immunity. Through a large-scale phylodynamic analysis of 115,622 Omicron BA.1 genomes, we identified >6,000 introductions of the antigenically distinct VOC into England and analyzed their local transmission and dispersal history. We find that six of the eight largest English Omicron lineages were already transmitting when Omicron was first reported in southern Africa (22 November 2021). Multiple datasets show that importation of Omicron continued despite subsequent restrictions on travel from southern Africa as a result of export from well-connected secondary locations. Initiation and dispersal of Omicron transmission lineages in England was a two-stage process that can be explained by models of the country's human geography and hierarchical travel network. Our results enable a comparison of the processes that drive the invasion of Omicron and other VOCs across multiple spatial scales.
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Affiliation(s)
| | - John T. McCrone
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
- Helix, San Mateo, USA
| | - Ben Lambert
- Institute of Ecology and Evolution, University of Edinburgh, Edinburgh, UK
| | - Sumali Bajaj
- Department of Biology, University of Oxford, Oxford, UK
| | | | - Paolo Bosetti
- Institut Pasteur, Université Paris Cité, CNRS, Paris, France
| | - Houriiyah Tegally
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
- Centre for Epidemic Response and Innovation (CERI), School for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
| | - Verity Hill
- Helix, San Mateo, USA
- Yale University, New Haven, USA
| | | | | | - Thomas P. Peacock
- Department of Infectious Disease, Imperial College London, London, UK
- UK Health Security Agency, London, UK
| | | | - Neo Wu
- Google Research, Mountain View, USA
| | | | - Isaac I. Bogoch
- Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, Canada
| | - Kamran Khan
- BlueDot, Toronto, Canada
- Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, Canada
| | | | | | | | | | | | | | - Eduan Wilkinson
- BlueDot, Toronto, Canada
- Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, Canada
| | - Tulio de Oliveira
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
- Centre for Epidemic Response and Innovation (CERI), School for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
| | | | - Thomas R. Connor
- Pathogen Genomics Unit, Public Health Wales NHS Trust, Cardiff, UK
- School of Biosciences, The Sir Martin Evans Building, Cardiff University, UK
- Quadram Institute, Norwich, UK
| | - Nicholas J. Loman
- Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK
| | - Vittoria Colizza
- Sorbonne Université, INSERM, Institut Pierre Louis d’Épidémiologie et de Santé Publique (IPLESP), Paris, France
| | - Christophe Fraser
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, UK
- Pandemic Sciences Institute, University of Oxford, UK
| | - Erik Volz
- MRC Centre of Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Xiang Ji
- Department of Mathematics, Tulane University, New Orleans, USA
| | | | | | - Simon Dellicour
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Bruxelles, Belgium
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Simon Cauchemez
- Institut Pasteur, Université Paris Cité, CNRS, Paris, France
| | - Jayna Raghwani
- Department of Biology, University of Oxford, Oxford, UK
- Department of Pathobiology and Population Science, Royal Veterinary College, London, UK
| | - Marc A. Suchard
- Departments of Biostatistics, Biomathematics and Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | | | - Oliver G. Pybus
- Department of Biology, University of Oxford, Oxford, UK
- Pandemic Sciences Institute, University of Oxford, UK
- Department of Pathobiology and Population Science, Royal Veterinary College, London, UK
| | - Moritz U.G. Kraemer
- Department of Biology, University of Oxford, Oxford, UK
- Pandemic Sciences Institute, University of Oxford, UK
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5
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Perez-Guzman PN, Knock E, Imai N, Rawson T, Elmaci Y, Alcada J, Whittles LK, Thekke Kanapram D, Sonabend R, Gaythorpe KAM, Hinsley W, FitzJohn RG, Volz E, Verity R, Ferguson NM, Cori A, Baguelin M. Epidemiological drivers of transmissibility and severity of SARS-CoV-2 in England. Nat Commun 2023; 14:4279. [PMID: 37460537 PMCID: PMC10352350 DOI: 10.1038/s41467-023-39661-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 06/23/2023] [Indexed: 07/20/2023] Open
Abstract
As the SARS-CoV-2 pandemic progressed, distinct variants emerged and dominated in England. These variants, Wildtype, Alpha, Delta, and Omicron were characterized by variations in transmissibility and severity. We used a robust mathematical model and Bayesian inference framework to analyse epidemiological surveillance data from England. We quantified the impact of non-pharmaceutical interventions (NPIs), therapeutics, and vaccination on virus transmission and severity. Each successive variant had a higher intrinsic transmissibility. Omicron (BA.1) had the highest basic reproduction number at 8.3 (95% credible interval (CrI) 7.7-8.8). Varying levels of NPIs were crucial in controlling virus transmission until population immunity accumulated. Immune escape properties of Omicron decreased effective levels of immunity in the population by a third. Furthermore, in contrast to previous studies, we found Alpha had the highest basic infection fatality ratio (2.9%, 95% CrI 2.7-3.2), followed by Delta (2.2%, 95% CrI 2.0-2.4), Wildtype (1.2%, 95% CrI 1.1-1.2), and Omicron (0.7%, 95% CrI 0.6-0.8). Our findings highlight the importance of continued surveillance. Long-term strategies for monitoring and maintaining effective immunity against SARS-CoV-2 are critical to inform the role of NPIs to effectively manage future variants with potentially higher intrinsic transmissibility and severe outcomes.
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Affiliation(s)
- Pablo N Perez-Guzman
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK
| | - Edward Knock
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK
| | - Natsuko Imai
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK
| | - Thomas Rawson
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK
| | - Yasin Elmaci
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK
| | - Joana Alcada
- Adult Intensive Care Unit, Royal Brompton Hospital, London, UK
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| | - Lilith K Whittles
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK
| | - Divya Thekke Kanapram
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK
- Department of Engineering, Division of Electrical Engineering, University of Cambridge, Cambridge, UK
| | - Raphael Sonabend
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK
| | - Katy A M Gaythorpe
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK
| | - Wes Hinsley
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK
| | - Richard G FitzJohn
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK
| | - Erik Volz
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK
| | - Robert Verity
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK
| | - Neil M Ferguson
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK
| | - Marc Baguelin
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK.
- National Institute for Health Research (NIHR) Health Protection Research Unit (HPRU) in Modelling and Health Economics, London, UK.
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
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6
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Eales O, Page AJ, Tang SN, Walters CE, Wang H, Haw D, Trotter AJ, Le Viet T, Foster-Nyarko E, Prosolek S, Atchison C, Ashby D, Cooke G, Barclay W, Donnelly CA, O’Grady J, Volz E, Darzi A, Ward H, Elliott P, Riley S. The use of representative community samples to assess SARS-CoV-2 lineage competition: Alpha outcompetes Beta and wild-type in England from January to March 2021. Microb Genom 2023; 9:mgen000887. [PMID: 36745545 PMCID: PMC9997751 DOI: 10.1099/mgen.0.000887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Genomic surveillance for SARS-CoV-2 lineages informs our understanding of possible future changes in transmissibility and vaccine efficacy and will be a high priority for public health for the foreseeable future. However, small changes in the frequency of one lineage over another are often difficult to interpret because surveillance samples are obtained using a variety of methods all of which are known to contain biases. As a case study, using an approach which is largely free of biases, we here describe lineage dynamics and phylogenetic relationships of the Alpha and Beta variant in England during the first 3 months of 2021 using sequences obtained from a random community sample who provided a throat and nose swab for rt-PCR as part of the REal-time Assessment of Community Transmission-1 (REACT-1) study. Overall, diversity decreased during the first quarter of 2021, with the Alpha variant (first identified in Kent) becoming predominant, driven by a reproduction number 0.3 higher than for the prior wild-type. During January, positive samples were more likely to be Alpha in those aged 18 to 54 years old. Although individuals infected with the Alpha variant were no more likely to report one or more classic COVID-19 symptoms compared to those infected with wild-type, they were more likely to be antibody-positive 6 weeks after infection. Further, viral load was higher in those infected with the Alpha variant as measured by cycle threshold (Ct) values. The presence of infections with non-imported Beta variant (first identified in South Africa) during January, but not during February or March, suggests initial establishment in the community followed by fade-out. However, this occurred during a period of stringent social distancing. These results highlight how sequence data from representative community surveys such as REACT-1 can augment routine genomic surveillance during periods of lineage diversity.
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Affiliation(s)
- Oliver Eales
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
- *Correspondence: Oliver Eales,
| | | | - Sonja N. Tang
- School of Public Health, Imperial College London, London, UK
| | - Caroline E. Walters
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Haowei Wang
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - David Haw
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | | | | | | | | | | | - Deborah Ashby
- School of Public Health, Imperial College London, London, UK
| | - Graham Cooke
- Department of Infectious Disease, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
| | - Wendy Barclay
- Department of Infectious Disease, Imperial College London, London, UK
| | - Christl A. Donnelly
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | | | - Erik Volz
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | | | - Ara Darzi
- Imperial College Healthcare NHS Trust, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
- Institute of Global Health Innovation at Imperial College London, London, UK
| | - Helen Ward
- School of Public Health, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
| | - Paul Elliott
- School of Public Health, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Health Data Research (HDR) UK London at Imperial College, London, UK
- UK Dementia Research Institute at Imperial College, London, UK
- *Correspondence: Paul Elliott,
| | - Steven Riley
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
- *Correspondence: Steven Riley,
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7
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McCrone JT, Hill V, Bajaj S, Pena RE, Lambert BC, Inward R, Bhatt S, Volz E, Ruis C, Dellicour S, Baele G, Zarebski AE, Sadilek A, Wu N, Schneider A, Ji X, Raghwani J, Jackson B, Colquhoun R, O'Toole Á, Peacock TP, Twohig K, Thelwall S, Dabrera G, Myers R, Faria NR, Huber C, Bogoch II, Khan K, du Plessis L, Barrett JC, Aanensen DM, Barclay WS, Chand M, Connor T, Loman NJ, Suchard MA, Pybus OG, Rambaut A, Kraemer MUG. Context-specific emergence and growth of the SARS-CoV-2 Delta variant. Nature 2022; 610:154-160. [PMID: 35952712 PMCID: PMC9534748 DOI: 10.1038/s41586-022-05200-3] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 08/05/2022] [Indexed: 02/01/2023]
Abstract
The SARS-CoV-2 Delta (Pango lineage B.1.617.2) variant of concern spread globally, causing resurgences of COVID-19 worldwide1,2. The emergence of the Delta variant in the UK occurred on the background of a heterogeneous landscape of immunity and relaxation of non-pharmaceutical interventions. Here we analyse 52,992 SARS-CoV-2 genomes from England together with 93,649 genomes from the rest of the world to reconstruct the emergence of Delta and quantify its introduction to and regional dissemination across England in the context of changing travel and social restrictions. Using analysis of human movement, contact tracing and virus genomic data, we find that the geographic focus of the expansion of Delta shifted from India to a more global pattern in early May 2021. In England, Delta lineages were introduced more than 1,000 times and spread nationally as non-pharmaceutical interventions were relaxed. We find that hotel quarantine for travellers reduced onward transmission from importations; however, the transmission chains that later dominated the Delta wave in England were seeded before travel restrictions were introduced. Increasing inter-regional travel within England drove the nationwide dissemination of Delta, with some cities receiving more than 2,000 observable lineage introductions from elsewhere. Subsequently, increased levels of local population mixing-and not the number of importations-were associated with the faster relative spread of Delta. The invasion dynamics of Delta depended on spatial heterogeneity in contact patterns, and our findings will inform optimal spatial interventions to reduce the transmission of current and future variants of concern, such as Omicron (Pango lineage B.1.1.529).
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Affiliation(s)
- John T McCrone
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Verity Hill
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Sumali Bajaj
- Department of Zoology, University of Oxford, Oxford, UK
| | | | - Ben C Lambert
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
| | - Rhys Inward
- Department of Zoology, University of Oxford, Oxford, UK
- MRC Centre of Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Samir Bhatt
- MRC Centre of Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Erik Volz
- MRC Centre of Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Christopher Ruis
- Molecular Immunity Unit, Department of Medicine, Cambridge University, Cambridge, UK
| | - Simon Dellicour
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Bruxelles, Belgium
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | | | | | - Neo Wu
- Google, Mountain View, CA, USA
| | | | - Xiang Ji
- Department of Mathematics, School of Science and Engineering, Tulane University, New Orleans, LA, USA
| | | | - Ben Jackson
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Rachel Colquhoun
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Áine O'Toole
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Thomas P Peacock
- Department of Infectious Disease, Imperial College London, London, UK
- UK Health Security Agency, London, UK
| | | | | | | | | | - Nuno R Faria
- Department of Zoology, University of Oxford, Oxford, UK
- MRC Centre of Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
- Instituto de Medicina Tropical, Faculdade de Medicina da Universidade de Sao Paulo, Sao Paulo, Brazil
| | | | - Isaac I Bogoch
- Divisions of Internal Medicine and Infectious Diseases, Toronto General Hospital, University Health Network, Toronto, Ontario, Canada
- Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, Ontario, Canada
| | - Kamran Khan
- BlueDot, Toronto, Ontario, Canada
- Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, Ontario, Canada
- Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, Ontario, Canada
| | - Louis du Plessis
- Department of Zoology, University of Oxford, Oxford, UK
- Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | | | - David M Aanensen
- Centre for Genomic Pathogen Surveillance, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Wendy S Barclay
- Department of Infectious Disease, Imperial College London, London, UK
| | | | - Thomas Connor
- Pathogen Genomics Unit, Public Health Wales NHS Trust, Cardiff, UK
- School of Biosciences, The Sir Martin Evans Building, Cardiff University, Cardiff, UK
- Quadram Institute, Norwich, UK
| | - Nicholas J Loman
- Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK
| | - Marc A Suchard
- Departments of Biostatistics, Biomathematics and Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Oliver G Pybus
- Department of Zoology, University of Oxford, Oxford, UK.
- Department of Pathobiology and Population Sciences, Royal Veterinary College London, London, UK.
- Pandemic Sciences Institute, University of Oxford, Oxford, UK.
| | - Andrew Rambaut
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK.
| | - Moritz U G Kraemer
- Department of Zoology, University of Oxford, Oxford, UK.
- Pandemic Sciences Institute, University of Oxford, Oxford, UK.
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8
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Hill V, Du Plessis L, Peacock TP, Aggarwal D, Colquhoun R, Carabelli AM, Ellaby N, Gallagher E, Groves N, Jackson B, McCrone JT, O’Toole Á, Price A, Sanderson T, Scher E, Southgate J, Volz E, Barclay WS, Barrett JC, Chand M, Connor T, Goodfellow I, Gupta RK, Harrison EM, Loman N, Myers R, Robertson DL, Pybus OG, Rambaut A. The origins and molecular evolution of SARS-CoV-2 lineage B.1.1.7 in the UK. Virus Evol 2022; 8:veac080. [PMID: 36533153 PMCID: PMC9752794 DOI: 10.1093/ve/veac080] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 07/28/2022] [Accepted: 08/25/2022] [Indexed: 01/06/2023] Open
Abstract
The first SARS-CoV-2 variant of concern (VOC) to be designated was lineage B.1.1.7, later labelled by the World Health Organization as Alpha. Originating in early autumn but discovered in December 2020, it spread rapidly and caused large waves of infections worldwide. The Alpha variant is notable for being defined by a long ancestral phylogenetic branch with an increased evolutionary rate, along which only two sequences have been sampled. Alpha genomes comprise a well-supported monophyletic clade within which the evolutionary rate is typical of SARS-CoV-2. The Alpha epidemic continued to grow despite the continued restrictions on social mixing across the UK and the imposition of new restrictions, in particular, the English national lockdown in November 2020. While these interventions succeeded in reducing the absolute number of cases, the impact of these non-pharmaceutical interventions was predominantly to drive the decline of the SARS-CoV-2 lineages that preceded Alpha. We investigate the only two sampled sequences that fall on the branch ancestral to Alpha. We find that one is likely to be a true intermediate sequence, providing information about the order of mutational events that led to Alpha. We explore alternate hypotheses that can explain how Alpha acquired a large number of mutations yet remained largely unobserved in a region of high genomic surveillance: an under-sampled geographical location, a non-human animal population, or a chronically infected individual. We conclude that the latter provides the best explanation of the observed behaviour and dynamics of the variant, although the individual need not be immunocompromised, as persistently infected immunocompetent hosts also display a higher within-host rate of evolution. Finally, we compare the ancestral branches and mutation profiles of other VOCs and find that Delta appears to be an outlier both in terms of the genomic locations of its defining mutations and a lack of the rapid evolutionary rate on its ancestral branch. As new variants, such as Omicron, continue to evolve (potentially through similar mechanisms), it remains important to investigate the origins of other variants to identify ways to potentially disrupt their evolution and emergence.
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Affiliation(s)
- Verity Hill
- Ashworth Laboratories, Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3FL, UK
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
| | - Louis Du Plessis
- Department of Biology, University of Oxford, 11a Mansfield Rd, Oxford OX1 3SZ, UK
- Department of Biosystems Science and Engineering, ETH Zürich, Zürich, Switzerland
| | - Thomas P Peacock
- Department of Infectious Disease, Imperial College London, London W2 1PG, UK
| | - Dinesh Aggarwal
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1RQ, UK
- UK Health Security Agency, 61 Colindale Avenue, London NW9 5EQ, UK
- Department of Medicine, University of Cambridge, Cambridge, UK
- Cambridge University Hospital NHS Foundation Trust, Cambridge, UK
| | - Rachel Colquhoun
- Ashworth Laboratories, Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3FL, UK
| | | | - Nicholas Ellaby
- UK Health Security Agency, 61 Colindale Avenue, London NW9 5EQ, UK
| | - Eileen Gallagher
- UK Health Security Agency, 61 Colindale Avenue, London NW9 5EQ, UK
| | - Natalie Groves
- UK Health Security Agency, 61 Colindale Avenue, London NW9 5EQ, UK
| | - Ben Jackson
- Ashworth Laboratories, Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3FL, UK
| | - J T McCrone
- Ashworth Laboratories, Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3FL, UK
| | - Áine O’Toole
- Ashworth Laboratories, Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3FL, UK
| | - Anna Price
- School of Biosciences, The Sir Martin Evans Building, Cardiff University, Cardiff CF10 AX, UK
| | - Theo Sanderson
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1RQ, UK
- The Francis Crick Institute, 1 Midland Rd, London NW1 1AT, UK
| | - Emily Scher
- Ashworth Laboratories, Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3FL, UK
| | - Joel Southgate
- School of Biosciences, The Sir Martin Evans Building, Cardiff University, Cardiff CF10 AX, UK
| | - Erik Volz
- MRC Unit for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Wendy S Barclay
- Department of Infectious Disease, Imperial College London, London W2 1PG, UK
| | - Jeffrey C Barrett
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1RQ, UK
| | - Meera Chand
- UK Health Security Agency, 61 Colindale Avenue, London NW9 5EQ, UK
- Guy’s and St Thomas’ Hospital NHS Trust, St Thomas’ Hospital, Westminster Bridge Rd, London SE1 7EH, UK
| | - Thomas Connor
- School of Biosciences, The Sir Martin Evans Building, Cardiff University, Cardiff CF10 AX, UK
- Pathogen Genomics Unit, Public Health Wales NHS Trust, Cardiff CF14 4XW, UK
| | - Ian Goodfellow
- Department of Pathology, University of Cambridge, Cambridge CB2 1QP, UK
| | - Ravindra K Gupta
- Department of Medicine, University of Cambridge, Cambridge, UK
- Africa Health Research Institute, Durban, South Africa
| | - Ewan M Harrison
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1RQ, UK
- Department of Medicine, University of Cambridge, Cambridge, UK
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Nicholas Loman
- Institute of Microbiology and Infection, University of Birmingham, Birmingham B15 2TT, UK
| | - Richard Myers
- UK Health Security Agency, 61 Colindale Avenue, London NW9 5EQ, UK
| | - David L Robertson
- MRC-University of Glasgow Centre for Virus Research, 464 Bearsden Road, Glasgow G61 1QH, Scotland, UK
| | - Oliver G Pybus
- Department of Biology, University of Oxford, 11a Mansfield Rd, Oxford OX1 3SZ, UK
- Department of Pathobiology and Population Science, The Royal Veterinary College, London, UK
| | - Andrew Rambaut
- Ashworth Laboratories, Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3FL, UK
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9
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Nascimento FF, Ragonnet-Cronin M, Golubchik T, Danaviah S, Derache A, Fraser C, Volz E. Evaluating whole HIV-1 genome sequence for estimation of incidence and migration in a rural South African community. Wellcome Open Res 2022. [DOI: 10.12688/wellcomeopenres.17891.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Background: South Africa has the largest number of people living with HIV (PLWHIV) in the world, with HIV prevalence and transmission patterns varying greatly between provinces. Transmission between regions is still poorly understood, but phylodynamics of HIV-1 evolution can reveal how many infections are attributable to contacts outside a given community. We analysed whole genome HIV-1 genetic sequences to estimate incidence and the proportion of transmissions between communities in Hlabisa, a rural South African community. Methods: We separately analysed HIV-1 for gag, pol, and env genes sampled from 2,503 PLWHIV. We estimated time-scaled phylogenies by maximum likelihood under a molecular clock model. Phylodynamic models were fitted to time-scaled trees to estimate transmission rates, effective number of infections, incidence through time, and the proportion of infections imported to Hlabisa. We also partitioned time-scaled phylogenies with significantly different distributions of coalescent times. Results: Phylodynamic analyses showed similar trends in epidemic growth rates between 1980 and 1990. Model-based estimates of incidence and effective number of infections were consistent across genes. Parameter estimates with gag were generally smaller than those estimated with pol and env. When estimating the proportions of new infections in Hlabisa from immigration or transmission from external sources, our posterior median estimates were 85% (95% credible interval (CI) = 78%–92%) for gag, 62% (CI = 40%–78%) for pol, and 77% (CI = 58%–90%) for env in 2015. Analysis of phylogenetic partitions by gene showed that most close global reference sequences clustered within a single partition. This suggests local evolving epidemics or potential unmeasured heterogeneity in the population. Conclusions: We estimated consistent epidemic dynamic trends for gag, pol and env genes using phylodynamic models. There was a high probability that new infections were not attributable to endogenous transmission within Hlabisa, suggesting high inter-connectedness between communities in rural South Africa.
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Vöhringer HS, Sanderson T, Sinnott M, De Maio N, Nguyen T, Goater R, Schwach F, Harrison I, Hellewell J, Ariani CV, Gonçalves S, Jackson DK, Johnston I, Jung AW, Saint C, Sillitoe J, Suciu M, Goldman N, Panovska-Griffiths J, Birney E, Volz E, Funk S, Kwiatkowski D, Chand M, Martincorena I, Barrett JC, Gerstung M. Publisher Correction: Genomic reconstruction of the SARS CoV-2 epidemic in England. Nature 2022; 606:E18. [PMID: 35701578 PMCID: PMC9172604 DOI: 10.1038/s41586-022-04887-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Harald S Vöhringer
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Hinxton, UK
| | - Theo Sanderson
- Wellcome Sanger Institute, Hinxton, UK
- The Francis Crick Institute, London, UK
| | | | - Nicola De Maio
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Hinxton, UK
| | | | | | - Frank Schwach
- Wellcome Sanger Institute, Hinxton, UK
- Public Health England, London, UK
| | | | - Joel Hellewell
- London School of Hygiene & Tropical Medicine, London, UK
| | | | | | | | | | - Alexander W Jung
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Hinxton, UK
| | | | | | | | - Nick Goldman
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Hinxton, UK
| | | | | | | | - Ewan Birney
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Hinxton, UK
| | - Erik Volz
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Sebastian Funk
- London School of Hygiene & Tropical Medicine, London, UK
| | | | - Meera Chand
- Public Health England, London, UK
- Guy's and St Thomas' NHS Foundation Trust, London, UK
| | | | | | - Moritz Gerstung
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Hinxton, UK.
- Division for AI in Oncology, German Cancer Research Centre DKFZ, Heidelberg, Germany.
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11
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Nyberg T, Ferguson NM, Nash SG, Webster HH, Flaxman S, Andrews N, Hinsley W, Bernal JL, Kall M, Bhatt S, Blomquist P, Zaidi A, Volz E, Aziz NA, Harman K, Funk S, Abbott S, Hope R, Charlett A, Chand M, Ghani AC, Seaman SR, Dabrera G, De Angelis D, Presanis AM, Thelwall S. Comparative analysis of the risks of hospitalisation and death associated with SARS-CoV-2 omicron (B.1.1.529) and delta (B.1.617.2) variants in England: a cohort study. Lancet 2022; 399:1303-1312. [PMID: 35305296 PMCID: PMC8926413 DOI: 10.1016/s0140-6736(22)00462-7] [Citation(s) in RCA: 674] [Impact Index Per Article: 337.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 02/17/2022] [Accepted: 02/25/2022] [Indexed: 02/06/2023]
Abstract
BACKGROUND The omicron variant (B.1.1.529) of SARS-CoV-2 has demonstrated partial vaccine escape and high transmissibility, with early studies indicating lower severity of infection than that of the delta variant (B.1.617.2). We aimed to better characterise omicron severity relative to delta by assessing the relative risk of hospital attendance, hospital admission, or death in a large national cohort. METHODS Individual-level data on laboratory-confirmed COVID-19 cases resident in England between Nov 29, 2021, and Jan 9, 2022, were linked to routine datasets on vaccination status, hospital attendance and admission, and mortality. The relative risk of hospital attendance or admission within 14 days, or death within 28 days after confirmed infection, was estimated using proportional hazards regression. Analyses were stratified by test date, 10-year age band, ethnicity, residential region, and vaccination status, and were further adjusted for sex, index of multiple deprivation decile, evidence of a previous infection, and year of age within each age band. A secondary analysis estimated variant-specific and vaccine-specific vaccine effectiveness and the intrinsic relative severity of omicron infection compared with delta (ie, the relative risk in unvaccinated cases). FINDINGS The adjusted hazard ratio (HR) of hospital attendance (not necessarily resulting in admission) with omicron compared with delta was 0·56 (95% CI 0·54-0·58); for hospital admission and death, HR estimates were 0·41 (0·39-0·43) and 0·31 (0·26-0·37), respectively. Omicron versus delta HR estimates varied with age for all endpoints examined. The adjusted HR for hospital admission was 1·10 (0·85-1·42) in those younger than 10 years, decreasing to 0·25 (0·21-0·30) in 60-69-year-olds, and then increasing to 0·47 (0·40-0·56) in those aged at least 80 years. For both variants, past infection gave some protection against death both in vaccinated (HR 0·47 [0·32-0·68]) and unvaccinated (0·18 [0·06-0·57]) cases. In vaccinated cases, past infection offered no additional protection against hospital admission beyond that provided by vaccination (HR 0·96 [0·88-1·04]); however, for unvaccinated cases, past infection gave moderate protection (HR 0·55 [0·48-0·63]). Omicron versus delta HR estimates were lower for hospital admission (0·30 [0·28-0·32]) in unvaccinated cases than the corresponding HR estimated for all cases in the primary analysis. Booster vaccination with an mRNA vaccine was highly protective against hospitalisation and death in omicron cases (HR for hospital admission 8-11 weeks post-booster vs unvaccinated: 0·22 [0·20-0·24]), with the protection afforded after a booster not being affected by the vaccine used for doses 1 and 2. INTERPRETATION The risk of severe outcomes following SARS-CoV-2 infection is substantially lower for omicron than for delta, with higher reductions for more severe endpoints and significant variation with age. Underlying the observed risks is a larger reduction in intrinsic severity (in unvaccinated individuals) counterbalanced by a reduction in vaccine effectiveness. Documented previous SARS-CoV-2 infection offered some protection against hospitalisation and high protection against death in unvaccinated individuals, but only offered additional protection in vaccinated individuals for the death endpoint. Booster vaccination with mRNA vaccines maintains over 70% protection against hospitalisation and death in breakthrough confirmed omicron infections. FUNDING Medical Research Council, UK Research and Innovation, Department of Health and Social Care, National Institute for Health Research, Community Jameel, and Engineering and Physical Sciences Research Council.
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Affiliation(s)
- Tommy Nyberg
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
| | - Neil M Ferguson
- NIHR Health Protection Research Unit for Modelling and Health Economics, MRC Centre for Global Infectious Disease Analysis, Jameel Institute, Imperial College London, London, UK.
| | - Sophie G Nash
- COVID-19 National Epidemiology Cell, UK Health Security Agency, London, UK
| | - Harriet H Webster
- COVID-19 National Epidemiology Cell, UK Health Security Agency, London, UK
| | - Seth Flaxman
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Nick Andrews
- COVID-19 Surveillance Cell, UK Health Security Agency, London, UK
| | - Wes Hinsley
- NIHR Health Protection Research Unit for Modelling and Health Economics, MRC Centre for Global Infectious Disease Analysis, Jameel Institute, Imperial College London, London, UK
| | - Jamie Lopez Bernal
- NIHR Health Protection Research Unit for Respiratory Infections, Imperial College London, London, UK; COVID-19 Surveillance Cell, UK Health Security Agency, London, UK
| | - Meaghan Kall
- COVID-19 National Epidemiology Cell, UK Health Security Agency, London, UK
| | - Samir Bhatt
- NIHR Health Protection Research Unit for Modelling and Health Economics, MRC Centre for Global Infectious Disease Analysis, Jameel Institute, Imperial College London, London, UK
| | - Paula Blomquist
- Outbreak Surveillance Team, UK Health Security Agency, London, UK
| | - Asad Zaidi
- COVID-19 National Epidemiology Cell, UK Health Security Agency, London, UK
| | - Erik Volz
- NIHR Health Protection Research Unit for Modelling and Health Economics, MRC Centre for Global Infectious Disease Analysis, Jameel Institute, Imperial College London, London, UK
| | - Nurin Abdul Aziz
- COVID-19 National Epidemiology Cell, UK Health Security Agency, London, UK
| | - Katie Harman
- COVID-19 National Epidemiology Cell, UK Health Security Agency, London, UK
| | - Sebastian Funk
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Sam Abbott
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Russell Hope
- COVID-19 National Epidemiology Cell, UK Health Security Agency, London, UK
| | - Andre Charlett
- NIHR Health Protection Research Unit for Modelling and Health Economics, MRC Centre for Global Infectious Disease Analysis, Jameel Institute, Imperial College London, London, UK; Statistics, Modelling and Economics Department, UK Health Security Agency, London, UK; Joint Modelling Team, UK Health Security Agency, London, UK; NIHR Health Protection Research Unit for Behavioural Science and Evaluation at the University of Bristol, University of the West of England, and University of Cambridge, Bristol, UK
| | - Meera Chand
- COVID-19 Genomics Cell, UK Health Security Agency, London, UK
| | - Azra C Ghani
- NIHR Health Protection Research Unit for Modelling and Health Economics, MRC Centre for Global Infectious Disease Analysis, Jameel Institute, Imperial College London, London, UK
| | - Shaun R Seaman
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Gavin Dabrera
- COVID-19 National Epidemiology Cell, UK Health Security Agency, London, UK
| | - Daniela De Angelis
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK; Statistics, Modelling and Economics Department, UK Health Security Agency, London, UK; Joint Modelling Team, UK Health Security Agency, London, UK; NIHR Health Protection Research Unit for Behavioural Science and Evaluation at the University of Bristol, University of the West of England, and University of Cambridge, Bristol, UK
| | - Anne M Presanis
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Simon Thelwall
- COVID-19 National Epidemiology Cell, UK Health Security Agency, London, UK
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12
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Stirrup O, Tostevin A, Ragonnet-Cronin M, Volz E, Burns F, Delpech V, Dunn D. Diagnosis delays in the UK according to pre or postmigration acquisition of HIV. AIDS 2022; 36:415-422. [PMID: 35084383 PMCID: PMC7612284 DOI: 10.1097/qad.0000000000003110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
OBJECTIVES The aim of this study was to evaluate whether infection occurred pre or postmigration and the associated diagnosis delay in migrants diagnosed with HIV in the UK. DESIGN We analyzed a cohort of individuals diagnosed with HIV in the UK in 2014-2016 born in Africa or elsewhere in Europe. Inclusion criteria were arrival within 15 years before diagnosis, availability of HIV pol sequence, and viral subtype shared by at least 10 individuals. METHODS We examined phylogenies for evidence of infection after entry into the UK and incorporated this information into a Bayesian analysis of timing of infection using biomarkers of CD4+ cell count, avidity assays, proportion of ambiguous nucleotides in viral sequences, and last negative test dates where available. RESULTS One thousand, two hundred and fifty-six individuals were included. The final model indicated that HIV was acquired postmigration for most MSM born in Europe (posterior expectation 65%, 95% credibility interval 64-67%) or Africa (65%, 62-69%), whereas a minority (20-30%) of men and women with heterosexual transmission acquired HIV postmigration. Estimated diagnosis delays were lower for MSM than for those with heterosexual transmission, and were lower for those with postmigration infection across all subgroups. For MSM acquiring HIV postmigration, the estimated mean time to diagnosis was less than one year, but for those who acquired HIV premigration, the mean time from infection to diagnosis was more than five years for all subgroups. CONCLUSION Acquisition of HIV postmigration is common, particularly among MSM, calling for prevention efforts aimed at migrant communities. Delays in diagnosis reinforce the need for targeted testing initiatives.
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Affiliation(s)
- Oliver Stirrup
- Institute for Global Health, University College London, London, UK
| | - Anna Tostevin
- Institute for Global Health, University College London, London, UK
| | - Manon Ragonnet-Cronin
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Erik Volz
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Fiona Burns
- Institute for Global Health, University College London, London, UK
- Royal Free London NHS Foundation Trust, London, UK
| | - Valerie Delpech
- HIV and STI Department, National Infection Service, Public Health England, London, United Kingdom
| | - David Dunn
- Institute for Global Health, University College London, London, UK
- MRC Clinical Trials Unit, University College London, London, UK
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13
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Aggarwal D, Page AJ, Schaefer U, Savva GM, Myers R, Volz E, Ellaby N, Platt S, Groves N, Gallagher E, Tumelty NM, Le Viet T, Hughes GJ, Chen C, Turner C, Logan S, Harrison A, Peacock SJ, Chand M, Harrison EM. Genomic assessment of quarantine measures to prevent SARS-CoV-2 importation and transmission. Nat Commun 2022; 13:1012. [PMID: 35197443 PMCID: PMC8866425 DOI: 10.1038/s41467-022-28371-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 01/18/2022] [Indexed: 01/16/2023] Open
Abstract
Mitigation of SARS-CoV-2 transmission from international travel is a priority. We evaluated the effectiveness of travellers being required to quarantine for 14-days on return to England in Summer 2020. We identified 4,207 travel-related SARS-CoV-2 cases and their contacts, and identified 827 associated SARS-CoV-2 genomes. Overall, quarantine was associated with a lower rate of contacts, and the impact of quarantine was greatest in the 16-20 age-group. 186 SARS-CoV-2 genomes were sufficiently unique to identify travel-related clusters. Fewer genomically-linked cases were observed for index cases who returned from countries with quarantine requirement compared to countries with no quarantine requirement. This difference was explained by fewer importation events per identified genome for these cases, as opposed to fewer onward contacts per case. Overall, our study demonstrates that a 14-day quarantine period reduces, but does not completely eliminate, the onward transmission of imported cases, mainly by dissuading travel to countries with a quarantine requirement.
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Affiliation(s)
- Dinesh Aggarwal
- University of Cambridge, Department of Medicine, Cambridge, UK. .,Public Health England, 61 Colindale Ave, London, NW9 5EQ, UK. .,Cambridge University Hospital NHS Foundation Trust, Cambridge, UK. .,Wellcome Sanger Institute, Hinxton, Cambridge, UK.
| | - Andrew J Page
- Quadram Institute Bioscience, Norwich Research Park, Norwich, NR4 7UQ, UK
| | - Ulf Schaefer
- Public Health England, 61 Colindale Ave, London, NW9 5EQ, UK
| | - George M Savva
- Quadram Institute Bioscience, Norwich Research Park, Norwich, NR4 7UQ, UK
| | - Richard Myers
- Public Health England, 61 Colindale Ave, London, NW9 5EQ, UK
| | - Erik Volz
- Imperial College London, Department of Infectious Disease Epidemiology, London, UK
| | - Nicholas Ellaby
- Public Health England, 61 Colindale Ave, London, NW9 5EQ, UK
| | - Steven Platt
- Public Health England, 61 Colindale Ave, London, NW9 5EQ, UK
| | - Natalie Groves
- Public Health England, 61 Colindale Ave, London, NW9 5EQ, UK
| | | | - Niamh M Tumelty
- University of Cambridge, Cambridge University Libraries, Cambridge, UK
| | - Thanh Le Viet
- Quadram Institute Bioscience, Norwich Research Park, Norwich, NR4 7UQ, UK
| | - Gareth J Hughes
- Public Health England National Infections Service, Field Service, Leeds, UK
| | - Cong Chen
- Public Health England, 61 Colindale Ave, London, NW9 5EQ, UK
| | - Charlie Turner
- Public Health England, 61 Colindale Ave, London, NW9 5EQ, UK
| | - Sophie Logan
- Public Health England, National Infections Service, Field Service, Nottingham, UK
| | - Abbie Harrison
- Public Health England, 61 Colindale Ave, London, NW9 5EQ, UK
| | | | - Sharon J Peacock
- University of Cambridge, Department of Medicine, Cambridge, UK.,Public Health England, 61 Colindale Ave, London, NW9 5EQ, UK.,Cambridge University Hospital NHS Foundation Trust, Cambridge, UK.,Wellcome Sanger Institute, Hinxton, Cambridge, UK
| | - Meera Chand
- Public Health England, 61 Colindale Ave, London, NW9 5EQ, UK
| | - Ewan M Harrison
- University of Cambridge, Department of Medicine, Cambridge, UK. .,Public Health England, 61 Colindale Ave, London, NW9 5EQ, UK. .,Wellcome Sanger Institute, Hinxton, Cambridge, UK. .,University of Cambridge, Department of Public Health and Primary Care, Cambridge, UK.
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14
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McCrone JT, Hill V, Bajaj S, Pena RE, Lambert BC, Inward R, Bhatt S, Volz E, Ruis C, Dellicour S, Baele G, Zarebski AE, Sadilek A, Wu N, Schneider A, Ji X, Raghwani J, Jackson B, Colquhoun R, O'Toole Á, Peacock TP, Twohig K, Thelwall S, Dabrera G, Myers R, Faria NR, Huber C, Bogoch II, Khan K, du Plessis L, Barrett JC, Aanensen DM, Barclay WS, Chand M, Connor T, Loman NJ, Suchard MA, Pybus OG, Rambaut A, Kraemer MUG. Context-specific emergence and growth of the SARS-CoV-2 Delta variant. medRxiv 2021:2021.12.14.21267606. [PMID: 34981069 PMCID: PMC8722612 DOI: 10.1101/2021.12.14.21267606] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
The Delta variant of concern of SARS-CoV-2 has spread globally causing large outbreaks and resurgences of COVID-19 cases 1-3 . The emergence of Delta in the UK occurred on the background of a heterogeneous landscape of immunity and relaxation of non-pharmaceutical interventions 4,5 . Here we analyse 52,992 Delta genomes from England in combination with 93,649 global genomes to reconstruct the emergence of Delta, and quantify its introduction to and regional dissemination across England, in the context of changing travel and social restrictions. Through analysis of human movement, contact tracing, and virus genomic data, we find that the focus of geographic expansion of Delta shifted from India to a more global pattern in early May 2021. In England, Delta lineages were introduced >1,000 times and spread nationally as non-pharmaceutical interventions were relaxed. We find that hotel quarantine for travellers from India reduced onward transmission from importations; however the transmission chains that later dominated the Delta wave in England had been already seeded before restrictions were introduced. In England, increasing inter-regional travel drove Delta's nationwide dissemination, with some cities receiving >2,000 observable lineage introductions from other regions. Subsequently, increased levels of local population mixing, not the number of importations, was associated with faster relative growth of Delta. Among US states, we find that regions that previously experienced large waves also had faster Delta growth rates, and a model including interactions between immunity and human behaviour could accurately predict the rise of Delta there. Delta's invasion dynamics depended on fine scale spatial heterogeneity in immunity and contact patterns and our findings will inform optimal spatial interventions to reduce transmission of current and future VOCs such as Omicron.
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Affiliation(s)
- John T McCrone
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
- contributed equally as first authors
| | - Verity Hill
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
- contributed equally as first authors
| | - Sumali Bajaj
- Department of Zoology, University of Oxford, Oxford, UK
- contributed equally as first authors
| | - Rosario Evans Pena
- Department of Zoology, University of Oxford, Oxford, UK
- contributed equally as first authors
| | - Ben C Lambert
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Rhys Inward
- Department of Zoology, University of Oxford, Oxford, UK
- MRC Centre of Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Samir Bhatt
- MRC Centre of Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Erik Volz
- MRC Centre of Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Christopher Ruis
- Molecular Immunity Unit, Department of Medicine, Cambridge University, Cambridge, UK
| | - Simon Dellicour
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Bruxelles, Belgium
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | | | | | - Neo Wu
- Google, Mountain View, CA, USA
| | | | - Xiang Ji
- Department of Mathematics, School of Science & Engineering, Tulane University, New Orleans, LA, USA
| | | | - Ben Jackson
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Rachel Colquhoun
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Áine O'Toole
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Thomas P Peacock
- Department of Infectious Disease, Imperial College London, London, UK
- UK Health Security Agency, London, UK
| | | | | | | | | | - Nuno R Faria
- Department of Zoology, University of Oxford, Oxford, UK
- MRC Centre of Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
- Instituto de Medicina Tropical, Faculdade de Medicina da Universidade de Sao Paulo, Sao Paulo, Brazil
| | | | - Isaac I Bogoch
- Divisions of Internal Medicine & Infectious Diseases, Toronto General Hospital, University Health Network, Toronto, Canada
- Department of Medicine, Division of Infectious Diseases, University of Toronto, ON, Canada
| | - Kamran Khan
- BlueDot, Toronto, Canada
- Department of Medicine, Division of Infectious Diseases, University of Toronto, ON, Canada
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada
| | | | | | - David M Aanensen
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Wendy S Barclay
- Department of Infectious Disease, Imperial College London, London, UK
| | | | - Thomas Connor
- Pathogen Genomics Unit, Public Health Wales NHS Trust, Cardiff, UK
- School of Biosciences, The Sir Martin Evans Building, Cardiff University, Cardiff, UK
- Quadram Institute, Norwich, UK
| | - Nicholas J Loman
- Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK
| | - Marc A Suchard
- Departments of Biostatistics, Biomathematics and Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Oliver G Pybus
- Department of Zoology, University of Oxford, Oxford, UK
- Department of Pathobiology and Population Sciences, Royal Veterinary College London, London, UK
- jointly supervised this work
| | - Andrew Rambaut
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
- jointly supervised this work
| | - Moritz U G Kraemer
- Department of Zoology, University of Oxford, Oxford, UK
- jointly supervised this work
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15
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McCrone JT, Hill V, Bajaj S, Pena RE, Lambert BC, Inward R, Bhatt S, Volz E, Ruis C, Dellicour S, Baele G, Zarebski AE, Sadilek A, Wu N, Schneider A, Ji X, Raghwani J, Jackson B, Colquhoun R, O'Toole Á, Peacock TP, Twohig K, Thelwall S, Dabrera G, Myers R, Faria NR, Huber C, Bogoch II, Khan K, du Plessis L, Barrett JC, Aanensen DM, Barclay WS, Chand M, Connor T, Loman NJ, Suchard MA, Pybus OG, Rambaut A, Kraemer MUG. Context-specific emergence and growth of the SARS-CoV-2 Delta variant. Res Sq 2021:rs.3.rs-1159614. [PMID: 34981043 PMCID: PMC8722606 DOI: 10.21203/rs.3.rs-1159614/v1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
The Delta variant of concern of SARS-CoV-2 has spread globally causing large outbreaks and resurgences of COVID-19 cases. The emergence of Delta in the UK occurred on the background of a heterogeneous landscape of immunity and relaxation of non-pharmaceutical interventions. Here we analyse 52,992 Delta genomes from England in combination with 93,649 global genomes to reconstruct the emergence of Delta, and quantify its introduction to and regional dissemination across England, in the context of changing travel and social restrictions. Through analysis of human movement, contact tracing, and virus genomic data, we find that the focus of geographic expansion of Delta shifted from India to a more global pattern in early May 2021. In England, Delta lineages were introduced >1,000 times and spread nationally as non-pharmaceutical interventions were relaxed. We find that hotel quarantine for travellers from India reduced onward transmission from importations; however the transmission chains that later dominated the Delta wave in England had been already seeded before restrictions were introduced. In England, increasing inter-regional travel drove Delta's nationwide dissemination, with some cities receiving >2,000 observable lineage introductions from other regions. Subsequently, increased levels of local population mixing, not the number of importations, was associated with faster relative growth of Delta. Among US states, we find that regions that previously experienced large waves also had faster Delta growth rates, and a model including interactions between immunity and human behaviour could accurately predict the rise of Delta there. Delta’s invasion dynamics depended on fine scale spatial heterogeneity in immunity and contact patterns and our findings will inform optimal spatial interventions to reduce transmission of current and future VOCs such as Omicron.
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Affiliation(s)
- John T McCrone
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Verity Hill
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Sumali Bajaj
- Department of Zoology, University of Oxford, Oxford, UK
| | | | - Ben C Lambert
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Rhys Inward
- Department of Zoology, University of Oxford, Oxford, UK
- MRC Centre of Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Samir Bhatt
- MRC Centre of Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Erik Volz
- MRC Centre of Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Christopher Ruis
- Molecular Immunity Unit, Department of Medicine, Cambridge University, Cambridge, UK
| | - Simon Dellicour
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Bruxelles, Belgium
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | | | | | - Neo Wu
- Google, Mountain View, CA, USA
| | | | - Xiang Ji
- Department of Mathematics, School of Science & Engineering, Tulane University, New Orleans, LA, USA
| | | | - Ben Jackson
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Rachel Colquhoun
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Áine O'Toole
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Thomas P Peacock
- Department of Infectious Disease, Imperial College London, London, UK
- UK Health Security Agency, London, UK
| | | | | | | | | | - Nuno R Faria
- Department of Zoology, University of Oxford, Oxford, UK
- MRC Centre of Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
- Instituto de Medicina Tropical, Faculdade de Medicina da Universidade de Sao Paulo, Sao Paulo, Brazil
| | | | - Isaac I Bogoch
- Divisions of Internal Medicine & Infectious Diseases, Toronto General Hospital, University Health Network, Toronto, Canada
- Department of Medicine, Division of Infectious Diseases, University of Toronto, ON, Canada
| | - Kamran Khan
- BlueDot, Toronto, Canada
- Department of Medicine, Division of Infectious Diseases, University of Toronto, ON, Canada
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada
| | | | | | - David M Aanensen
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Wendy S Barclay
- Department of Infectious Disease, Imperial College London, London, UK
| | | | - Thomas Connor
- Pathogen Genomics Unit, Public Health Wales NHS Trust, Cardiff, UK
- School of Biosciences, The Sir Martin Evans Building, Cardiff University, Cardiff, UK
- Quadram Institute, Norwich, UK
| | - Nicholas J Loman
- Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK
| | - Marc A Suchard
- Departments of Biostatistics, Biomathematics and Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Oliver G Pybus
- Department of Zoology, University of Oxford, Oxford, UK
- Department of Pathobiology and Population Sciences, Royal Veterinary College London, London, UK
| | - Andrew Rambaut
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
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16
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Helekal D, Ledda A, Volz E, Wyllie D, Didelot X. Bayesian inference of clonal expansions in a dated phylogeny. Syst Biol 2021; 71:1073-1087. [PMID: 34893904 PMCID: PMC9366454 DOI: 10.1093/sysbio/syab095] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 11/23/2021] [Accepted: 11/29/2021] [Indexed: 11/16/2022] Open
Abstract
Microbial population genetics models often assume that all lineages are constrained by the same population size dynamics over time. However, many neutral and selective events can invalidate this assumption and can contribute to the clonal expansion of a specific lineage relative to the rest of the population. Such differential phylodynamic properties between lineages result in asymmetries and imbalances in phylogenetic trees that are sometimes described informally but which are difficult to analyze formally. To this end, we developed a model of how clonal expansions occur and affect the branching patterns of a phylogeny. We show how the parameters of this model can be inferred from a given dated phylogeny using Bayesian statistics, which allows us to assess the probability that one or more clonal expansion events occurred. For each putative clonal expansion event, we estimate its date of emergence and subsequent phylodynamic trajectory, including its long-term evolutionary potential which is important to determine how much effort should be placed on specific control measures. We demonstrate the applicability of our methodology on simulated and real data sets. Inference under our clonal expansion model can reveal important features in the evolution and epidemiology of infectious disease pathogens. [Clonal expansion; genomic epidemiology; microbial population genomics; phylodynamics.]
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Affiliation(s)
- David Helekal
- Centre for Doctoral Training in Mathematics for Real-World Systems, University of Warwick, United Kingdom
| | - Alice Ledda
- Healthcare Associated Infections and Antimicrobial Resistance Division, National Infection Service, Public Health England, United Kingdom
| | - Erik Volz
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, United Kingdom
| | - David Wyllie
- Field Service, East of England, National Infection Service, Public Health England, Cambridge, United Kingdom
| | - Xavier Didelot
- School of Life Sciences and Department of Statistics, University of Warwick, United Kingdom
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17
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Vöhringer HS, Sanderson T, Sinnott M, De Maio N, Nguyen T, Goater R, Schwach F, Harrison I, Hellewell J, Ariani CV, Gonçalves S, Jackson DK, Johnston I, Jung AW, Saint C, Sillitoe J, Suciu M, Goldman N, Panovska-Griffiths J, Birney E, Volz E, Funk S, Kwiatkowski D, Chand M, Martincorena I, Barrett JC, Gerstung M. Genomic reconstruction of the SARS-CoV-2 epidemic in England. Nature 2021; 600:506-511. [PMID: 34649268 PMCID: PMC8674138 DOI: 10.1038/s41586-021-04069-y] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Accepted: 09/29/2021] [Indexed: 11/09/2022]
Abstract
The evolution of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus leads to new variants that warrant timely epidemiological characterization. Here we use the dense genomic surveillance data generated by the COVID-19 Genomics UK Consortium to reconstruct the dynamics of 71 different lineages in each of 315 English local authorities between September 2020 and June 2021. This analysis reveals a series of subepidemics that peaked in early autumn 2020, followed by a jump in transmissibility of the B.1.1.7/Alpha lineage. The Alpha variant grew when other lineages declined during the second national lockdown and regionally tiered restrictions between November and December 2020. A third more stringent national lockdown suppressed the Alpha variant and eliminated nearly all other lineages in early 2021. Yet a series of variants (most of which contained the spike E484K mutation) defied these trends and persisted at moderately increasing proportions. However, by accounting for sustained introductions, we found that the transmissibility of these variants is unlikely to have exceeded the transmissibility of the Alpha variant. Finally, B.1.617.2/Delta was repeatedly introduced in England and grew rapidly in early summer 2021, constituting approximately 98% of sampled SARS-CoV-2 genomes on 26 June 2021.
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Affiliation(s)
- Harald S Vöhringer
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Hinxton, UK
| | - Theo Sanderson
- Wellcome Sanger Institute, Hinxton, UK
- The Francis Crick Institute, London, UK
| | | | - Nicola De Maio
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Hinxton, UK
| | | | | | - Frank Schwach
- Wellcome Sanger Institute, Hinxton, UK
- Public Health England, London, UK
| | | | - Joel Hellewell
- London School of Hygiene & Tropical Medicine, London, UK
| | | | | | | | | | - Alexander W Jung
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Hinxton, UK
| | | | | | | | - Nick Goldman
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Hinxton, UK
| | | | - Ewan Birney
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Hinxton, UK
| | - Erik Volz
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Sebastian Funk
- London School of Hygiene & Tropical Medicine, London, UK
| | | | - Meera Chand
- Public Health England, London, UK
- Guy's and St Thomas' NHS Foundation Trust, London, UK
| | | | | | - Moritz Gerstung
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Hinxton, UK.
- Division for AI in Oncology, German Cancer Research Centre DKFZ, Heidelberg, Germany.
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18
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Mishra S, Mindermann S, Sharma M, Whittaker C, Mellan TA, Wilton T, Klapsa D, Mate R, Fritzsche M, Zambon M, Ahuja J, Howes A, Miscouridou X, Nason GP, Ratmann O, Semenova E, Leech G, Sandkühler JF, Rogers-Smith C, Vollmer M, Unwin HJT, Gal Y, Chand M, Gandy A, Martin J, Volz E, Ferguson NM, Bhatt S, Brauner JM, Flaxman S. Changing composition of SARS-CoV-2 lineages and rise of Delta variant in England. EClinicalMedicine 2021; 39:101064. [PMID: 34401689 PMCID: PMC8349999 DOI: 10.1016/j.eclinm.2021.101064] [Citation(s) in RCA: 81] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 07/13/2021] [Accepted: 07/15/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Since its emergence in Autumn 2020, the SARS-CoV-2 Variant of Concern (VOC) B.1.1.7 (WHO label Alpha) rapidly became the dominant lineage across much of Europe. Simultaneously, several other VOCs were identified globally. Unlike B.1.1.7, some of these VOCs possess mutations thought to confer partial immune escape. Understanding when and how these additional VOCs pose a threat in settings where B.1.1.7 is currently dominant is vital. METHODS We examine trends in the prevalence of non-B.1.1.7 lineages in London and other English regions using passive-case detection PCR data, cross-sectional community infection surveys, genomic surveillance, and wastewater monitoring. The study period spans from 31st January 2021 to 15th May 2021. FINDINGS Across data sources, the percentage of non-B.1.1.7 variants has been increasing since late March 2021. This increase was initially driven by a variety of lineages with immune escape. From mid-April, B.1.617.2 (WHO label Delta) spread rapidly, becoming the dominant variant in England by late May. INTERPRETATION The outcome of competition between variants depends on a wide range of factors such as intrinsic transmissibility, evasion of prior immunity, demographic specificities and interactions with non-pharmaceutical interventions. The presence and rise of non-B.1.1.7 variants in March likely was driven by importations and some community transmission. There was competition between non-B.1.17 variants which resulted in B.1.617.2 becoming dominant in April and May with considerable community transmission. Our results underscore that early detection of new variants requires a diverse array of data sources in community surveillance. Continued real-time information on the highly dynamic composition and trajectory of different SARS-CoV-2 lineages is essential to future control efforts. FUNDING National Institute for Health Research, Medicines and Healthcare products Regulatory Agency, DeepMind, EPSRC, EA Funds programme, Open Philanthropy, Academy of Medical Sciences Bill,Melinda Gates Foundation, Imperial College Healthcare NHS Trust, The Novo Nordisk Foundation, MRC Centre for Global Infectious Disease Analysis, Community Jameel, Cancer Research UK, Imperial College COVID-19 Research Fund, Medical Research Council, Wellcome Sanger Institute.
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Affiliation(s)
- Swapnil Mishra
- Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
| | - Sören Mindermann
- Oxford Applied and Theoretical Machine Learning (OATML) Group, Department of Computer Science, University of Oxford, UK
| | - Mrinank Sharma
- Department of Statistics, University of Oxford, UK
- Department of Engineering Science, University of Oxford, UK
- Future of Humanity Institute, University of Oxford, UK
| | - Charles Whittaker
- Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
| | - Thomas A Mellan
- Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
| | - Thomas Wilton
- National Institute for Biological Standards and Control (NIBSC), UK
| | - Dimitra Klapsa
- National Institute for Biological Standards and Control (NIBSC), UK
| | - Ryan Mate
- National Institute for Biological Standards and Control (NIBSC), UK
| | - Martin Fritzsche
- National Institute for Biological Standards and Control (NIBSC), UK
| | | | - Janvi Ahuja
- Future of Humanity Institute, University of Oxford, UK
- Medical Sciences Division, University of Oxford, UK
| | - Adam Howes
- Department of Mathematics, Imperial College London, UK
| | | | - Guy P Nason
- Department of Mathematics, Imperial College London, UK
| | | | | | - Gavin Leech
- Department of Computer Science, University of Bristol, UK
| | | | - Charlie Rogers-Smith
- OATML Group (work done while at OATML as an external collaborator), Department of Computer Science, University of Oxford, UK
| | - Michaela Vollmer
- Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
- Public Health England, London, UK
| | - H Juliette T Unwin
- Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
| | - Yarin Gal
- Oxford Applied and Theoretical Machine Learning (OATML) Group, Department of Computer Science, University of Oxford, UK
| | | | - Axel Gandy
- Department of Mathematics, Imperial College London, UK
| | - Javier Martin
- National Institute for Biological Standards and Control (NIBSC), UK
| | - Erik Volz
- Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
| | - Neil M Ferguson
- Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
| | - Samir Bhatt
- Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Denmark
| | - Jan M Brauner
- Oxford Applied and Theoretical Machine Learning (OATML) Group, Department of Computer Science, University of Oxford, UK
- Future of Humanity Institute, University of Oxford, UK
| | - Seth Flaxman
- Department of Mathematics, Imperial College London, UK
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19
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Mishra S, Mindermann S, Sharma M, Whittaker C, Mellan TA, Wilton T, Klapsa D, Mate R, Fritzsche M, Zambon M, Ahuja J, Howes A, Miscouridou X, Nason GP, Ratmann O, Semenova E, Leech G, Sandkühler JF, Rogers-Smith C, Vollmer M, Unwin HJT, Gal Y, Chand M, Gandy A, Martin J, Volz E, Ferguson NM, Bhatt S, Brauner JM, Flaxman S. Changing composition of SARS-CoV-2 lineages and rise of Delta variant in England. EClinicalMedicine 2021. [PMID: 34401689 DOI: 10.25561/88876] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/21/2023] Open
Abstract
BACKGROUND Since its emergence in Autumn 2020, the SARS-CoV-2 Variant of Concern (VOC) B.1.1.7 (WHO label Alpha) rapidly became the dominant lineage across much of Europe. Simultaneously, several other VOCs were identified globally. Unlike B.1.1.7, some of these VOCs possess mutations thought to confer partial immune escape. Understanding when and how these additional VOCs pose a threat in settings where B.1.1.7 is currently dominant is vital. METHODS We examine trends in the prevalence of non-B.1.1.7 lineages in London and other English regions using passive-case detection PCR data, cross-sectional community infection surveys, genomic surveillance, and wastewater monitoring. The study period spans from 31st January 2021 to 15th May 2021. FINDINGS Across data sources, the percentage of non-B.1.1.7 variants has been increasing since late March 2021. This increase was initially driven by a variety of lineages with immune escape. From mid-April, B.1.617.2 (WHO label Delta) spread rapidly, becoming the dominant variant in England by late May. INTERPRETATION The outcome of competition between variants depends on a wide range of factors such as intrinsic transmissibility, evasion of prior immunity, demographic specificities and interactions with non-pharmaceutical interventions. The presence and rise of non-B.1.1.7 variants in March likely was driven by importations and some community transmission. There was competition between non-B.1.17 variants which resulted in B.1.617.2 becoming dominant in April and May with considerable community transmission. Our results underscore that early detection of new variants requires a diverse array of data sources in community surveillance. Continued real-time information on the highly dynamic composition and trajectory of different SARS-CoV-2 lineages is essential to future control efforts. FUNDING National Institute for Health Research, Medicines and Healthcare products Regulatory Agency, DeepMind, EPSRC, EA Funds programme, Open Philanthropy, Academy of Medical Sciences Bill,Melinda Gates Foundation, Imperial College Healthcare NHS Trust, The Novo Nordisk Foundation, MRC Centre for Global Infectious Disease Analysis, Community Jameel, Cancer Research UK, Imperial College COVID-19 Research Fund, Medical Research Council, Wellcome Sanger Institute.
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Affiliation(s)
- Swapnil Mishra
- Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
| | - Sören Mindermann
- Oxford Applied and Theoretical Machine Learning (OATML) Group, Department of Computer Science, University of Oxford, UK
| | - Mrinank Sharma
- Department of Statistics, University of Oxford, UK
- Department of Engineering Science, University of Oxford, UK
- Future of Humanity Institute, University of Oxford, UK
| | - Charles Whittaker
- Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
| | - Thomas A Mellan
- Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
| | - Thomas Wilton
- National Institute for Biological Standards and Control (NIBSC), UK
| | - Dimitra Klapsa
- National Institute for Biological Standards and Control (NIBSC), UK
| | - Ryan Mate
- National Institute for Biological Standards and Control (NIBSC), UK
| | - Martin Fritzsche
- National Institute for Biological Standards and Control (NIBSC), UK
| | | | - Janvi Ahuja
- Future of Humanity Institute, University of Oxford, UK
- Medical Sciences Division, University of Oxford, UK
| | - Adam Howes
- Department of Mathematics, Imperial College London, UK
| | | | - Guy P Nason
- Department of Mathematics, Imperial College London, UK
| | | | | | - Gavin Leech
- Department of Computer Science, University of Bristol, UK
| | | | - Charlie Rogers-Smith
- OATML Group (work done while at OATML as an external collaborator), Department of Computer Science, University of Oxford, UK
| | - Michaela Vollmer
- Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
- Public Health England, London, UK
| | - H Juliette T Unwin
- Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
| | - Yarin Gal
- Oxford Applied and Theoretical Machine Learning (OATML) Group, Department of Computer Science, University of Oxford, UK
| | | | - Axel Gandy
- Department of Mathematics, Imperial College London, UK
| | - Javier Martin
- National Institute for Biological Standards and Control (NIBSC), UK
| | - Erik Volz
- Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
| | - Neil M Ferguson
- Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
| | - Samir Bhatt
- Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Denmark
| | - Jan M Brauner
- Oxford Applied and Theoretical Machine Learning (OATML) Group, Department of Computer Science, University of Oxford, UK
- Future of Humanity Institute, University of Oxford, UK
| | - Seth Flaxman
- Department of Mathematics, Imperial College London, UK
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20
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Kraemer MUG, Hill V, Ruis C, Dellicour S, Bajaj S, McCrone JT, Baele G, Parag KV, Battle AL, Gutierrez B, Jackson B, Colquhoun R, O'Toole Á, Klein B, Vespignani A, Volz E, Faria NR, Aanensen DM, Loman NJ, du Plessis L, Cauchemez S, Rambaut A, Scarpino SV, Pybus OG. Spatiotemporal invasion dynamics of SARS-CoV-2 lineage B.1.1.7 emergence. Science 2021; 373:889-895. [PMID: 34301854 PMCID: PMC9269003 DOI: 10.1126/science.abj0113] [Citation(s) in RCA: 93] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 07/12/2021] [Indexed: 12/24/2022]
Abstract
Understanding the causes and consequences of the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern is crucial to pandemic control yet difficult to achieve because they arise in the context of variable human behavior and immunity. We investigated the spatial invasion dynamics of lineage B.1.1.7 by jointly analyzing UK human mobility, virus genomes, and community-based polymerase chain reaction data. We identified a multistage spatial invasion process in which early B.1.1.7 growth rates were associated with mobility and asymmetric lineage export from a dominant source location, enhancing the effects of B.1.1.7's increased intrinsic transmissibility. We further explored how B.1.1.7 spread was shaped by nonpharmaceutical interventions and spatial variation in previous attack rates. Our findings show that careful accounting of the behavioral and epidemiological context within which variants of concern emerge is necessary to interpret correctly their observed relative growth rates.
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Affiliation(s)
- Moritz U G Kraemer
- Instituto de Medicina Tropical, Faculdade de Medicina da Universidade de Sao Paulo, Sao Paulo, Brazil.
- Department of Zoology, University of Oxford, Oxford, UK
| | - Verity Hill
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Christopher Ruis
- Department of Zoology, University of Oxford, Oxford, UK
- Molecular Immunity Unit, Department of Medicine, Cambridge University, Cambridge, UK
| | - Simon Dellicour
- Network Science Institute, Northeastern University, Boston, USA
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Bruxelles, Belgium
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, 3000 Leuven, Belgium
| | - Sumali Bajaj
- Instituto de Medicina Tropical, Faculdade de Medicina da Universidade de Sao Paulo, Sao Paulo, Brazil
- Department of Zoology, University of Oxford, Oxford, UK
| | - John T McCrone
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Guy Baele
- Centre for Genomic Pathogen Surveillance, Wellcome Genome Campus, Hinxton, UK
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, 3000 Leuven, Belgium
| | - Kris V Parag
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Anya Lindström Battle
- Instituto de Medicina Tropical, Faculdade de Medicina da Universidade de Sao Paulo, Sao Paulo, Brazil
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
- Department of Plant Sciences, University of Oxford, Oxford, UK
| | - Bernardo Gutierrez
- Instituto de Medicina Tropical, Faculdade de Medicina da Universidade de Sao Paulo, Sao Paulo, Brazil
- Department of Plant Sciences, University of Oxford, Oxford, UK
- Department of Zoology, University of Oxford, Oxford, UK
| | - Ben Jackson
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Rachel Colquhoun
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Áine O'Toole
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Brennan Klein
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, 3000 Leuven, Belgium
- Network Science Institute, Northeastern University, Boston, USA
| | - Alessandro Vespignani
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, 3000 Leuven, Belgium
- Network Science Institute, Northeastern University, Boston, USA
| | - Erik Volz
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Nuno R Faria
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
- Department of Zoology, University of Oxford, Oxford, UK
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
- Instituto de Medicina Tropical, Faculdade de Medicina da Universidade de Sao Paulo, Sao Paulo, Brazil
| | - David M Aanensen
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Bruxelles, Belgium
- Centre for Genomic Pathogen Surveillance, Wellcome Genome Campus, Hinxton, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Nicholas J Loman
- Molecular Immunity Unit, Department of Medicine, Cambridge University, Cambridge, UK
- Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK
| | - Louis du Plessis
- Instituto de Medicina Tropical, Faculdade de Medicina da Universidade de Sao Paulo, Sao Paulo, Brazil
- Department of Zoology, University of Oxford, Oxford, UK
| | - Simon Cauchemez
- Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France
| | - Andrew Rambaut
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France.
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Samuel V Scarpino
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, 3000 Leuven, Belgium.
- Network Science Institute, Northeastern University, Boston, USA
- Vermont Complex Systems Center, University of Vermont, Burlington, USA
- Santa Fe Institute, Santa Fe, USA
| | - Oliver G Pybus
- Instituto de Medicina Tropical, Faculdade de Medicina da Universidade de Sao Paulo, Sao Paulo, Brazil.
- Department of Zoology, University of Oxford, Oxford, UK
- Department of Pathobiology and Population Sciences, Royal Veterinary College London, London, UK
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21
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Dennis AM, Frost SD, Enders K, Cressman AE, Volz E, Adams N, Miller WC, Cohen MS, Mobley V, Samoff E, Eron JJ. HIV-1 Transmission linkages among persons with incident infection to inform public health surveillance. EClinicalMedicine 2021; 37:100968. [PMID: 34195581 PMCID: PMC8225702 DOI: 10.1016/j.eclinm.2021.100968] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 05/19/2021] [Accepted: 05/28/2021] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND We evaluated features of HIV transmission networks involving persons diagnosed during incident HIV infection (IHI) to assess network-based opportunities to curtail onward transmission. METHODS Transmission networks were constructed using partial pol sequences reported to North Carolina surveillance among persons with recent (2014-2018) and past (<2014) HIV diagnoses. IHI were defined as documented acute infections or seroconversion. Demographic and virologic features of HIV genetic clusters (<1.5% pairwise genetic distance) involving ≥ 1 IHI were assessed. Persons with viral genetic links and who had diagnoses >90 days prior to an IHI were further characterized. We assessed named partner outcomes among IHI index persons using contact tracing data. FINDINGS Of 4,405 HIV diagnoses 2014-2018 with sequences, there were 323 (7%) IHI index persons; most were male (88%), Black (65%), young (68% <30 years), and reported sex with men (MSM) risk (79%). Index persons were more likely to be cluster members compared to non-index persons diagnosed during the same period (72% vs. 49%). In total, 162 clusters were identified involving 233 IHI, 577 recent diagnoses, and 163 past diagnoses. Most IHI cases (53%) had viral linkages to ≥1 previously diagnosed person without evidence of HIV viral suppression in the year prior to the diagnosis of the IHI index. In contact tracing, only 53% IHI cases named an HIV-positive contact, resulting in 0.5 previously diagnosed persons detected per IHI investigated. When combined with viral analyses, the detection rate of viremic previously diagnosed persons increased to 1.3. INTERPRETATION Integrating public health with molecular epidemiology, revealed that more than half of IHI have viral links to persons with previously diagnosed unsuppressed HIV infection which was largely unrecognized by traditional contact tracing. Enhanced partner services to support engagement and retention in HIV care and improved case finding supported by rapid phylogenetic analysis are tools to substantially reduce onward HIV transmission.
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Affiliation(s)
- Ann M. Dennis
- Division of Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Simon D.W. Frost
- Microsoft Research, Redmond, WA, United States
- London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Kimberly Enders
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Andrew E. Cressman
- Division of Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Erik Volz
- Imperial College, London, United Kingdom
| | - Nicole Adams
- Division of Public Health, North Carolina Department of Health and Human Services, Raleigh, NC, United States
| | - William C. Miller
- Department of Epidemiology, The Ohio State University, Columbus, OH, United States
| | - Myron S. Cohen
- Division of Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Victoria Mobley
- Division of Public Health, North Carolina Department of Health and Human Services, Raleigh, NC, United States
| | - Erika Samoff
- Division of Public Health, North Carolina Department of Health and Human Services, Raleigh, NC, United States
| | - Joseph J. Eron
- Division of Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
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22
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Ragonnet-Cronin M, Golubchik T, Moyo S, Fraser C, Essex M, Novitsky V, Volz E. HIV genetic diversity informs stage of HIV-1 infection among patients receiving antiretroviral therapy in Botswana. J Infect Dis 2021; 225:1330-1338. [PMID: 34077517 PMCID: PMC9016439 DOI: 10.1093/infdis/jiab293] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 06/01/2021] [Indexed: 12/20/2022] Open
Abstract
Background Human immunodeficiency virus (HIV)-1 genetic diversity increases during infection and can help infer the time elapsed since infection. However, the effect of antiretroviral treatment (ART) on the inference remains unknown. Methods Participants with estimated duration of HIV-1 infection based on repeated testing were sourced from cohorts in Botswana (n = 1944). Full-length HIV genome sequencing was performed from proviral deoxyribonucleic acid. We optimized a machine learning model to classify infections as < or >1 year based on viral genetic diversity, demographic, and clinical data. Results The best predictive model included variables for genetic diversity of HIV-1 gag, pol, and env, viral load, age, sex, and ART status. Most participants were on ART. Balanced accuracy was 90.6% (95% confidence interval, 86.7%–94.1%). We tested the algorithm among newly diagnosed participants with or without documented negative HIV tests. Among those without records, those who self-reported a negative HIV test within <1 year were more frequently classified as recent than those who reported a test >1 year previously. There was no difference in classification between those self-reporting a negative HIV test <1 year, whether or not they had a record. Conclusions These results indicate that recency of HIV-1 infection can be inferred from viral sequence diversity even among patients on suppressive ART.
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Affiliation(s)
- Manon Ragonnet-Cronin
- MRC Centre for Global Infectious Diseases Analysis, Imperial College London, London W2 1PG, UK
| | - Tanya Golubchik
- Big Data Institute, University of Oxford, Oxford OX3 7LF, UK
| | | | | | - Max Essex
- Botswana Harvard AIDS Initiative, Gaborone, Botswana.,Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA FXB 402, USA
| | - Vlad Novitsky
- Botswana Harvard AIDS Initiative, Gaborone, Botswana.,Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA FXB 402, USA.,Brown University, Providence RI 02912, USA
| | - Erik Volz
- MRC Centre for Global Infectious Diseases Analysis, Imperial College London, London W2 1PG, UK
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23
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Nouvellet P, Bhatia S, Cori A, Ainslie KEC, Baguelin M, Bhatt S, Boonyasiri A, Brazeau NF, Cattarino L, Cooper LV, Coupland H, Cucunuba ZM, Cuomo-Dannenburg G, Dighe A, Djaafara BA, Dorigatti I, Eales OD, van Elsland SL, Nascimento FF, FitzJohn RG, Gaythorpe KAM, Geidelberg L, Green WD, Hamlet A, Hauck K, Hinsley W, Imai N, Jeffrey B, Knock E, Laydon DJ, Lees JA, Mangal T, Mellan TA, Nedjati-Gilani G, Parag KV, Pons-Salort M, Ragonnet-Cronin M, Riley S, Unwin HJT, Verity R, Vollmer MAC, Volz E, Walker PGT, Walters CE, Wang H, Watson OJ, Whittaker C, Whittles LK, Xi X, Ferguson NM, Donnelly CA. Reduction in mobility and COVID-19 transmission. Nat Commun 2021; 12:1090. [PMID: 33597546 PMCID: PMC7889876 DOI: 10.1038/s41467-021-21358-2] [Citation(s) in RCA: 252] [Impact Index Per Article: 84.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 01/13/2021] [Indexed: 01/02/2023] Open
Abstract
In response to the COVID-19 pandemic, countries have sought to control SARS-CoV-2 transmission by restricting population movement through social distancing interventions, thus reducing the number of contacts. Mobility data represent an important proxy measure of social distancing, and here, we characterise the relationship between transmission and mobility for 52 countries around the world. Transmission significantly decreased with the initial reduction in mobility in 73% of the countries analysed, but we found evidence of decoupling of transmission and mobility following the relaxation of strict control measures for 80% of countries. For the majority of countries, mobility explained a substantial proportion of the variation in transmissibility (median adjusted R-squared: 48%, interquartile range - IQR - across countries [27-77%]). Where a change in the relationship occurred, predictive ability decreased after the relaxation; from a median adjusted R-squared of 74% (IQR across countries [49-91%]) pre-relaxation, to a median adjusted R-squared of 30% (IQR across countries [12-48%]) post-relaxation. In countries with a clear relationship between mobility and transmission both before and after strict control measures were relaxed, mobility was associated with lower transmission rates after control measures were relaxed indicating that the beneficial effects of ongoing social distancing behaviours were substantial.
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Affiliation(s)
- Pierre Nouvellet
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK.
- School of Life Sciences, University of Sussex, Brighton, UK.
| | - Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Kylie E C Ainslie
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Marc Baguelin
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Samir Bhatt
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Adhiratha Boonyasiri
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Nicholas F Brazeau
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Lorenzo Cattarino
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Laura V Cooper
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Helen Coupland
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Zulma M Cucunuba
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Gina Cuomo-Dannenburg
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Amy Dighe
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Bimandra A Djaafara
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Ilaria Dorigatti
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Oliver D Eales
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Sabine L van Elsland
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Fabricia F Nascimento
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Richard G FitzJohn
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Katy A M Gaythorpe
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Lily Geidelberg
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - William D Green
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Arran Hamlet
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Katharina Hauck
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Wes Hinsley
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Natsuko Imai
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Benjamin Jeffrey
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Edward Knock
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Daniel J Laydon
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - John A Lees
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Tara Mangal
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Thomas A Mellan
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Gemma Nedjati-Gilani
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Kris V Parag
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Margarita Pons-Salort
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Manon Ragonnet-Cronin
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - H Juliette T Unwin
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Robert Verity
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Michaela A C Vollmer
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Erik Volz
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Patrick G T Walker
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Caroline E Walters
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Haowei Wang
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Oliver J Watson
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Charles Whittaker
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Lilith K Whittles
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Xiaoyue Xi
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Neil M Ferguson
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Christl A Donnelly
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK.
- Department of Statistics, University of Oxford, Oxford, UK.
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24
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Volz E, Hill V, McCrone JT, Price A, Jorgensen D, O'Toole Á, Southgate J, Johnson R, Jackson B, Nascimento FF, Rey SM, Nicholls SM, Colquhoun RM, da Silva Filipe A, Shepherd J, Pascall DJ, Shah R, Jesudason N, Li K, Jarrett R, Pacchiarini N, Bull M, Geidelberg L, Siveroni I, Goodfellow I, Loman NJ, Pybus OG, Robertson DL, Thomson EC, Rambaut A, Connor TR. Evaluating the Effects of SARS-CoV-2 Spike Mutation D614G on Transmissibility and Pathogenicity. Cell 2021; 184:64-75.e11. [PMID: 33275900 PMCID: PMC7674007 DOI: 10.1016/j.cell.2020.11.020] [Citation(s) in RCA: 655] [Impact Index Per Article: 218.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 10/14/2020] [Accepted: 11/11/2020] [Indexed: 12/20/2022]
Abstract
Global dispersal and increasing frequency of the SARS-CoV-2 spike protein variant D614G are suggestive of a selective advantage but may also be due to a random founder effect. We investigate the hypothesis for positive selection of spike D614G in the United Kingdom using more than 25,000 whole genome SARS-CoV-2 sequences. Despite the availability of a large dataset, well represented by both spike 614 variants, not all approaches showed a conclusive signal of positive selection. Population genetic analysis indicates that 614G increases in frequency relative to 614D in a manner consistent with a selective advantage. We do not find any indication that patients infected with the spike 614G variant have higher COVID-19 mortality or clinical severity, but 614G is associated with higher viral load and younger age of patients. Significant differences in growth and size of 614G phylogenetic clusters indicate a need for continued study of this variant.
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Affiliation(s)
- Erik Volz
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK.
| | - Verity Hill
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - John T McCrone
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Anna Price
- School of Biosciences, Cardiff University, Cardiff, UK
| | - David Jorgensen
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Áine O'Toole
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Joel Southgate
- School of Biosciences, Cardiff University, Cardiff, UK; Pathogen Genomics Unit, Public Health Wales NHS Trust, Cardiff, UK
| | - Robert Johnson
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Ben Jackson
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Fabricia F Nascimento
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Sara M Rey
- Pathogen Genomics Unit, Public Health Wales NHS Trust, Cardiff, UK
| | - Samuel M Nicholls
- Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK
| | - Rachel M Colquhoun
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | | | - James Shepherd
- MRC-University of Glasgow Centre for Virus Research, Glasgow, UK
| | - David J Pascall
- Institute of Biodiversity, Animal Health and Comparative Medicine, Boyd Orr Centre for Population and Ecosystem Health, University of Glasgow, Glasgow, UK
| | - Rajiv Shah
- MRC-University of Glasgow Centre for Virus Research, Glasgow, UK
| | | | - Kathy Li
- MRC-University of Glasgow Centre for Virus Research, Glasgow, UK
| | - Ruth Jarrett
- MRC-University of Glasgow Centre for Virus Research, Glasgow, UK
| | | | - Matthew Bull
- Pathogen Genomics Unit, Public Health Wales NHS Trust, Cardiff, UK
| | - Lily Geidelberg
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Igor Siveroni
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Ian Goodfellow
- Department of Pathology, University of Cambridge, Cambridge, UK
| | - Nicholas J Loman
- Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK
| | - Oliver G Pybus
- Department of Zoology, University of Oxford, Oxford, UK; Department of Pathobiology and Population Sciences, The Royal Veterinary College, London, UK
| | | | - Emma C Thomson
- MRC-University of Glasgow Centre for Virus Research, Glasgow, UK
| | - Andrew Rambaut
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK.
| | - Thomas R Connor
- School of Biosciences, Cardiff University, Cardiff, UK; Pathogen Genomics Unit, Public Health Wales NHS Trust, Cardiff, UK; Quadram Institute Bioscience, Norwich, UK.
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Fu H, Wang H, Xi X, Boonyasiri A, Wang Y, Hinsley W, Fraser KJ, McCabe R, Olivera Mesa D, Skarp J, Ledda A, Dewé T, Dighe A, Winskill P, van Elsland SL, Ainslie KEC, Baguelin M, Bhatt S, Boyd O, Brazeau NF, Cattarino L, Charles G, Coupland H, Cucunuba ZM, Cuomo-Dannenburg G, Donnelly CA, Dorigatti I, Eales OD, FitzJohn RG, Flaxman S, Gaythorpe KAM, Ghani AC, Green WD, Hamlet A, Hauck K, Haw DJ, Jeffrey B, Laydon DJ, Lees JA, Mellan T, Mishra S, Nedjati-Gilani G, Nouvellet P, Okell L, Parag KV, Ragonnet-Cronin M, Riley S, Schmit N, Thompson HA, Unwin HJT, Verity R, Vollmer MAC, Volz E, Walker PGT, Walters CE, Watson OJ, Whittaker C, Whittles LK, Imai N, Bhatia S, Ferguson NM. Database of epidemic trends and control measures during the first wave of COVID-19 in mainland China. Int J Infect Dis 2021; 102:463-471. [PMID: 33130212 PMCID: PMC7603985 DOI: 10.1016/j.ijid.2020.10.075] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 10/20/2020] [Accepted: 10/23/2020] [Indexed: 02/06/2023] Open
Abstract
OBJECTIVES In this data collation study, we aimed to provide a comprehensive database describing the epidemic trends and responses during the first wave of coronavirus disease 2019 (COVID-19) throughout the main provinces in China. METHODS From mid-January to March 2020, we extracted publicly available data regarding the spread and control of COVID-19 from 31 provincial health authorities and major media outlets in mainland China. Based on these data, we conducted descriptive analyses of the epidemic in the six most-affected provinces. RESULTS School closures, travel restrictions, community-level lockdown, and contact tracing were introduced concurrently around late January but subsequent epidemic trends differed among provinces. Compared with Hubei, the other five most-affected provinces reported a lower crude case fatality ratio and proportion of critical and severe hospitalised cases. From March 2020, as the local transmission of COVID-19 declined, switching the focus of measures to the testing and quarantine of inbound travellers may have helped to sustain the control of the epidemic. CONCLUSIONS Aggregated indicators of case notifications and severity distributions are essential for monitoring an epidemic. A publicly available database containing these indicators and information regarding control measures is a useful resource for further research and policy planning in response to the COVID-19 epidemic.
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Affiliation(s)
- Han Fu
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK.
| | - Haowei Wang
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Xiaoyue Xi
- Department of Mathematics, Imperial College London, London, UK
| | - Adhiratha Boonyasiri
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK
| | - Yuanrong Wang
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Wes Hinsley
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Keith J Fraser
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Ruth McCabe
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Daniela Olivera Mesa
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Janetta Skarp
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Alice Ledda
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Tamsin Dewé
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Amy Dighe
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Peter Winskill
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Sabine L van Elsland
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Kylie E C Ainslie
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Marc Baguelin
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Samir Bhatt
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Olivia Boyd
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Nicholas F Brazeau
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Lorenzo Cattarino
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Giovanni Charles
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Helen Coupland
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Zulma M Cucunuba
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Gina Cuomo-Dannenburg
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Christl A Donnelly
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK; Department of Statistics, University of Oxford, Oxford, UK
| | - Ilaria Dorigatti
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Oliver D Eales
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Richard G FitzJohn
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Seth Flaxman
- Department of Mathematics, Imperial College London, London, UK
| | - Katy A M Gaythorpe
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Azra C Ghani
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - William D Green
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Arran Hamlet
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Katharina Hauck
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - David J Haw
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Benjamin Jeffrey
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Daniel J Laydon
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - John A Lees
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Thomas Mellan
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Swapnil Mishra
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Gemma Nedjati-Gilani
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Pierre Nouvellet
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK; School of Life Sciences, University of Sussex, Brighton, UK
| | - Lucy Okell
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Kris V Parag
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Manon Ragonnet-Cronin
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Nora Schmit
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Hayley A Thompson
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - H Juliette T Unwin
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Robert Verity
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Michaela A C Vollmer
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Erik Volz
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Patrick G T Walker
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Caroline E Walters
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Oliver J Watson
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Charles Whittaker
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Lilith K Whittles
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Natsuko Imai
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Neil M Ferguson
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
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26
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Thompson HA, Imai N, Dighe A, Ainslie KEC, Baguelin M, Bhatia S, Bhatt S, Boonyasiri A, Boyd O, Brazeau NF, Cattarino L, Cooper LV, Coupland H, Cucunuba Z, Cuomo-Dannenburg G, Djaafara B, Dorigatti I, van Elsland S, FitzJohn R, Fu H, Gaythorpe KAM, Green W, Hallett T, Hamlet A, Haw D, Hayes S, Hinsley W, Jeffrey B, Knock E, Laydon DJ, Lees J, Mangal TD, Mellan T, Mishra S, Mousa A, Nedjati-Gilani G, Nouvellet P, Okell L, Parag KV, Ragonnet-Cronin M, Riley S, Unwin HJT, Verity R, Vollmer M, Volz E, Walker PGT, Walters C, Wang H, Wang Y, Watson OJ, Whittaker C, Whittles LK, Winskill P, Xi X, Donnelly CA, Ferguson NM. SARS-CoV-2 infection prevalence on repatriation flights from Wuhan City, China. J Travel Med 2020; 27:5896041. [PMID: 32830853 PMCID: PMC7499665 DOI: 10.1093/jtm/taaa135] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Accepted: 08/14/2020] [Indexed: 11/14/2022]
Affiliation(s)
- Hayley A Thompson
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Natsuko Imai
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Amy Dighe
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Kylie E C Ainslie
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Marc Baguelin
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Samir Bhatt
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Adhiratha Boonyasiri
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK
| | - Olivia Boyd
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Nicholas F Brazeau
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Lorenzo Cattarino
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Laura V Cooper
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Helen Coupland
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Zulma Cucunuba
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Gina Cuomo-Dannenburg
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Bimandra Djaafara
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Ilaria Dorigatti
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Sabine van Elsland
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Richard FitzJohn
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Han Fu
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Katy A M Gaythorpe
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Will Green
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Timothy Hallett
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Arran Hamlet
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - David Haw
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Sarah Hayes
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Wes Hinsley
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Benjamin Jeffrey
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Edward Knock
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Daniel J Laydon
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - John Lees
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Tara D Mangal
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Thomas Mellan
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Swapnil Mishra
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Andria Mousa
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Gemma Nedjati-Gilani
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Pierre Nouvellet
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.,School of Life Sciences, University of Sussex, Sussex, UK
| | - Lucy Okell
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Kris V Parag
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Manon Ragonnet-Cronin
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - H Juliette T Unwin
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Robert Verity
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Michaela Vollmer
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Erik Volz
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Patrick G T Walker
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Caroline Walters
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Haowei Wang
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Yuanrong Wang
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Oliver J Watson
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Charles Whittaker
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Lilith K Whittles
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Peter Winskill
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Xiaoyue Xi
- Department of Mathematics, Imperial College London, London, UK
| | - Christl A Donnelly
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.,Department of Statistics, University of Oxford, Oxford, UK
| | - Neil M Ferguson
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
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27
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Abstract
Since spilling over into humans, SARS-CoV-2 has rapidly spread across the globe, accumulating significant genetic diversity. The structure of this genetic diversity and whether it reveals epidemiological insights are fundamental questions for understanding the evolutionary trajectory of this virus. Here, we use a recently developed phylodynamic approach to uncover phylogenetic structures underlying the SARS-CoV-2 pandemic. We find support for three SARS-CoV-2 lineages co-circulating, each with significantly different demographic dynamics concordant with known epidemiological factors. For example, Lineage C emerged in Europe with a high growth rate in late February, just prior to the exponential increase in cases in several European countries. Non-synonymous mutations that characterize Lineage C occur in functionally important gene regions responsible for viral replication and cell entry. Even though Lineages A and B had distinct demographic patterns, they were much more difficult to distinguish. Continuous application of phylogenetic approaches to track the evolutionary epidemiology of SARS-CoV-2 lineages will be increasingly important to validate the efficacy of control efforts and monitor significant evolutionary events in the future.
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Affiliation(s)
| | - Raima Carol Appaw
- School of Natural Sciences, University of Tasmania, Hobart, 7001, Australia
| | - Scott Carver
- School of Natural Sciences, University of Tasmania, Hobart, 7001, Australia
| | - Xavier Didelot
- School of Life Sciences and Department of Statistics, University of Warwick, Coventry CV47AL, UK
| | - Erik Volz
- Department of Infectious Disease Epidemiology, MRC Centre for Global Infectious Disease Analysis, Imperial College London, London W2 1PG, UK
| | - Michael Charleston
- School of Natural Sciences, University of Tasmania, Hobart, 7001, Australia
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28
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Verity R, Okell LC, Dorigatti I, Winskill P, Whittaker C, Imai N, Cuomo-Dannenburg G, Thompson H, Walker PGT, Fu H, Dighe A, Griffin JT, Baguelin M, Bhatia S, Boonyasiri A, Cori A, Cucunubá Z, FitzJohn R, Gaythorpe K, Green W, Hamlet A, Hinsley W, Laydon D, Nedjati-Gilani G, Riley S, van Elsland S, Volz E, Wang H, Wang Y, Xi X, Donnelly CA, Ghani AC, Ferguson NM. Estimates of the severity of coronavirus disease 2019: a model-based analysis. Lancet Infect Dis 2020; 20:669-677. [PMID: 32240634 PMCID: PMC7158570 DOI: 10.1016/s1473-3099(20)30243-7] [Citation(s) in RCA: 2105] [Impact Index Per Article: 526.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 03/20/2020] [Accepted: 03/20/2020] [Indexed: 02/07/2023]
Abstract
BACKGROUND In the face of rapidly changing data, a range of case fatality ratio estimates for coronavirus disease 2019 (COVID-19) have been produced that differ substantially in magnitude. We aimed to provide robust estimates, accounting for censoring and ascertainment biases. METHODS We collected individual-case data for patients who died from COVID-19 in Hubei, mainland China (reported by national and provincial health commissions to Feb 8, 2020), and for cases outside of mainland China (from government or ministry of health websites and media reports for 37 countries, as well as Hong Kong and Macau, until Feb 25, 2020). These individual-case data were used to estimate the time between onset of symptoms and outcome (death or discharge from hospital). We next obtained age-stratified estimates of the case fatality ratio by relating the aggregate distribution of cases to the observed cumulative deaths in China, assuming a constant attack rate by age and adjusting for demography and age-based and location-based under-ascertainment. We also estimated the case fatality ratio from individual line-list data on 1334 cases identified outside of mainland China. Using data on the prevalence of PCR-confirmed cases in international residents repatriated from China, we obtained age-stratified estimates of the infection fatality ratio. Furthermore, data on age-stratified severity in a subset of 3665 cases from China were used to estimate the proportion of infected individuals who are likely to require hospitalisation. FINDINGS Using data on 24 deaths that occurred in mainland China and 165 recoveries outside of China, we estimated the mean duration from onset of symptoms to death to be 17·8 days (95% credible interval [CrI] 16·9-19·2) and to hospital discharge to be 24·7 days (22·9-28·1). In all laboratory confirmed and clinically diagnosed cases from mainland China (n=70 117), we estimated a crude case fatality ratio (adjusted for censoring) of 3·67% (95% CrI 3·56-3·80). However, after further adjusting for demography and under-ascertainment, we obtained a best estimate of the case fatality ratio in China of 1·38% (1·23-1·53), with substantially higher ratios in older age groups (0·32% [0·27-0·38] in those aged <60 years vs 6·4% [5·7-7·2] in those aged ≥60 years), up to 13·4% (11·2-15·9) in those aged 80 years or older. Estimates of case fatality ratio from international cases stratified by age were consistent with those from China (parametric estimate 1·4% [0·4-3·5] in those aged <60 years [n=360] and 4·5% [1·8-11·1] in those aged ≥60 years [n=151]). Our estimated overall infection fatality ratio for China was 0·66% (0·39-1·33), with an increasing profile with age. Similarly, estimates of the proportion of infected individuals likely to be hospitalised increased with age up to a maximum of 18·4% (11·0-37·6) in those aged 80 years or older. INTERPRETATION These early estimates give an indication of the fatality ratio across the spectrum of COVID-19 disease and show a strong age gradient in risk of death. FUNDING UK Medical Research Council.
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Affiliation(s)
- Robert Verity
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Lucy C Okell
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Ilaria Dorigatti
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Peter Winskill
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Charles Whittaker
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Natsuko Imai
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Gina Cuomo-Dannenburg
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Hayley Thompson
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Patrick G T Walker
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Han Fu
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Amy Dighe
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Jamie T Griffin
- School of Mathematical Sciences, Queen Mary University of London, London, UK
| | - Marc Baguelin
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Adhiratha Boonyasiri
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Zulma Cucunubá
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Rich FitzJohn
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Katy Gaythorpe
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Will Green
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Arran Hamlet
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Wes Hinsley
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Daniel Laydon
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Gemma Nedjati-Gilani
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Sabine van Elsland
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Erik Volz
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Haowei Wang
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Yuanrong Wang
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Xiaoyue Xi
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Christl A Donnelly
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK; Department of Statistics, University of Oxford, Oxford, UK
| | - Azra C Ghani
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
| | - Neil M Ferguson
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
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29
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Verity R, Okell LC, Dorigatti I, Winskill P, Whittaker C, Imai N, Cuomo-Dannenburg G, Thompson H, Walker PGT, Fu H, Dighe A, Griffin JT, Baguelin M, Bhatia S, Boonyasiri A, Cori A, Cucunubá Z, FitzJohn R, Gaythorpe K, Green W, Hamlet A, Hinsley W, Laydon D, Nedjati-Gilani G, Riley S, van Elsland S, Volz E, Wang H, Wang Y, Xi X, Donnelly CA, Ghani AC, Ferguson NM. Estimates of the severity of coronavirus disease 2019: a model-based analysis. Lancet Infect Dis 2020; 20:669-677. [PMID: 32240634 DOI: 10.1101/2020.03.09.20033357] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 03/20/2020] [Accepted: 03/20/2020] [Indexed: 05/25/2023]
Abstract
BACKGROUND In the face of rapidly changing data, a range of case fatality ratio estimates for coronavirus disease 2019 (COVID-19) have been produced that differ substantially in magnitude. We aimed to provide robust estimates, accounting for censoring and ascertainment biases. METHODS We collected individual-case data for patients who died from COVID-19 in Hubei, mainland China (reported by national and provincial health commissions to Feb 8, 2020), and for cases outside of mainland China (from government or ministry of health websites and media reports for 37 countries, as well as Hong Kong and Macau, until Feb 25, 2020). These individual-case data were used to estimate the time between onset of symptoms and outcome (death or discharge from hospital). We next obtained age-stratified estimates of the case fatality ratio by relating the aggregate distribution of cases to the observed cumulative deaths in China, assuming a constant attack rate by age and adjusting for demography and age-based and location-based under-ascertainment. We also estimated the case fatality ratio from individual line-list data on 1334 cases identified outside of mainland China. Using data on the prevalence of PCR-confirmed cases in international residents repatriated from China, we obtained age-stratified estimates of the infection fatality ratio. Furthermore, data on age-stratified severity in a subset of 3665 cases from China were used to estimate the proportion of infected individuals who are likely to require hospitalisation. FINDINGS Using data on 24 deaths that occurred in mainland China and 165 recoveries outside of China, we estimated the mean duration from onset of symptoms to death to be 17·8 days (95% credible interval [CrI] 16·9-19·2) and to hospital discharge to be 24·7 days (22·9-28·1). In all laboratory confirmed and clinically diagnosed cases from mainland China (n=70 117), we estimated a crude case fatality ratio (adjusted for censoring) of 3·67% (95% CrI 3·56-3·80). However, after further adjusting for demography and under-ascertainment, we obtained a best estimate of the case fatality ratio in China of 1·38% (1·23-1·53), with substantially higher ratios in older age groups (0·32% [0·27-0·38] in those aged <60 years vs 6·4% [5·7-7·2] in those aged ≥60 years), up to 13·4% (11·2-15·9) in those aged 80 years or older. Estimates of case fatality ratio from international cases stratified by age were consistent with those from China (parametric estimate 1·4% [0·4-3·5] in those aged <60 years [n=360] and 4·5% [1·8-11·1] in those aged ≥60 years [n=151]). Our estimated overall infection fatality ratio for China was 0·66% (0·39-1·33), with an increasing profile with age. Similarly, estimates of the proportion of infected individuals likely to be hospitalised increased with age up to a maximum of 18·4% (11·0-37·6) in those aged 80 years or older. INTERPRETATION These early estimates give an indication of the fatality ratio across the spectrum of COVID-19 disease and show a strong age gradient in risk of death. FUNDING UK Medical Research Council.
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Affiliation(s)
- Robert Verity
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Lucy C Okell
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Ilaria Dorigatti
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Peter Winskill
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Charles Whittaker
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Natsuko Imai
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Gina Cuomo-Dannenburg
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Hayley Thompson
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Patrick G T Walker
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Han Fu
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Amy Dighe
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Jamie T Griffin
- School of Mathematical Sciences, Queen Mary University of London, London, UK
| | - Marc Baguelin
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Adhiratha Boonyasiri
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Zulma Cucunubá
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Rich FitzJohn
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Katy Gaythorpe
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Will Green
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Arran Hamlet
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Wes Hinsley
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Daniel Laydon
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Gemma Nedjati-Gilani
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Sabine van Elsland
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Erik Volz
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Haowei Wang
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Yuanrong Wang
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Xiaoyue Xi
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Christl A Donnelly
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK; Department of Statistics, University of Oxford, Oxford, UK
| | - Azra C Ghani
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
| | - Neil M Ferguson
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
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30
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Verity R, Okell LC, Dorigatti I, Winskill P, Whittaker C, Imai N, Cuomo-Dannenburg G, Thompson H, Walker PGT, Fu H, Dighe A, Griffin JT, Baguelin M, Bhatia S, Boonyasiri A, Cori A, Cucunubá Z, FitzJohn R, Gaythorpe K, Green W, Hamlet A, Hinsley W, Laydon D, Nedjati-Gilani G, Riley S, van Elsland S, Volz E, Wang H, Wang Y, Xi X, Donnelly CA, Ghani AC, Ferguson NM. Estimates of the severity of coronavirus disease 2019: a model-based analysis. Lancet Infect Dis 2020; 20:669-677. [PMID: 32240634 DOI: 10.1101/2020.03.09.20033357v1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 03/20/2020] [Accepted: 03/20/2020] [Indexed: 05/23/2023]
Abstract
BACKGROUND In the face of rapidly changing data, a range of case fatality ratio estimates for coronavirus disease 2019 (COVID-19) have been produced that differ substantially in magnitude. We aimed to provide robust estimates, accounting for censoring and ascertainment biases. METHODS We collected individual-case data for patients who died from COVID-19 in Hubei, mainland China (reported by national and provincial health commissions to Feb 8, 2020), and for cases outside of mainland China (from government or ministry of health websites and media reports for 37 countries, as well as Hong Kong and Macau, until Feb 25, 2020). These individual-case data were used to estimate the time between onset of symptoms and outcome (death or discharge from hospital). We next obtained age-stratified estimates of the case fatality ratio by relating the aggregate distribution of cases to the observed cumulative deaths in China, assuming a constant attack rate by age and adjusting for demography and age-based and location-based under-ascertainment. We also estimated the case fatality ratio from individual line-list data on 1334 cases identified outside of mainland China. Using data on the prevalence of PCR-confirmed cases in international residents repatriated from China, we obtained age-stratified estimates of the infection fatality ratio. Furthermore, data on age-stratified severity in a subset of 3665 cases from China were used to estimate the proportion of infected individuals who are likely to require hospitalisation. FINDINGS Using data on 24 deaths that occurred in mainland China and 165 recoveries outside of China, we estimated the mean duration from onset of symptoms to death to be 17·8 days (95% credible interval [CrI] 16·9-19·2) and to hospital discharge to be 24·7 days (22·9-28·1). In all laboratory confirmed and clinically diagnosed cases from mainland China (n=70 117), we estimated a crude case fatality ratio (adjusted for censoring) of 3·67% (95% CrI 3·56-3·80). However, after further adjusting for demography and under-ascertainment, we obtained a best estimate of the case fatality ratio in China of 1·38% (1·23-1·53), with substantially higher ratios in older age groups (0·32% [0·27-0·38] in those aged <60 years vs 6·4% [5·7-7·2] in those aged ≥60 years), up to 13·4% (11·2-15·9) in those aged 80 years or older. Estimates of case fatality ratio from international cases stratified by age were consistent with those from China (parametric estimate 1·4% [0·4-3·5] in those aged <60 years [n=360] and 4·5% [1·8-11·1] in those aged ≥60 years [n=151]). Our estimated overall infection fatality ratio for China was 0·66% (0·39-1·33), with an increasing profile with age. Similarly, estimates of the proportion of infected individuals likely to be hospitalised increased with age up to a maximum of 18·4% (11·0-37·6) in those aged 80 years or older. INTERPRETATION These early estimates give an indication of the fatality ratio across the spectrum of COVID-19 disease and show a strong age gradient in risk of death. FUNDING UK Medical Research Council.
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Affiliation(s)
- Robert Verity
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Lucy C Okell
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Ilaria Dorigatti
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Peter Winskill
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Charles Whittaker
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Natsuko Imai
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Gina Cuomo-Dannenburg
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Hayley Thompson
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Patrick G T Walker
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Han Fu
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Amy Dighe
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Jamie T Griffin
- School of Mathematical Sciences, Queen Mary University of London, London, UK
| | - Marc Baguelin
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Adhiratha Boonyasiri
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Zulma Cucunubá
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Rich FitzJohn
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Katy Gaythorpe
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Will Green
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Arran Hamlet
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Wes Hinsley
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Daniel Laydon
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Gemma Nedjati-Gilani
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Sabine van Elsland
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Erik Volz
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Haowei Wang
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Yuanrong Wang
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Xiaoyue Xi
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Christl A Donnelly
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK; Department of Statistics, University of Oxford, Oxford, UK
| | - Azra C Ghani
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
| | - Neil M Ferguson
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
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31
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Bhatia S, Imai N, Cuomo-Dannenburg G, Baguelin M, Boonyasiri A, Cori A, Cucunubá Z, Dorigatti I, FitzJohn R, Fu H, Gaythorpe K, Ghani A, Hamlet A, Hinsley W, Laydon D, Nedjati-Gilani G, Okell L, Riley S, Thompson H, van Elsland S, Volz E, Wang H, Wang Y, Whittaker C, Xi X, Donnelly CA, Ferguson NM. Estimating the number of undetected COVID-19 cases among travellers from mainland China. Wellcome Open Res 2020; 5:143. [PMID: 34632083 PMCID: PMC8477353.3 DOI: 10.12688/wellcomeopenres.15805.3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/19/2021] [Indexed: 11/23/2022] Open
Abstract
Background: As of August 2021, every region of the world has been affected by the COVID-19 pandemic, with more than 196,000,000 cases worldwide. Methods: We analysed COVID-19 cases among travellers from mainland China to different regions and countries, comparing the region- and country-specific rates of detected and confirmed cases per flight volume to estimate the relative sensitivity of surveillance in different regions and countries. Results: Although travel restrictions from Wuhan City and other cities across China may have reduced the absolute number of travellers to and from China, we estimated that up to 70% (95% CI: 54% - 80%) of imported cases could remain undetected relative to the sensitivity of surveillance in Singapore. The percentage of undetected imported cases rises to 75% (95% CI 66% - 82%) when comparing to the surveillance sensitivity in multiple countries. Conclusions: Our analysis shows that a large number of COVID-19 cases remain undetected across the world. These undetected cases potentially resulted in multiple chains of human-to-human transmission outside mainland China.
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Affiliation(s)
- Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Natsuko Imai
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Gina Cuomo-Dannenburg
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Marc Baguelin
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Adhiratha Boonyasiri
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Zulma Cucunubá
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Ilaria Dorigatti
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Rich FitzJohn
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Han Fu
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Katy Gaythorpe
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Azra Ghani
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Arran Hamlet
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Wes Hinsley
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Daniel Laydon
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Gemma Nedjati-Gilani
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Lucy Okell
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Hayley Thompson
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Sabine van Elsland
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Erik Volz
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Haowei Wang
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Yuanrong Wang
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Charles Whittaker
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Xiaoyue Xi
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Christl A. Donnelly
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | - Neil M. Ferguson
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
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Bhatia S, Imai N, Cuomo-Dannenburg G, Baguelin M, Boonyasiri A, Cori A, Cucunubá Z, Dorigatti I, FitzJohn R, Fu H, Gaythorpe K, Ghani A, Hamlet A, Hinsley W, Laydon D, Nedjati-Gilani G, Okell L, Riley S, Thompson H, van Elsland S, Volz E, Wang H, Wang Y, Whittaker C, Xi X, Donnelly CA, Ferguson NM. Estimating the number of undetected COVID-19 cases among travellers from mainland China. Wellcome Open Res 2020; 5:143. [PMID: 34632083 PMCID: PMC8477353.2 DOI: 10.12688/wellcomeopenres.15805.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/12/2021] [Indexed: 11/20/2022] Open
Abstract
Background: As of August 2021, every region of the world has been affected by the COVID-19 pandemic, with more than 196,000,000 cases worldwide. Methods: We analysed COVID-19 cases among travellers from mainland China to different regions and countries, comparing the region- and country-specific rates of detected and confirmed cases per flight volume to estimate the relative sensitivity of surveillance in different regions and countries. Results: Although travel restrictions from Wuhan City and other cities across China may have reduced the absolute number of travellers to and from China, we estimated that up to 70% (95% CI: 54% - 80%) of imported cases could remain undetected relative to the sensitivity of surveillance in Singapore. The percentage of undetected imported cases rises to 75% (95% CI 66% - 82%) when comparing to the surveillance sensitivity in multiple countries. Conclusions: Our analysis shows that a large number of COVID-19 cases remain undetected across the world. These undetected cases potentially resulted in multiple chains of human-to-human transmission outside mainland China.
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Affiliation(s)
- Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Natsuko Imai
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Gina Cuomo-Dannenburg
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Marc Baguelin
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Adhiratha Boonyasiri
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Zulma Cucunubá
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Ilaria Dorigatti
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Rich FitzJohn
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Han Fu
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Katy Gaythorpe
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Azra Ghani
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Arran Hamlet
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Wes Hinsley
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Daniel Laydon
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Gemma Nedjati-Gilani
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Lucy Okell
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Hayley Thompson
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Sabine van Elsland
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Erik Volz
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Haowei Wang
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Yuanrong Wang
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Charles Whittaker
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Xiaoyue Xi
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Christl A. Donnelly
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | - Neil M. Ferguson
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
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Bhatia S, Imai N, Cuomo-Dannenburg G, Baguelin M, Boonyasiri A, Cori A, Cucunubá Z, Dorigatti I, FitzJohn R, Fu H, Gaythorpe K, Ghani A, Hamlet A, Hinsley W, Laydon D, Nedjati-Gilani G, Okell L, Riley S, Thompson H, van Elsland S, Volz E, Wang H, Wang Y, Whittaker C, Xi X, Donnelly CA, Ferguson NM. Estimating the number of undetected COVID-19 cases among travellers from mainland China. Wellcome Open Res 2020; 5:143. [PMID: 34632083 PMCID: PMC8477353 DOI: 10.12688/wellcomeopenres.15805.1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/04/2020] [Indexed: 11/20/2022] Open
Abstract
Background: Since the start of the COVID-19 epidemic in late 2019, there have been more than 152 affected regions and countries with over 110,000 confirmed cases outside mainland China. Methods: We analysed COVID-19 cases among travellers from mainland China to different regions and countries, comparing the region- and country-specific rates of detected and confirmed cases per flight volume to estimate the relative sensitivity of surveillance in different regions and countries. Results: Although travel restrictions from Wuhan City and other cities across China may have reduced the absolute number of travellers to and from China, we estimated that more than two thirds (70%, 95% CI: 54% - 80%, compared to Singapore; 75%, 95% CI: 66% - 82%, compared to multiple countries) of cases exported from mainland China have remained undetected. Conclusions: These undetected cases potentially resulted in multiple chains of human-to-human transmission outside mainland China.
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Affiliation(s)
- Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Natsuko Imai
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Gina Cuomo-Dannenburg
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Marc Baguelin
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Adhiratha Boonyasiri
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Zulma Cucunubá
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Ilaria Dorigatti
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Rich FitzJohn
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Han Fu
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Katy Gaythorpe
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Azra Ghani
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Arran Hamlet
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Wes Hinsley
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Daniel Laydon
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Gemma Nedjati-Gilani
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Lucy Okell
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Hayley Thompson
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Sabine van Elsland
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Erik Volz
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Haowei Wang
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Yuanrong Wang
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Charles Whittaker
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Xiaoyue Xi
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Christl A. Donnelly
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | - Neil M. Ferguson
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
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Stirrup OT, Sabin CA, Phillips AN, Williams I, Churchill D, Tostevin A, Hill T, Dunn DT, Asboe D, Pozniak A, Cane P, Chadwick D, Churchill D, Clark D, Collins S, Delpech V, Douthwaite S, Dunn D, Fearnhill E, Porter K, Tostevin A, Stirrup O, Fraser C, Geretti AM, Gunson R, Hale A, Hué S, Lazarus L, Leigh-Brown A, Mbisa T, Mackie N, Orkin C, Nastouli E, Pillay D, Phillips A, Sabin C, Smit E, Templeton K, Tilston P, Volz E, Williams I, Zhang H, Fairbrother K, Dawkins J, O’Shea S, Mullen J, Cox A, Tandy R, Fawcett T, Hopkins M, Booth C, Renwick L, Renwick L, Schmid ML, Payne B, Hubb J, Dustan S, Kirk S, Bradley-Stewart A, Hill T, Jose S, Thornton A, Huntington S, Glabay A, Shidfar S, Lynch J, Hand J, de Souza C, Perry N, Tilbury S, Youssef E, Gazzard B, Nelson M, Mabika T, Mandalia S, Anderson J, Munshi S, Post F, Adefisan A, Taylor C, Gleisner Z, Ibrahim F, Campbell L, Baillie K, Gilson R, Brima N, Ainsworth J, Schwenk A, Miller S, Wood C, Johnson M, Youle M, Lampe F, Smith C, Tsintas R, Chaloner C, Hutchinson S, Walsh J, Mackie N, Winston A, Weber J, Ramzan F, Carder M, Leen C, Wilson A, Morris S, Gompels M, Allan S, Palfreeman A, Lewszuk A, Kegg S, Faleye A, Ogunbiyi V, Mitchell S, Hay P, Kemble C, Martin F, Russell-Sharpe S, Gravely J, Allan S, Harte A, Tariq A, Spencer H, Jones R, Pritchard J, Cumming S, Atkinson C, Mital D, Edgell V, Allen J, Ustianowski A, Murphy C, Gunder I, Trevelion R, Babiker A. Associations between baseline characteristics, CD4 cell count response and virological failure on first-line efavirenz + tenofovir + emtricitabine for HIV. J Virus Erad 2019. [DOI: 10.1016/s2055-6640(20)30037-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
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Dennis AM, Volz E, Frost AMSD, Hossain M, Poon AF, Rebeiro PF, Vermund SH, Sterling TR, Kalish ML. HIV-1 Transmission Clustering and Phylodynamics Highlight the Important Role of Young Men Who Have Sex with Men. AIDS Res Hum Retroviruses 2018; 34:879-888. [PMID: 30027754 PMCID: PMC6204570 DOI: 10.1089/aid.2018.0039] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
More persons living with HIV reside in the Southern United States than in any other region, yet little is known about HIV molecular epidemiology in the South. We used cluster and phylodynamic analyses to evaluate HIV transmission patterns in middle Tennessee. We performed cross-sectional analyses of HIV-1 pol sequences and clinical data collected from 2001 to 2015 among persons attending the Vanderbilt Comprehensive Care Clinic. Transmission clusters were identified using maximum likelihood phylogenetics and patristic distance differences. Demographic, risk behavior, and clinical factors were assessed evaluating “active” clusters (clusters including sequences sampled 2011–2015) and associations estimated with logistic regression. Transmission risk ratios for men who have sex with men (MSM) were estimated with phylodynamic models. Among 2915 persons (96% subtype-B sequences), 963 (33%) were members of 292 clusters (distance ≤1.5%, size range 2–39). Most clusters (62%, n = 690 persons) were active, either being newly identified (n = 80) or showing expansion on existing clusters (n = 101). Correlates of active clustering among persons with sequences collected during 2011–2015 included MSM risk and ≤30 years of age. Active clusters were significantly more concentrated in MSM and younger persons than historical clusters. Young MSM (YMSM) (≤26.4 years) had high estimated transmission risk [risk ratio = 4.04 (2.85–5.65) relative to older MSM] and were much more likely to transmit to YMSM. In this Tennessee cohort, transmission clusters over time were more concentrated by MSM and younger age, with high transmission risk among and between YMSM, highlighting the importance of interventions among this group. Detecting active clusters could help direct interventions to disrupt ongoing transmission chains.
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Affiliation(s)
- Ann M. Dennis
- Division of Infectious Diseases, University of North Carolina, Chapel Hill, North Carolina
| | - Erik Volz
- Department of Infectious Disease Epidemiology and Centre for Outbreak Analysis and Modeling, Imperial College, London, United Kingdom
| | | | - Mukarram Hossain
- Department of Veterinary Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Art F.Y. Poon
- Department of Pathology and Laboratory Medicine, Western University, London, Canada
| | - Peter F. Rebeiro
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Sten H. Vermund
- Department of Epidemiology of Microbial Diseases, Yale University School of Public Health, New Haven, Connecticut
| | - Timothy R. Sterling
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Marcia L. Kalish
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee
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Mukandavire C, Walker J, Schwartz S, Boily M, Danon L, Lyons C, Diouf D, Liestman B, Diouf NL, Drame F, Coly K, Muhire RSM, Thiam S, Diallo PAN, Kane CT, Ndour C, Volz E, Mishra S, Baral S, Vickerman P. Estimating the contribution of key populations towards the spread of HIV in Dakar, Senegal. J Int AIDS Soc 2018; 21 Suppl 5:e25126. [PMID: 30033604 PMCID: PMC6055131 DOI: 10.1002/jia2.25126] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Accepted: 05/16/2018] [Indexed: 11/11/2022] Open
Abstract
INTRODUCTION Key populations including female sex workers (FSW) and men who have sex with men (MSM) bear a disproportionate burden of HIV. However, the role of focusing prevention efforts on these groups for reducing a country's HIV epidemic is debated. We estimate the extent to which HIV transmission among FSW and MSM contributes to overall HIV transmission in Dakar, Senegal, using a dynamic assessment of the population attributable fraction (PAF). METHODS A dynamic transmission model of HIV among FSW, their clients, MSM and the lower-risk adult population was parameterized and calibrated within a Bayesian framework using setting-specific demographic, behavioural, HIV epidemiological and antiretroviral treatment (ART) coverage data for 1985 to 2015. We used the model to estimate the 10-year PAF of commercial sex between FSW and their clients, and sex between men, to overall HIV transmission (defined as the percentage of new infections prevented when these modes of transmission are removed). In addition, we estimated the prevention benefits associated with historical increases in condom use and ART uptake, and impact of further increases in prevention and treatment. RESULTS The model projections suggest that unprotected sex between men contributed to 42% (2.5 to 97.5th percentile range 24 to 59%) of transmissions between 1995 and 2005, increasing to 64% (37 to 79%) from 2015 to 2025. The 10-year PAF of commercial sex is smaller, diminishing from 21% (7 to 39%) in 1995 to 14% (5 to 35%) in 2015. Without ART, 49% (32 to 71%) more HIV infections would have occurred since 2000, when ART was initiated, whereas without condom use since 1985, 67% (27 to 179%) more HIV infections would have occurred, and the overall HIV prevalence would have been 60% (29 to 211%) greater than what it is now. Further large decreases in HIV incidence (68%) can be achieved by scaling up ART in MSM to 74% coverage and reducing their susceptibility to HIV by two-thirds through any prevention modality. CONCLUSIONS Unprotected sex between men may be an important contributor to HIV transmission in Dakar, due to suboptimal coverage of evidence-informed interventions. Although existing interventions have effectively reduced HIV transmission among adults, it is crucial that further strategies address the unmet need among MSM.
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Affiliation(s)
| | - Josephine Walker
- Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUK
| | - Sheree Schwartz
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMDUSA
| | | | - Leon Danon
- Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUK
- College of Engineering, Mathematics and Physical SciencesUniversity of ExeterExeterUK
| | - Carrie Lyons
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMDUSA
| | | | - Ben Liestman
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMDUSA
| | - Nafissatou Leye Diouf
- Institut de Recherche en Santéde Surveillance Epidemiologique et de FormationsDakarSenegal
| | | | - Karleen Coly
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMDUSA
| | | | - Safiatou Thiam
- Department of HealthNational AIDS Council of SenegalDakarSenegal
| | | | - Coumba Toure Kane
- Institut de Recherche en Santéde Surveillance Epidemiologique et de FormationsDakarSenegal
| | - Cheikh Ndour
- Division de La Lutte Contre Le Sida et Les ISTMinistry of HealthDakarSenegal
| | - Erik Volz
- Department of Infectious Disease EpidemiologyImperial CollegeLondonUK
| | | | - Stefan Baral
- Department of Infectious Disease EpidemiologyImperial CollegeLondonUK
| | - Peter Vickerman
- Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUK
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Geidelberg E, Volz E. A6 Using phylodynamic modelling to estimate the population attributable fraction of HIV spread due to key populations in Dakar, Senegal. Virus Evol 2018. [PMCID: PMC5905447 DOI: 10.1093/ve/vey010.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
| | - E Volz
- Imperial College London, London, UK
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38
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Sadasivam RS, Cutrona SL, Luger TM, Volz E, Kinney R, Rao SR, Allison JJ, Houston TK. Share2Quit: Online Social Network Peer Marketing of Tobacco Cessation Systems. Nicotine Tob Res 2017; 19:314-323. [PMID: 27613918 DOI: 10.1093/ntr/ntw187] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2016] [Accepted: 07/18/2016] [Indexed: 11/13/2022]
Abstract
Introduction Although technology-assisted tobacco interventions (TATIs) are effective, they are underused due to recruitment challenges. We tested whether we could successfully recruit smokers to a TATI using peer marketing through a social network (Facebook). Methods We recruited smokers on Facebook using online advertisements. These recruited smokers (seeds) and subsequent waves of smokers (peer recruits) were provided the Share2Quit peer recruitment Facebook app and other tools. Smokers were incentivized for up to seven successful peer recruitments and had 30 days to recruit from date of registration. Successful peer recruitment was defined as a peer recruited smoker completing the registration on the TATI following a referral. Our primary questions were (1) whether smokers would recruit other smokers and (2) whether peer recruitment would extend the reach of the intervention to harder-to-reach groups, including those not ready to quit and minority smokers. Results Overall, 759 smokers were recruited (seeds: 190; peer recruits: 569). Fifteen percent (n = 117) of smokers successfully recruited their peers (seeds: 24.7%; peer recruits: 7.7%) leading to four recruitment waves. Compared to seeds, peer recruits were less likely to be ready to quit (peer recruits 74.2% vs. seeds 95.1%), more likely to be male (67.1% vs. 32.9%), and more likely to be African American (23.8% vs. 10.8%) (p < .01 for all comparisons). Conclusions Peer marketing quadrupled our engaged smokers and enriched the sample with not-ready-to-quit and African American smokers. Peer recruitment is promising, and our study uncovered several important challenges for future research. Implications This study demonstrates the successful recruitment of smokers to a TATI using a Facebook-based peer marketing strategy. Smokers on Facebook were willing and able to recruit other smokers to a TATI, yielding a large and diverse population of smokers.
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Affiliation(s)
- Rajani S Sadasivam
- Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA
| | - Sarah L Cutrona
- Department of Medicine, University of Massachusetts Medical School, Worcester, MA.,Meyers Primary Care Institute, Worcester, MA
| | - Tana M Luger
- Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA.,Center for Healthcare Organization and Implementation Research, Department of Veterans Affairs, Bedford, MA
| | | | - Rebecca Kinney
- Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA
| | - Sowmya R Rao
- Department of Surgery, Boston University, Boston, MA
| | - Jeroan J Allison
- Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA
| | - Thomas K Houston
- Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA.,Center for Healthcare Organization and Implementation Research, Department of Veterans Affairs, Bedford, MA
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Ratmann O, Hodcroft EB, Pickles M, Cori A, Hall M, Lycett S, Colijn C, Dearlove B, Didelot X, Frost S, Hossain ASMM, Joy JB, Kendall M, Kühnert D, Leventhal GE, Liang R, Plazzotta G, Poon AFY, Rasmussen DA, Stadler T, Volz E, Weis C, Leigh Brown AJ, Fraser C. Phylogenetic Tools for Generalized HIV-1 Epidemics: Findings from the PANGEA-HIV Methods Comparison. Mol Biol Evol 2016; 34:185-203. [PMID: 28053012 PMCID: PMC5854118 DOI: 10.1093/molbev/msw217] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Viral phylogenetic methods contribute to understanding how HIV spreads in populations, and thereby help guide the design of prevention interventions. So far, most analyses have been applied to well-sampled concentrated HIV-1 epidemics in wealthy countries. To direct the use of phylogenetic tools to where the impact of HIV-1 is greatest, the Phylogenetics And Networks for Generalized HIV Epidemics in Africa (PANGEA-HIV) consortium generates full-genome viral sequences from across sub-Saharan Africa. Analyzing these data presents new challenges, since epidemics are principally driven by heterosexual transmission and a smaller fraction of cases is sampled. Here, we show that viral phylogenetic tools can be adapted and used to estimate epidemiological quantities of central importance to HIV-1 prevention in sub-Saharan Africa. We used a community-wide methods comparison exercise on simulated data, where participants were blinded to the true dynamics they were inferring. Two distinct simulations captured generalized HIV-1 epidemics, before and after a large community-level intervention that reduced infection levels. Five research groups participated. Structured coalescent modeling approaches were most successful: phylogenetic estimates of HIV-1 incidence, incidence reductions, and the proportion of transmissions from individuals in their first 3 months of infection correlated with the true values (Pearson correlation > 90%), with small bias. However, on some simulations, true values were markedly outside reported confidence or credibility intervals. The blinded comparison revealed current limits and strengths in using HIV phylogenetics in challenging settings, provided benchmarks for future methods' development, and supports using the latest generation of phylogenetic tools to advance HIV surveillance and prevention.
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Affiliation(s)
- Oliver Ratmann
- Department of Infectious Disease Epidemiology, MRC Centre for Outbreak Analyses and Modelling, School of Public Health, Imperial College London, London, United Kingdom
| | - Emma B Hodcroft
- School of Biological Sciences, Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, United Kingdom
| | - Michael Pickles
- Department of Infectious Disease Epidemiology, MRC Centre for Outbreak Analyses and Modelling, School of Public Health, Imperial College London, London, United Kingdom
| | - Anne Cori
- Department of Infectious Disease Epidemiology, MRC Centre for Outbreak Analyses and Modelling, School of Public Health, Imperial College London, London, United Kingdom
| | - Matthew Hall
- School of Biological Sciences, Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, United Kingdom.,Nuffield Department of Medicine, Li Ka Shing Centre for Health Information and Discovery, Oxford Big Data Institute, University of Oxford, Oxford, United Kingdom
| | - Samantha Lycett
- School of Biological Sciences, Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, United Kingdom.,The Roslin Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Caroline Colijn
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Bethany Dearlove
- Department of Veterinary Medicine, Cambridge Veterinary School, Cambridge, United Kingdom
| | - Xavier Didelot
- Department of Infectious Disease Epidemiology, MRC Centre for Outbreak Analyses and Modelling, School of Public Health, Imperial College London, London, United Kingdom
| | - Simon Frost
- Department of Veterinary Medicine, Cambridge Veterinary School, Cambridge, United Kingdom
| | | | - Jeffrey B Joy
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada.,British Columbia Centre for Excellence in HIV/AIDS, Vancouver, BC, Canada
| | - Michelle Kendall
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Denise Kühnert
- Department of Environmental Systems Science, ETH Zürich, Zürich, Switzerland.,Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Gabriel E Leventhal
- Department of Environmental Systems Science, ETH Zürich, Zürich, Switzerland.,Department of Civil and Environmental Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA
| | - Richard Liang
- British Columbia Centre for Excellence in HIV/AIDS, Vancouver, BC, Canada
| | - Giacomo Plazzotta
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Art F Y Poon
- Department of Pathology & Laboratory Medicine, Western University, Ontario, Canada
| | - David A Rasmussen
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Tanja Stadler
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Erik Volz
- Department of Infectious Disease Epidemiology, MRC Centre for Outbreak Analyses and Modelling, School of Public Health, Imperial College London, London, United Kingdom
| | - Caroline Weis
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Andrew J Leigh Brown
- School of Biological Sciences, Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, United Kingdom
| | - Christophe Fraser
- Department of Infectious Disease Epidemiology, MRC Centre for Outbreak Analyses and Modelling, School of Public Health, Imperial College London, London, United Kingdom.,Nuffield Department of Medicine, Li Ka Shing Centre for Health Information and Discovery, Oxford Big Data Institute, University of Oxford, Oxford, United Kingdom
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Aiello AE, Simanek AM, Eisenberg MC, Walsh AR, Davis B, Volz E, Cheng C, Rainey JJ, Uzicanin A, Gao H, Osgood N, Knowles D, Stanley K, Tarter K, Monto AS. Design and methods of a social network isolation study for reducing respiratory infection transmission: The eX-FLU cluster randomized trial. Epidemics 2016; 15:38-55. [PMID: 27266848 PMCID: PMC4903923 DOI: 10.1016/j.epidem.2016.01.001] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2015] [Revised: 01/09/2016] [Accepted: 01/19/2016] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND Social networks are increasingly recognized as important points of intervention, yet relatively few intervention studies of respiratory infection transmission have utilized a network design. Here we describe the design, methods, and social network structure of a randomized intervention for isolating respiratory infection cases in a university setting over a 10-week period. METHODOLOGY/PRINCIPAL FINDINGS 590 students in six residence halls enrolled in the eX-FLU study during a chain-referral recruitment process from September 2012-January 2013. Of these, 262 joined as "seed" participants, who nominated their social contacts to join the study, of which 328 "nominees" enrolled. Participants were cluster-randomized by 117 residence halls. Participants were asked to respond to weekly surveys on health behaviors, social interactions, and influenza-like illness (ILI) symptoms. Participants were randomized to either a 3-Day dorm room isolation intervention or a control group (no isolation) upon illness onset. ILI cases reported on their isolation behavior during illness and provided throat and nasal swab specimens at onset, day-three, and day-six of illness. A subsample of individuals (N=103) participated in a sub-study using a novel smartphone application, iEpi, which collected sensor and contextually-dependent survey data on social interactions. Within the social network, participants were significantly positively assortative by intervention group, enrollment type, residence hall, iEpi participation, age, gender, race, and alcohol use (all P<0.002). CONCLUSIONS/SIGNIFICANCE We identified a feasible study design for testing the impact of isolation from social networks in a university setting. These data provide an unparalleled opportunity to address questions about isolation and infection transmission, as well as insights into social networks and behaviors among college-aged students. Several important lessons were learned over the course of this project, including feasible isolation durations, the need for extensive organizational efforts, as well as the need for specialized programmers and server space for managing survey and smartphone data.
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Affiliation(s)
- Allison E Aiello
- University of North Carolina-Chapel Hill, Gillings School of Global Public Health, Chapel Hill, NC, United States.
| | - Amanda M Simanek
- Joseph J. Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI, United States
| | - Marisa C Eisenberg
- Department of Epidemiology, University of Michigan-School of Public Health, Ann Arbor, MI, United States
| | - Alison R Walsh
- Department of Epidemiology, University of Michigan-School of Public Health, Ann Arbor, MI, United States
| | - Brian Davis
- Department of Epidemiology, University of Michigan-School of Public Health, Ann Arbor, MI, United States
| | | | - Caroline Cheng
- Department of Epidemiology, University of Michigan-School of Public Health, Ann Arbor, MI, United States
| | - Jeanette J Rainey
- Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Amra Uzicanin
- Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Hongjiang Gao
- Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Nathaniel Osgood
- University of Saskatchewan, Department of Computer Science, Saskatoon, SK, Canada
| | - Dylan Knowles
- University of Saskatchewan, Department of Computer Science, Saskatoon, SK, Canada
| | - Kevin Stanley
- University of Saskatchewan, Department of Computer Science, Saskatoon, SK, Canada
| | - Kara Tarter
- Department of Epidemiology, University of Michigan-School of Public Health, Ann Arbor, MI, United States
| | - Arnold S Monto
- Department of Epidemiology, University of Michigan-School of Public Health, Ann Arbor, MI, United States
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DeCesaris C, Woodhouse K, Volz E, Gabriel P, Suneja G, Maity A, Hahn S. Examining Patient Perceptions of Quality and Safety in a Large Radiation Oncology Department. Int J Radiat Oncol Biol Phys 2015. [DOI: 10.1016/j.ijrobp.2015.07.1813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Woodhouse K, Volz E, Bellerive M, Gabriel P, Maity A, Hahn S, Vapiwala N. The Implementation and Assessment of a Quality and Safety Culture Education Program in a Large Radiation Oncology Department. Int J Radiat Oncol Biol Phys 2015. [DOI: 10.1016/j.ijrobp.2015.07.1814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Romero-Severson EO, Volz E, Koopman JS, Leitner T, Ionides EL. Dynamic Variation in Sexual Contact Rates in a Cohort of HIV-Negative Gay Men. Am J Epidemiol 2015; 182:255-62. [PMID: 25995288 DOI: 10.1093/aje/kwv044] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2014] [Accepted: 02/09/2015] [Indexed: 12/25/2022] Open
Abstract
Human immunodeficiency virus (HIV) transmission models that include variability in sexual behavior over time have shown increased incidence, prevalence, and acute-state transmission rates for a given population risk profile. This raises the question of whether dynamic variation in individual sexual behavior is a real phenomenon that can be observed and measured. To study this dynamic variation, we developed a model incorporating heterogeneity in both between-person and within-person sexual contact patterns. Using novel methodology that we call iterated filtering for longitudinal data, we fitted this model by maximum likelihood to longitudinal survey data from the Centers for Disease Control and Prevention's Collaborative HIV Seroincidence Study (1992-1995). We found evidence for individual heterogeneity in sexual behavior over time. We simulated an epidemic process and found that inclusion of empirically measured levels of dynamic variation in individual-level sexual behavior brought the theoretical predictions of HIV incidence into closer alignment with reality given the measured per-act probabilities of transmission. The methods developed here provide a framework for quantifying variation in sexual behaviors that helps in understanding the HIV epidemic among gay men.
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Abstract
Background: The Ebola virus (EBOV) epidemic in Western Africa is the largest in recorded history and control efforts have so far failed to stem the rapid growth in the number of infections. Mathematical models serve a key role in estimating epidemic growth rates and the reproduction number (R0) from surveillance data and, recently, molecular sequence data. Phylodynamic analysis of existing EBOV time-stamped sequence data may provide independent estimates of the unobserved number of infections, reveal recent epidemiological history, and provide insight into selective pressures acting upon viral genes.
Methods: We fit a series mathematical models of infectious disease dynamics to phylogenies estimated from 78 whole EBOV genomes collected from distinct patients in May and June of 2014 in Sierra Leone, and perform evolutionary analysis on these genomes combined with closely related EBOV genomes from previous outbreaks. Two analyses are conducted with values of the latent period that have been used in recent modelling efforts. We also examined the EBOV sequences for evidence of possible episodic adaptive molecular evolution during the 2014 outbreak.
Results: We find evidence for adaptive evolution affecting L and GP protein coding regions of the EBOV genome, which is unlikely to bias molecular clock and phylodynamic analyses. We estimate R0=2.40 (95% HPD:1.54-3.87 ) if the mean latent period is 5.3 days, and R0=3.81, (95% HPD:2.47-6.3) if the mean latent period is 12.7 days. The estimated coefficient of variation (CV) of the number of transmissions per infected host is very high, and a large proportion of infections yield no transmissions.
Conclusions: Estimates of R0 are sensitive to the unknown latent infectious period which can not be reliably estimated from genetic data alone. EBOV phylogenies show significant evidence for superspreading and extreme variance in the number of transmissions per infected individual during the early epidemic in Sierra Leone.
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Bauermeister JA, Zimmerman MA, Johns MM, Glowacki P, Stoddard S, Volz E. Innovative recruitment using online networks: lessons learned from an online study of alcohol and other drug use utilizing a web-based, respondent-driven sampling (webRDS) strategy. J Stud Alcohol Drugs 2013; 73:834-8. [PMID: 22846248 DOI: 10.15288/jsad.2012.73.834] [Citation(s) in RCA: 111] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
OBJECTIVE We used a web version of Respondent-Driven Sampling (webRDS) to recruit a sample of young adults (ages 18-24) and examined whether this strategy would result in alcohol and other drug (AOD) prevalence estimates comparable to national estimates (National Survey on Drug Use and Health [NSDUH]). METHOD We recruited 22 initial participants (seeds) via Facebook to complete a web survey examining AOD risk correlates. Sequential, incentivized recruitment continued until our desired sample size was achieved. After correcting for webRDS clustering effects, we contrasted our AOD prevalence estimates (past 30 days) to NSDUH estimates by comparing the 95% confidence intervals of prevalence estimates. RESULTS We found comparable AOD prevalence estimates between our sample and NSDUH for the past 30 days for alcohol, marijuana, cocaine, Ecstasy (3,4-methylenedioxymethamphetamine, or MDMA), and hallucinogens. Cigarette use was lower than NSDUH estimates. CONCLUSIONS WebRDS may be a suitable strategy to recruit young adults online. We discuss the unique strengths and challenges that may be encountered by public health researchers using webRDS methods.
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Affiliation(s)
- José A Bauermeister
- Department of Health Behavior and Health Education, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.
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Sadasivam RS, Cutrona SL, Volz E, Rao SR, Houston TK. Web-based peer-driven chain referrals for smoking cessation. Stud Health Technol Inform 2013; 192:357-361. [PMID: 23920576 PMCID: PMC4339050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
BACKGROUND We are testing web-based respondent-driven sampling (RDS) chain referrals to recruit smokers to the Decide2Quit.org (D2Q) web-assisted tobacco intervention. METHODS Using an online survey of smokers, we assessed the potential of recruiting 1200 smokers in 9 months using RDS chain referrals. RDS is a complex sample design, and many factors can influence its success. We conducted simulations to determine the design of optimal RDS chains. RESULTS Smokers (n=48) were mostly female (72%) and between ages 30-60 (82%). Estimation of smokers in their network: 1-5 (40%), 6-10 (24%), and 10-20 (22%), with mean number of intimate family (2.2, SD=2.1) and close friend smokers (3.7, SD=3.8). Most smokers (82%) were willing to refer to D2Q and thought their friends (mean=5.0, SD=4.4, range=0-20) would be open to referral. Simulations suggested that with a quota of 3 and 10 seeds, 99.9% of the sample would be achieved in 107 days if the acceptance probability was 0.5. Acceptance probability of 25% would necessitate an increased quota. CONCLUSIONS Our study suggests that it is possible to recruit smokers using RDS.
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Affiliation(s)
- Rajani S Sadasivam
- VA eHealth Quality Enhancement Research Initiative, Bedford VAMC, Bedford, Massachusetts, USA
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Volz E, Gabriel P, Bergendahl H, Maity A, Hahn S. Improving Safety Culture Through Incident Reporting. Int J Radiat Oncol Biol Phys 2012. [DOI: 10.1016/j.ijrobp.2012.07.263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Craft ME, Volz E, Packer C, Meyers LA. Disease transmission in territorial populations: the small-world network of Serengeti lions. J R Soc Interface 2011; 8:776-86. [PMID: 21030428 PMCID: PMC3104347 DOI: 10.1098/rsif.2010.0511] [Citation(s) in RCA: 112] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2010] [Accepted: 10/06/2010] [Indexed: 11/12/2022] Open
Abstract
Territoriality in animal populations creates spatial structure that is thought to naturally buffer disease invasion. Often, however, territorial populations also include highly mobile, non-residential individuals that potentially serve as disease superspreaders. Using long-term data from the Serengeti Lion Project, we characterize the contact network structure of a territorial wildlife population and address the epidemiological impact of nomadic individuals. As expected, pride contacts are dominated by interactions with neighbouring prides and interspersed by encounters with nomads as they wander throughout the ecosystem. Yet the pride-pride network also includes occasional long-range contacts between prides, making it surprisingly small world and vulnerable to epidemics, even without nomads. While nomads increase both the local and global connectivity of the network, their epidemiological impact is marginal, particularly for diseases with short infectious periods like canine distemper virus. Thus, territoriality in Serengeti lions may be less protective and non-residents less important for disease transmission than previously considered.
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Affiliation(s)
- Meggan E Craft
- Boyd Orr Centre for Population and Ecosystem Health, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK.
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Volz E, Frost SD, Rothenberg R, Meyers LA. Epidemiological bridging by injection drug use drives an early HIV epidemic. Epidemics 2010; 2:155-164. [DOI: 10.1016/j.epidem.2010.06.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2010] [Revised: 05/12/2010] [Accepted: 06/17/2010] [Indexed: 10/19/2022] Open
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Abstract
The reproductive ratio, R0, is a fundamental quantity in epidemiology, which determines the initial increase in an infectious disease in a susceptible host population. In most epidemic models, there is a specific value of R0, the epidemic threshold, above which epidemics are possible, but below which epidemics cannot occur. As the complexity of an epidemic model increases, so too does the difficulty of calculating epidemic thresholds. Here we derive the reproductive ratio and epidemic thresholds for susceptible-infected-recovered (SIR) epidemics in a simple class of dynamic random networks. As in most epidemiological models, R0 depends on two basic epidemic parameters, the transmission and recovery rates. We find that R0 also depends on social parameters, namely the degree distribution that describes heterogeneity in the numbers of concurrent contacts and the mixing parameter that gives the rate at which contacts are initiated and terminated. We show that social mixing fundamentally changes the epidemiological landscape and, consequently, that static network approximations of dynamic networks can be inadequate.
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Affiliation(s)
- Erik Volz
- Integrative Biology, University of Texas at Austin, Austin, TX 78712, USA.
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