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Kulmala I, Taipale A, Sanmark E, Lastovets N, Sormunen P, Nuorti P, Saari S, Luoto A, Säämänen A. Estimated relative potential for airborne SARS-CoV-2 transmission in a day care centre. Heliyon 2024; 10:e30724. [PMID: 38756615 PMCID: PMC11096945 DOI: 10.1016/j.heliyon.2024.e30724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 05/01/2024] [Accepted: 05/02/2024] [Indexed: 05/18/2024] Open
Abstract
We estimated the hourly probability of airborne severe acute respiratory coronavirus 2 (SARS-CoV-2) transmission and further the estimated number of persons at transmission risk in a day care centre by calculating the inhaled dose for airborne pathogens based on their concentration, exposure time and activity. Information about the occupancy and activity of the rooms was collected from day care centre personnel and building characteristics were obtained from the design values. The generation rate of pathogens was calculated as a product of viral load of the respiratory fluids and the emission of the exhaled airborne particles, considering the prevalence of the disease and the activity of the individuals. A well-mixed model was used in the estimation of the concentration of pathogens in the air. The Wells-Riley model was used for infection probability. The approach presented in this study was utilised in the identification of hot spots and critical events in the day care centre. Large variation in the infection probabilities and estimated number of persons at transmission risk was observed when modelling a normal day at the centre. The estimated hourly infection probabilities between the worst hour in the worst room and the best hour in the best room varied in the ratio of 100:1. Similarly, the number of persons at transmission risk between the worst and best cases varied in the ratio 1000:1. Although there are uncertainties in the input values affecting the absolute risk estimates the model proved to be useful in ranking and identifying the hot spots and events in the building and implementing effective control measures.
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Affiliation(s)
- Ilpo Kulmala
- VTT Smart Energy and Built Environment, Visiokatu 4, PO Box 1300, FI-33101, Tampere, Finland
| | - Aimo Taipale
- VTT Smart Energy and Built Environment, Visiokatu 4, PO Box 1300, FI-33101, Tampere, Finland
| | - Enni Sanmark
- Helsinki University Hospital, Department of Otorhinolaryngology and Phoniatrics – Head and Neck Surgery, Helsinki, Finland
- University of Helsinki, Helsinki, Finland
| | - Natalia Lastovets
- Tampere University, Faculty of Built Environment, Civil Engineering Unit, Korkeakoulunkatu 5D, FI-33720, Tampere, Finland
| | - Piia Sormunen
- Tampere University, Faculty of Built Environment, Civil Engineering Unit, Korkeakoulunkatu 5D, FI-33720, Tampere, Finland
| | - Pekka Nuorti
- Tampere University, Faculty of Social Sciences, Health Sciences Unit, Arvo Ylpön Katu 34, 33520, Tampere, Finland
| | - Sampo Saari
- Tampere University of Applied Sciences, Kuntokatu 3, 33520, Tampere, Finland
| | - Anni Luoto
- Granlund Oy, Malminkaari 21, 00700, Helsinki, Finland
| | - Arto Säämänen
- VTT Smart Energy and Built Environment, Visiokatu 4, PO Box 1300, FI-33101, Tampere, Finland
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2
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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] [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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3
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Harari S, Miller D, Fleishon S, Burstein D, Stern A. Using big sequencing data to identify chronic SARS-Coronavirus-2 infections. Nat Commun 2024; 15:648. [PMID: 38245511 PMCID: PMC10799923 DOI: 10.1038/s41467-024-44803-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 01/04/2024] [Indexed: 01/22/2024] Open
Abstract
The evolution of SARS-Coronavirus-2 (SARS-CoV-2) has been characterized by the periodic emergence of highly divergent variants. One leading hypothesis suggests these variants may have emerged during chronic infections of immunocompromised individuals, but limited data from these cases hinders comprehensive analyses. Here, we harnessed millions of SARS-CoV-2 genomes to identify potential chronic infections and used language models (LM) to infer chronic-associated mutations. First, we mined the SARS-CoV-2 phylogeny and identified chronic-like clades with identical metadata (location, age, and sex) spanning over 21 days, suggesting a prolonged infection. We inferred 271 chronic-like clades, which exhibited characteristics similar to confirmed chronic infections. Chronic-associated mutations were often high-fitness immune-evasive mutations located in the spike receptor-binding domain (RBD), yet a minority were unique to chronic infections and absent in global settings. The probability of observing high-fitness RBD mutations was 10-20 times higher in chronic infections than in global transmission chains. The majority of RBD mutations in BA.1/BA.2 chronic-like clades bore predictive value, i.e., went on to display global success. Finally, we used our LM to infer hundreds of additional chronic-like clades in the absence of metadata. Our approach allows mining extensive sequencing data and providing insights into future evolutionary patterns of SARS-CoV-2.
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Affiliation(s)
- Sheri Harari
- The Shmunis School of Biomedicine and Cancer Research, Tel Aviv University, Tel Aviv, Israel
- Edmond J. Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv, Israel
| | - Danielle Miller
- The Shmunis School of Biomedicine and Cancer Research, Tel Aviv University, Tel Aviv, Israel
- Edmond J. Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv, Israel
| | - Shay Fleishon
- Israeli Health Intelligence Agency, Public Health Division, Ministry of Health, Jerusalem, Israel
| | - David Burstein
- The Shmunis School of Biomedicine and Cancer Research, Tel Aviv University, Tel Aviv, Israel
- Edmond J. Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv, Israel
| | - Adi Stern
- The Shmunis School of Biomedicine and Cancer Research, Tel Aviv University, Tel Aviv, Israel.
- Edmond J. Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv, Israel.
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4
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Zaidi AK, Singh RB. Epidemiology of COVID-19. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 202:25-38. [PMID: 38237988 DOI: 10.1016/bs.pmbts.2023.09.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
This chapter provides a detailed exploration of the epidemiology of COVID-19, focusing on several key aspects that offer valuable insights into the disease progression. A comprehensive comparison is made between the three related coronaviruses: SARS-CoV, MERS-CoV, and SARS-CoV-2, elucidating their similarities and differences in terms of transmission dynamics, clinical presentation, laboratory and radiological findings, infection mechanisms, and mortality rates. The concept of herd immunity is then discussed, exploring its relevance and potential implications for controlling the spread of COVID-19. Next, the chapter delves into the changing epidemiology of the disease, examining how various factors such as human behavior, public health interventions, and viral mutations have influenced its transmission patterns and severity over time. Finally, the timelines and evolution of COVID-19 are outlined, tracing the origins of the virus, its rapid global spread, and the emergence of new variants.
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Affiliation(s)
| | - Rohan Bir Singh
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, United State; Department of Population, Policy and Practice, Greater Ormond Street Institute of Child Health, University College London, United Kingdom; Discipline of Ophthalmology and Visual Sciences, Adelaide Medical School, University of Adelaide, Australia
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5
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Fujimoto K, Kuo J, Stott G, Lewis R, Chan HK, Lyu L, Veytsel G, Carr M, Broussard T, Short K, Brown P, Sealy R, Brown A, Bahl J. Beyond scale-free networks: integrating multilayer social networks with molecular clusters in the local spread of COVID-19. Sci Rep 2023; 13:21861. [PMID: 38071385 PMCID: PMC10710469 DOI: 10.1038/s41598-023-49109-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 12/04/2023] [Indexed: 12/18/2023] Open
Abstract
This study evaluates the scale-free network assumption commonly used in COVID-19 epidemiology, using empirical social network data from SARS-CoV-2 Delta variant molecular local clusters in Houston, Texas. We constructed genome-informed social networks from contact and co-residence data, tested them for scale-free power-law distributions that imply highly connected hubs, and compared them to alternative models (exponential, log-normal, power-law with exponential cutoff, and Weibull) that suggest more evenly distributed network connections. Although the power-law model failed the goodness of fit test, after incorporating social network ties, the power-law model was at least as good as, if not better than, the alternatives, implying the presence of both hub and non-hub mechanisms in local SARS-CoV-2 transmission. These findings enhance our understanding of the complex social interactions that drive SARS-CoV-2 transmission, thereby informing more effective public health interventions.
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Affiliation(s)
- Kayo Fujimoto
- School of Public Health, University of Texas Health Science Center at Houston, 7000 Fannin Street, UCT 2514, Houston, TX, 77030, USA.
| | - Jacky Kuo
- School of Public Health, University of Texas Health Science Center at Houston, 7000 Fannin Street, UCT 2514, Houston, TX, 77030, USA
| | - Guppy Stott
- Institute of Bioinformatics, University of Georgia, 501 D.W. Brooks Drive, Athens, GA, 30602, USA
| | - Ryan Lewis
- School of Public Health, University of Texas Health Science Center at Houston, 7000 Fannin Street, UCT 2514, Houston, TX, 77030, USA
| | - Hei Kit Chan
- School of Public Health, University of Texas Health Science Center at Houston, 7000 Fannin Street, UCT 2514, Houston, TX, 77030, USA
| | - Leke Lyu
- Institute of Bioinformatics, University of Georgia, 501 D.W. Brooks Drive, Athens, GA, 30602, USA
| | - Gabriella Veytsel
- Institute of Bioinformatics, University of Georgia, 501 D.W. Brooks Drive, Athens, GA, 30602, USA
| | | | | | | | - Pamela Brown
- City of Houston Health Department, Houston, TX, USA
| | - Roger Sealy
- City of Houston Health Department, Houston, TX, USA
| | - Armand Brown
- City of Houston Health Department, Houston, TX, USA
| | - Justin Bahl
- Institute of Bioinformatics, University of Georgia, 501 D.W. Brooks Drive, Athens, GA, 30602, USA.
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6
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Molina Grané C, Mancuso P, Vicentini M, Venturelli F, Djuric O, Manica M, Guzzetta G, Marziano V, Zardini A, d'Andrea V, Trentini F, Bisaccia E, Larosa E, Cilloni S, Cassinadri MT, Pezzotti P, Ajelli M, Rossi PG, Merler S, Poletti P. SARS-CoV-2 transmission patterns in educational settings during the Alpha wave in Reggio-Emilia, Italy. Epidemics 2023; 44:100712. [PMID: 37567090 DOI: 10.1016/j.epidem.2023.100712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 07/17/2023] [Accepted: 07/31/2023] [Indexed: 08/13/2023] Open
Abstract
Different monitoring and control policies have been implemented in schools to minimize the spread of SARS-CoV-2. Transmission in schools has been hard to quantify due to the large proportion of asymptomatic carriers in young individuals. We applied a Bayesian approach to reconstruct the transmission chains between 284 SARS-CoV-2 infections ascertained during 87 school outbreak investigations conducted between March and April 2021 in Italy. Under the policy of reactive quarantines, we found that 42.5% (95%CrI: 29.5-54.3%) of infections among school attendees were caused by school contacts. The mean number of secondary cases infected at school by a positive individual during in-person education was estimated to be 0.33 (95%CrI: 0.23-0.43), with marked heterogeneity across individuals. Specifically, we estimated that only 26.0% (95%CrI: 17.6-34.1%) of students and school personnel who tested positive during in-person education caused at least one secondary infection at school. Positive individuals who attended school for at least 6 days before being isolated or quarantined infected on average 0.49 (95%CrI: 0.14-0.83) secondary cases. Our findings provide quantitative insights on the contribution of school transmission to the spread of SARS-CoV-2 in young individuals. Identifying positive cases within 5 days after exposure to their infector could reduce onward transmission at school by at least 30%.
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Affiliation(s)
- Carla Molina Grané
- Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy; Department of Mathematics, University of Trento, Trento, Italy
| | - Pamela Mancuso
- Epidemiology Unit, Azienda Unità Sanitaria Locale - IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Massimo Vicentini
- Epidemiology Unit, Azienda Unità Sanitaria Locale - IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Francesco Venturelli
- Epidemiology Unit, Azienda Unità Sanitaria Locale - IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Olivera Djuric
- Epidemiology Unit, Azienda Unità Sanitaria Locale - IRCCS di Reggio Emilia, Reggio Emilia, Italy; Department of Biomedical, Metabolic and Neural Sciences, Centre for Environmental, Nutritional and Genetic Epidemiology (CREAGEN), Public Health Unit, University of Modena and Reggio Emilia, Reggio Emilia, Italy
| | - Mattia Manica
- Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy
| | - Giorgio Guzzetta
- Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy
| | | | - Agnese Zardini
- Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy
| | - Valeria d'Andrea
- Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy
| | - Filippo Trentini
- Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy; Dondena Centre for Research on Social Dynamics and Public Policy, Bocconi University, Milan, Italy
| | - Eufemia Bisaccia
- Public Health Unit, Azienda Unità Sanitaria Locale - IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Elisabetta Larosa
- Public Health Unit, Azienda Unità Sanitaria Locale - IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Silvia Cilloni
- Public Health Unit, Azienda Unità Sanitaria Locale - IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Maria Teresa Cassinadri
- Public Health Unit, Azienda Unità Sanitaria Locale - IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Patrizio Pezzotti
- Department of Infectious Diseases, Istituto Superiore di Sanità, Rome, Italy
| | - Marco Ajelli
- Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
| | - Paolo Giorgi Rossi
- Epidemiology Unit, Azienda Unità Sanitaria Locale - IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Stefano Merler
- Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy
| | - Piero Poletti
- Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy.
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7
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Sinha A, Sangeet S, Roy S. Evolution of Sequence and Structure of SARS-CoV-2 Spike Protein: A Dynamic Perspective. ACS OMEGA 2023; 8:23283-23304. [PMID: 37426203 PMCID: PMC10324094 DOI: 10.1021/acsomega.3c00944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Accepted: 06/01/2023] [Indexed: 07/11/2023]
Abstract
Novel coronavirus (SARS-CoV-2) enters its host cell through a surface spike protein. The viral spike protein has undergone several modifications/mutations at the genomic level, through which it modulated its structure-function and passed through several variants of concern. Recent advances in high-resolution structure determination and multiscale imaging techniques, cost-effective next-generation sequencing, and development of new computational methods (including information theory, statistical methods, machine learning, and many other artificial intelligence-based techniques) have hugely contributed to the characterization of sequence, structure, function of spike proteins, and its different variants to understand viral pathogenesis, evolutions, and transmission. Laying on the foundation of the sequence-structure-function paradigm, this review summarizes not only the important findings on structure/function but also the structural dynamics of different spike components, highlighting the effects of mutations on them. As dynamic fluctuations of three-dimensional spike structure often provide important clues for functional modulation, quantifying time-dependent fluctuations of mutational events over spike structure and its genetic/amino acidic sequence helps identify alarming functional transitions having implications for enhanced fusogenicity and pathogenicity of the virus. Although these dynamic events are more difficult to capture than quantifying a static, average property, this review encompasses those challenging aspects of characterizing the evolutionary dynamics of spike sequence and structure and their implications for functions.
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8
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Wegehaupt O, Endo A, Vassall A. Superspreading, overdispersion and their implications in the SARS-CoV-2 (COVID-19) pandemic: a systematic review and meta-analysis of the literature. BMC Public Health 2023; 23:1003. [PMID: 37254143 DOI: 10.1186/s12889-023-15915-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 05/17/2023] [Indexed: 06/01/2023] Open
Abstract
BACKGROUND A recurrent feature of infectious diseases is the observation that different individuals show different levels of secondary transmission. This inter-individual variation in transmission potential is often quantified by the dispersion parameter k. Low values of k indicate a high degree of variability and a greater probability of superspreading events. Understanding k for COVID-19 across contexts can assist policy makers prepare for future pandemics. METHODS A literature search following a systematic approach was carried out in PubMed, Embase, Web of Science, Cochrane Library, medRxiv, bioRxiv and arXiv to identify publications containing epidemiological findings on superspreading in COVID-19. Study characteristics, epidemiological data, including estimates for k and R0, and public health recommendations were extracted from relevant records. RESULTS The literature search yielded 28 peer-reviewed studies. The mean k estimates ranged from 0.04 to 2.97. Among the 28 studies, 93% reported mean k estimates lower than one, which is considered as marked heterogeneity in inter-individual transmission potential. Recommended control measures were specifically aimed at preventing superspreading events. The combination of forward and backward contact tracing, timely confirmation of cases, rapid case isolation, vaccination and preventive measures were suggested as important components to suppress superspreading. CONCLUSIONS Superspreading events were a major feature in the pandemic of SARS-CoV-2. On the one hand, this made outbreaks potentially more explosive but on the other hand also more responsive to public health interventions. Going forward, understanding k is critical for tailoring public health measures to high-risk groups and settings where superspreading events occur.
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Affiliation(s)
- Oliver Wegehaupt
- Institute for Immunodeficiency, Center for Chronic Immunodeficiency (CCI), Medical Center, Faculty of Medicine, University of Freiburg, Breisacherstr. 115, Freiburg, 79106, Germany.
- Clinic of Pediatric Hematology, Oncology and Stem Cell Transplantation, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK.
| | - Akira Endo
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
- The Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
- School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan
| | - Anna Vassall
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
- Department of Global Health, The Academic Medical Center (AMC), The University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
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9
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Park Y, Martin MA, Koelle K. Epidemiological inference for emerging viruses using segregating sites. Nat Commun 2023; 14:3105. [PMID: 37248255 DOI: 10.1038/s41467-023-38809-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 05/16/2023] [Indexed: 05/31/2023] Open
Abstract
Epidemiological models are commonly fit to case and pathogen sequence data to estimate parameters and to infer unobserved disease dynamics. Here, we present an inference approach based on sequence data that is well suited for model fitting early on during the expansion of a viral lineage. Our approach relies on a trajectory of segregating sites to infer epidemiological parameters within a Sequential Monte Carlo framework. Using simulated data, we first show that our approach accurately recovers key epidemiological quantities under a single-introduction scenario. We then apply our approach to SARS-CoV-2 sequence data from France, estimating a basic reproduction number of approximately 2.3-2.7 under an epidemiological model that allows for multiple introductions. Our approach presented here indicates that inference approaches that rely on simple population genetic summary statistics can be informative of epidemiological parameters and can be used for reconstructing infectious disease dynamics during the early expansion of a viral lineage.
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Affiliation(s)
- Yeongseon Park
- Graduate Program in Population Biology, Ecology, and Evolution, Emory University, Atlanta, GA, 30322, USA
| | - Michael A Martin
- Graduate Program in Population Biology, Ecology, and Evolution, Emory University, Atlanta, GA, 30322, USA
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Katia Koelle
- Department of Biology, Emory University, Atlanta, GA, 30322, USA.
- Emory Center of Excellence for Influenza Research and Response (CEIRR), Atlanta, GA, USA.
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10
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Wanelik KM, Begon M, Fenton A, Norman RA, Beldomenico PM. Positive feedback loops exacerbate the influence of superspreaders in disease transmission. iScience 2023; 26:106618. [PMID: 37250299 PMCID: PMC10214397 DOI: 10.1016/j.isci.2023.106618] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 02/13/2023] [Accepted: 04/03/2023] [Indexed: 05/31/2023] Open
Abstract
Superspreaders are recognized as being important drivers of disease spread. However, models to date have assumed random occurrence of superspreaders, irrespective of whom they were infected by. Evidence suggests though that those individuals infected by superspreaders may be more likely to become superspreaders themselves. Here, we begin to explore, theoretically, the effects of such a positive feedback loop on (1) the final epidemic size, (2) the herd immunity threshold, (3) the basic reproduction number, R0, and (4) the peak prevalence of superspreaders, using a generic model for a hypothetical acute viral infection and illustrative parameter values. We show that positive feedback loops can have a profound effect on our chosen epidemic outcomes, even when the transmission advantage of superspreaders is moderate, and despite peak prevalence of superspreaders remaining low. We argue that positive superspreader feedback loops in different infectious diseases, including SARS-CoV-2, should be investigated further, both theoretically and empirically.
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Affiliation(s)
- Klara M. Wanelik
- Department of Evolution, Ecology and Behaviour, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
- Department of Biology, University of Oxford, Oxford, UK
| | - Mike Begon
- Department of Evolution, Ecology and Behaviour, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
| | - Andy Fenton
- Department of Evolution, Ecology and Behaviour, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
| | - Rachel A. Norman
- Department of Computing Science and Mathematics, Faculty of Natural Sciences, University of Stirling, Stirling, UK
| | - Pablo M. Beldomenico
- Laboratorio de Ecología de Enfermedades, Instituto de Ciencias Veterinarias del Litoral (Consejo de Investigaciones Científicas y Técnicas - Universidad Nacional del Litoral), Esperanza, Argentina
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11
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Tavori J, Levy H. On the Convexity of the Effective Reproduction Number. J Comput Biol 2023. [PMID: 37130305 DOI: 10.1089/cmb.2022.0371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/04/2023] Open
Abstract
In this study, we analyze the evolution of the effective reproduction number, R, through a Susceptible-Infective-Recovered spreading process in heterogeneous populations; Characterizing its decay process allows to analytically study the effects of countermeasures on the progress of the virus under heterogeneity, and to optimize their policies. A striking result of recent studies has shown that heterogeneity across individuals (or superspreading) may have a drastic effect on the spreading process progression, which may cause a nonlinear decrease of R in the number of infected individuals. We account for heterogeneity and analyze the stochastic progression of the spreading process. We show that the decrease of R is, in fact, convex in the number of infected individuals, where this convexity stems from heterogeneity. The analysis is based on establishing stochastic monotonic relations between the susceptible populations in varying times of the spread. We demonstrate that the convex behavior of the effective reproduction number affects the performance of countermeasures used to fight the spread of a virus. The results are applicable to the control of virus and malware spreading in computer networks as well. We examine numerically the sensitivity of the herd immunity threshold to the heterogeneity level and to the chosen countermeasures policy.
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Affiliation(s)
- Jhonatan Tavori
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
| | - Hanoch Levy
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
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12
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Marcus-Mandelblit N, Ashkenazi-Hoffnung L, Scheuerman O. Reply to "SARS-CoV-2 reinfection or persistence among immunodeficient patients". THE JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY. IN PRACTICE 2023; 11:972-973. [PMID: 36894285 PMCID: PMC9989502 DOI: 10.1016/j.jaip.2022.11.049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 11/23/2022] [Indexed: 03/09/2023]
Affiliation(s)
- Nufar Marcus-Mandelblit
- Schneider Children's Medical Center of Israel, Kipper Institute of Immunology, Petah Tikva, Israel.
| | - Liat Ashkenazi-Hoffnung
- Department of Day Hospitalization, Pediatric Infectious Disease Unit, Schneider Children's Medical Center of Israel, Petah Tikva, Israel
| | - Oded Scheuerman
- Pediatrics B, Pediatric Infectious Disease Unit, Schneider Children's Medical Center of Israel, Petah Tikva, Israel
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13
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Low Viral Diversity Limits the Effectiveness of Sequence-Based Transmission Inference for SARS-CoV-2. mSphere 2023; 8:e0054422. [PMID: 36695609 PMCID: PMC9942562 DOI: 10.1128/msphere.00544-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
Tracking the spread of infection amongst individuals within and between communities has been a major challenge during viral outbreaks. With the unprecedented scale of viral sequence data collection during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, the possibility of using phylogenetics to reconstruct past transmission events has been explored as a more rigorous alternative to traditional contact tracing; however, the reliability of sequence-based inference of transmission networks has yet to be directly evaluated. E. E. Bendall, G. Paz-Bailey, G. A. Santiago, C. A. Porucznik, et al. (mSphere 7:e00400-22, 2022, https://doi.org/10.1128/mSphere.00400-22) evaluate the potential of this technique by applying best practices sequence comparison methods to three geographically distinct cohorts that include known transmission pairs and demonstrate that linked pairs are often indistinguishable from unrelated samples. This study clearly establishes how low viral diversity limits the utility of genomic methods of epidemiological inference for SARS-CoV-2.
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14
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Nadeau SA, Vaughan TG, Beckmann C, Topolsky I, Chen C, Hodcroft E, Schär T, Nissen I, Santacroce N, Burcklen E, Ferreira P, Jablonski KP, Posada-Céspedes S, Capece V, Seidel S, Santamaria de Souza N, Martinez-Gomez JM, Cheng P, Bosshard PP, Levesque MP, Kufner V, Schmutz S, Zaheri M, Huber M, Trkola A, Cordey S, Laubscher F, Gonçalves AR, Aeby S, Pillonel T, Jacot D, Bertelli C, Greub G, Leuzinger K, Stange M, Mari A, Roloff T, Seth-Smith H, Hirsch HH, Egli A, Redondo M, Kobel O, Noppen C, du Plessis L, Beerenwinkel N, Neher RA, Beisel C, Stadler T. Swiss public health measures associated with reduced SARS-CoV-2 transmission using genome data. Sci Transl Med 2023; 15:eabn7979. [PMID: 36346321 PMCID: PMC9765449 DOI: 10.1126/scitranslmed.abn7979] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Genome sequences from evolving infectious pathogens allow quantification of case introductions and local transmission dynamics. We sequenced 11,357 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes from Switzerland in 2020-the sixth largest effort globally. Using a representative subset of these data, we estimated viral introductions to Switzerland and their persistence over the course of 2020. We contrasted these estimates with simple null models representing the absence of certain public health measures. We show that Switzerland's border closures decoupled case introductions from incidence in neighboring countries. Under a simple model, we estimate an 86 to 98% reduction in introductions during Switzerland's strictest border closures. Furthermore, the Swiss 2020 partial lockdown roughly halved the time for sampled introductions to die out. Last, we quantified local transmission dynamics once introductions into Switzerland occurred using a phylodynamic model. We found that transmission slowed 35 to 63% upon outbreak detection in summer 2020 but not in fall. This finding may indicate successful contact tracing over summer before overburdening in fall. The study highlights the added value of genome sequencing data for understanding transmission dynamics.
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Affiliation(s)
- Sarah A. Nadeau
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland.,Corresponding author. (T.S.); (S.A.N.)
| | - Timothy G. Vaughan
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland
| | | | - Ivan Topolsky
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland
| | - Chaoran Chen
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland
| | - Emma Hodcroft
- SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland.,Institute for Social and Preventive Medicine, University of Bern; 3012, Bern, Switzerland
| | - Tobias Schär
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland
| | - Ina Nissen
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland
| | - Natascha Santacroce
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland
| | - Elodie Burcklen
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland
| | - Pedro Ferreira
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland
| | - Kim Philipp Jablonski
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland
| | - Susana Posada-Céspedes
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland
| | - Vincenzo Capece
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland
| | - Sophie Seidel
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland
| | | | - Julia M. Martinez-Gomez
- Department of Dermatology, University Hospital Zurich, University of Zurich; 8091, Zurich, Switzerland
| | - Phil Cheng
- Department of Dermatology, University Hospital Zurich, University of Zurich; 8091, Zurich, Switzerland
| | - Philipp P. Bosshard
- Department of Dermatology, University Hospital Zurich, University of Zurich; 8091, Zurich, Switzerland
| | - Mitchell P. Levesque
- Department of Dermatology, University Hospital Zurich, University of Zurich; 8091, Zurich, Switzerland
| | - Verena Kufner
- Institute of Medical Virology, University of Zurich; 8057, Zurich, Switzerland
| | - Stefan Schmutz
- Institute of Medical Virology, University of Zurich; 8057, Zurich, Switzerland
| | - Maryam Zaheri
- Institute of Medical Virology, University of Zurich; 8057, Zurich, Switzerland
| | - Michael Huber
- Institute of Medical Virology, University of Zurich; 8057, Zurich, Switzerland
| | - Alexandra Trkola
- Institute of Medical Virology, University of Zurich; 8057, Zurich, Switzerland
| | - Samuel Cordey
- Laboratory of Virology, Department of Diagnostics, Geneva University Hospitals & Faculty of Medicine; 1205, Geneva, Switzerland
| | - Florian Laubscher
- Laboratory of Virology, Department of Diagnostics, Geneva University Hospitals & Faculty of Medicine; 1205, Geneva, Switzerland
| | - Ana Rita Gonçalves
- Swiss National Reference Centre for Influenza, University Hospitals of Geneva; 1205, Geneva, Switzerland
| | - Sébastien Aeby
- Institute of Microbiology, University Hospital Centre and University of Lausanne; 1011, Lausanne, Switzerland
| | - Trestan Pillonel
- Institute of Microbiology, University Hospital Centre and University of Lausanne; 1011, Lausanne, Switzerland
| | - Damien Jacot
- Institute of Microbiology, University Hospital Centre and University of Lausanne; 1011, Lausanne, Switzerland
| | - Claire Bertelli
- Institute of Microbiology, University Hospital Centre and University of Lausanne; 1011, Lausanne, Switzerland
| | - Gilbert Greub
- Institute of Microbiology, University Hospital Centre and University of Lausanne; 1011, Lausanne, Switzerland
| | - Karoline Leuzinger
- Division of Clinical Virology, University Hospital Basel; 4051, Basel, Switzerland.,Department of Biomedicine, University of Basel; 4031, Basel, Switzerland
| | - Madlen Stange
- SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland.,Department of Biomedicine, University of Basel; 4031, Basel, Switzerland.,Division of Clinical Bacteriology and Mycology, University Hospital Basel; 4031, Basel, Switzerland
| | - Alfredo Mari
- SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland.,Department of Biomedicine, University of Basel; 4031, Basel, Switzerland.,Division of Clinical Bacteriology and Mycology, University Hospital Basel; 4031, Basel, Switzerland
| | - Tim Roloff
- SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland.,Department of Biomedicine, University of Basel; 4031, Basel, Switzerland.,Division of Clinical Bacteriology and Mycology, University Hospital Basel; 4031, Basel, Switzerland
| | - Helena Seth-Smith
- SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland.,Department of Biomedicine, University of Basel; 4031, Basel, Switzerland.,Division of Clinical Bacteriology and Mycology, University Hospital Basel; 4031, Basel, Switzerland
| | - Hans H. Hirsch
- Division of Clinical Virology, University Hospital Basel; 4051, Basel, Switzerland.,Department of Biomedicine, University of Basel; 4031, Basel, Switzerland
| | - Adrian Egli
- Department of Biomedicine, University of Basel; 4031, Basel, Switzerland.,Division of Clinical Bacteriology and Mycology, University Hospital Basel; 4031, Basel, Switzerland
| | | | | | | | - Louis du Plessis
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland
| | - Richard A. Neher
- SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland.,Biozentrum, University of Basel; 4056, Basel, Switzerland
| | - Christian Beisel
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland
| | - Tanja Stadler
- Department of Biosystems Science and Engineering, ETH Zurich; 4058, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics; 1015, Lausanne, Switzerland.,Corresponding author. (T.S.); (S.A.N.)
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15
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Lamkiewicz K, Esquivel Gomez LR, Kühnert D, Marz M. Genome Structure, Life Cycle, and Taxonomy of Coronaviruses and the Evolution of SARS-CoV-2. Curr Top Microbiol Immunol 2023; 439:305-339. [PMID: 36592250 DOI: 10.1007/978-3-031-15640-3_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Coronaviruses have a broad host range and exhibit high zoonotic potential. In this chapter, we describe their genomic organization in terms of encoded proteins and provide an introduction to the peculiar discontinuous transcription mechanism. Further, we present evolutionary conserved genomic RNA secondary structure features, which are involved in the complex replication mechanism. With a focus on computational methods, we review the emergence of SARS-CoV-2 starting with the 2019 strains. In that context, we also discuss the debated hypothesis of whether SARS-CoV-2 was created in a laboratory. We focus on the molecular evolution and the epidemiological dynamics of this recently emerged pathogen and we explain how variants of concern are detected and characterised. COVID-19, the disease caused by SARS-CoV-2, can spread through different transmission routes and also depends on a number of risk factors. We describe how current computational models of viral epidemiology, or more specifically, phylodynamics, have facilitated and will continue to enable a better understanding of the epidemic dynamics of SARS-CoV-2.
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Affiliation(s)
- Kevin Lamkiewicz
- RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University Jena, Leutragraben 1, 07743, Jena, Germany
- European Virus Bioinformatics Center, Leutragraben 1, 07743, Jena, Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstr. 4, 04103, Leipzig, Germany
| | - Luis Roger Esquivel Gomez
- Transmission, Infection, Diversification and Evolution Group, Max Planck Institute for the Science of Human History, Kahlaische Straße 10, 07745, Jena, Germany
| | - Denise Kühnert
- Transmission, Infection, Diversification and Evolution Group, Max Planck Institute for the Science of Human History, Kahlaische Straße 10, 07745, Jena, Germany
- European Virus Bioinformatics Center, Leutragraben 1, 07743, Jena, Germany
| | - Manja Marz
- RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University Jena, Leutragraben 1, 07743, Jena, Germany.
- European Virus Bioinformatics Center, Leutragraben 1, 07743, Jena, Germany.
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstr. 4, 04103, Leipzig, Germany.
- FLI Leibniz Institute for Age Research, Beutenbergstraße 11, 07745, Jena, Germany.
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16
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Jankowiak M, Obermeyer FH, Lemieux JE. Inferring selection effects in SARS-CoV-2 with Bayesian Viral Allele Selection. PLoS Genet 2022; 18:e1010540. [PMID: 36508459 PMCID: PMC9779722 DOI: 10.1371/journal.pgen.1010540] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 12/22/2022] [Accepted: 11/23/2022] [Indexed: 12/14/2022] Open
Abstract
The global effort to sequence millions of SARS-CoV-2 genomes has provided an unprecedented view of viral evolution. Characterizing how selection acts on SARS-CoV-2 is critical to developing effective, long-lasting vaccines and other treatments, but the scale and complexity of genomic surveillance data make rigorous analysis challenging. To meet this challenge, we develop Bayesian Viral Allele Selection (BVAS), a principled and scalable probabilistic method for inferring the genetic determinants of differential viral fitness and the relative growth rates of viral lineages, including newly emergent lineages. After demonstrating the accuracy and efficacy of our method through simulation, we apply BVAS to 6.9 million SARS-CoV-2 genomes. We identify numerous mutations that increase fitness, including previously identified mutations in the SARS-CoV-2 Spike and Nucleocapsid proteins, as well as mutations in non-structural proteins whose contribution to fitness is less well characterized. In addition, we extend our baseline model to identify mutations whose fitness exhibits strong dependence on vaccination status as well as pairwise interaction effects, i.e. epistasis. Strikingly, both these analyses point to the pivotal role played by the N501 residue in the Spike protein. Our method, which couples Bayesian variable selection with a diffusion approximation in allele frequency space, lays a foundation for identifying fitness-associated mutations under the assumption that most alleles are neutral.
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Affiliation(s)
- Martin Jankowiak
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
- * E-mail:
| | - Fritz H. Obermeyer
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
- Generate Biomedicines, Cambridge, Massachusetts, United States of America
| | - Jacob E. Lemieux
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
- Division of Infectious Diseases, Massachusetts General Hospital, Cambridge, Massachusetts, United States of America
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17
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Stockdale JE, Liu P, Colijn C. The potential of genomics for infectious disease forecasting. Nat Microbiol 2022; 7:1736-1743. [PMID: 36266338 DOI: 10.1038/s41564-022-01233-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 08/18/2022] [Indexed: 11/09/2022]
Abstract
Genomic technologies have led to tremendous gains in understanding how pathogens function, evolve and interact. Pathogen diversity is now measurable at high precision and resolution, in part because over the past decade, sequencing technologies have increased in speed and capacity, at decreased cost. Alongside this, the use of models that can forecast emergence and size of infectious disease outbreaks has risen, highlighted by the coronavirus disease 2019 pandemic but also due to modelling advances that allow for rapid estimates in emerging outbreaks to inform monitoring, coordination and resource deployment. However, genomics studies have remained largely retrospective. While they contain high-resolution views of pathogen diversification and evolution in the context of selection, they are often not aligned with designing interventions. This is a missed opportunity because pathogen diversification is at the core of the most pressing infectious public health challenges, and interventions need to take the mechanisms of virulence and understanding of pathogen diversification into account. In this Perspective, we assess these converging fields, discuss current challenges facing both surveillance specialists and modellers who want to harness genomic data, and propose next steps for integrating longitudinally sampled genomic data with statistical learning and interpretable modelling to make reliable predictions into the future.
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Affiliation(s)
- Jessica E Stockdale
- Department of Mathematics, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Pengyu Liu
- Department of Mathematics, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Caroline Colijn
- Department of Mathematics, Simon Fraser University, Burnaby, British Columbia, Canada.
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18
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Steiner MC, Novembre J. Population genetic models for the spatial spread of adaptive variants: A review in light of SARS-CoV-2 evolution. PLoS Genet 2022; 18:e1010391. [PMID: 36137003 PMCID: PMC9498967 DOI: 10.1371/journal.pgen.1010391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Theoretical population genetics has long studied the arrival and geographic spread of adaptive variants through the analysis of mathematical models of dispersal and natural selection. These models take on a renewed interest in the context of the COVID-19 pandemic, especially given the consequences that novel adaptive variants have had on the course of the pandemic as they have spread through global populations. Here, we review theoretical models for the spatial spread of adaptive variants and identify areas to be improved in future work, toward a better understanding of variants of concern in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) evolution and other contemporary applications. As we describe, characteristics of pandemics such as COVID-19-such as the impact of long-distance travel patterns and the overdispersion of lineages due to superspreading events-suggest new directions for improving upon existing population genetic models.
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Affiliation(s)
- Margaret C. Steiner
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
| | - John Novembre
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
- Department of Ecology & Evolution, University of Chicago, Chicago, Illinois, United States of America
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19
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Attwood SW, Hill SC, Aanensen DM, Connor TR, Pybus OG. Phylogenetic and phylodynamic approaches to understanding and combating the early SARS-CoV-2 pandemic. Nat Rev Genet 2022; 23:547-562. [PMID: 35459859 PMCID: PMC9028907 DOI: 10.1038/s41576-022-00483-8] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/23/2022] [Indexed: 01/05/2023]
Abstract
Determining the transmissibility, prevalence and patterns of movement of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections is central to our understanding of the impact of the pandemic and to the design of effective control strategies. Phylogenies (evolutionary trees) have provided key insights into the international spread of SARS-CoV-2 and enabled investigation of individual outbreaks and transmission chains in specific settings. Phylodynamic approaches combine evolutionary, demographic and epidemiological concepts and have helped track virus genetic changes, identify emerging variants and inform public health strategy. Here, we review and synthesize studies that illustrate how phylogenetic and phylodynamic techniques were applied during the first year of the pandemic, and summarize their contributions to our understanding of SARS-CoV-2 transmission and control.
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Affiliation(s)
- Stephen W Attwood
- Department of Zoology, University of Oxford, Oxford, UK.
- Pathogen Genomics Unit, Public Health Wales NHS Trust, Cardiff, UK.
| | - Sarah C Hill
- Department of Pathobiology and Population Sciences, Royal Veterinary College, University of London, London, UK
| | - David M Aanensen
- 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
| | - Thomas R Connor
- Pathogen Genomics Unit, Public Health Wales NHS Trust, Cardiff, UK
- School of Biosciences, Cardiff University, Cardiff, UK
| | - Oliver G Pybus
- Department of Zoology, University of Oxford, Oxford, UK.
- Department of Pathobiology and Population Sciences, Royal Veterinary College, University of London, London, UK.
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20
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Appelberg S, Ahlén G, Yan J, Nikouyan N, Weber S, Larsson O, Höglund U, Aleman S, Weber F, Perlhamre E, Apro J, Gidlund E, Tuvesson O, Salati S, Cadossi M, Tegel H, Hober S, Frelin L, Mirazimi A, Sallberg M. A universal
SARS‐CoV DNA
vaccine inducing highly crossreactive neutralizing antibodies and T cells. EMBO Mol Med 2022; 14:e15821. [PMID: 35986481 PMCID: PMC9538582 DOI: 10.15252/emmm.202215821] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 08/16/2022] [Accepted: 08/17/2022] [Indexed: 11/20/2022] Open
Abstract
New variants in the SARS‐CoV‐2 pandemic are more contagious (Alpha/Delta), evade neutralizing antibodies (Beta), or both (Omicron). This poses a challenge in vaccine development according to WHO. We designed a more universal SARS‐CoV‐2 DNA vaccine containing receptor‐binding domain loops from the huCoV‐19/WH01, the Alpha, and the Beta variants, combined with the membrane and nucleoproteins. The vaccine induced spike antibodies crossreactive between huCoV‐19/WH01, Beta, and Delta spike proteins that neutralized huCoV‐19/WH01, Beta, Delta, and Omicron virus in vitro. The vaccine primed nucleoprotein‐specific T cells, unlike spike‐specific T cells, recognized Bat‐CoV sequences. The vaccine protected mice carrying the human ACE2 receptor against lethal infection with the SARS‐CoV‐2 Beta variant. Interestingly, priming of cross‐reactive nucleoprotein‐specific T cells alone was 60% protective, verifying observations from humans that T cells protect against lethal disease. This SARS‐CoV vaccine induces a uniquely broad and functional immunity that adds to currently used vaccines.
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Affiliation(s)
| | - Gustaf Ahlén
- Department of Laboratory Medicine, Karolinska Institutet Sweden
| | - Jingyi Yan
- Department of Laboratory Medicine, Karolinska Institutet Sweden
| | - Negin Nikouyan
- Department of Laboratory Medicine, Karolinska Institutet Sweden
| | | | | | | | - Soo Aleman
- Department of Infectious Disease Karolinska University Hospital and Department of Medicine Huddinge, Karolinska Institutet Sweden
| | - Friedemann Weber
- Institute for Virology FB10‐Veterinary Medicine, Justus‐Liebing University Giessen Germany
| | - Emma Perlhamre
- Karolinska Trial Alliance Karolinska University Hospital Sweden
| | - Johanna Apro
- Karolinska Trial Alliance Karolinska University Hospital Sweden
| | | | | | | | | | - Hanna Tegel
- Department of Protein Science Royal Institute of Technology Stockholm Sweden
| | - Sophia Hober
- Department of Protein Science Royal Institute of Technology Stockholm Sweden
| | - Lars Frelin
- Department of Laboratory Medicine, Karolinska Institutet Sweden
| | - Ali Mirazimi
- Public Health Agency of Sweden Solna Sweden
- Department of Laboratory Medicine, Karolinska Institutet Sweden
| | - Matti Sallberg
- Department of Laboratory Medicine, Karolinska Institutet Sweden
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21
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Parker E, Anderson C, Zeller M, Tibi A, Havens JL, Laroche G, Benlarbi M, Ariana A, Robles-Sikisaka R, Latif AA, Watts A, Awidi A, Jaradat SA, Gangavarapu K, Ramesh K, Kurzban E, Matteson NL, Han AX, Hughes LD, McGraw M, Spencer E, Nicholson L, Khan K, Suchard MA, Wertheim JO, Wohl S, Côté M, Abdelnour A, Andersen KG, Abu-Dayyeh I. Regional connectivity drove bidirectional transmission of SARS-CoV-2 in the Middle East during travel restrictions. Nat Commun 2022; 13:4784. [PMID: 35970983 PMCID: PMC9376901 DOI: 10.1038/s41467-022-32536-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 08/04/2022] [Indexed: 01/02/2023] Open
Abstract
Regional connectivity and land travel have been identified as important drivers of SARS-CoV-2 transmission. However, the generalizability of this finding is understudied outside of well-sampled, highly connected regions. In this study, we investigated the relative contributions of regional and intercontinental connectivity to the source-sink dynamics of SARS-CoV-2 for Jordan and the Middle East. By integrating genomic, epidemiological and travel data we show that the source of introductions into Jordan was dynamic across 2020, shifting from intercontinental seeding in the early pandemic to more regional seeding for the travel restrictions period. We show that land travel, particularly freight transport, drove introduction risk during the travel restrictions period. High regional connectivity and land travel also drove Jordan's export risk. Our findings emphasize regional connectedness and land travel as drivers of transmission in the Middle East.
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Affiliation(s)
- Edyth Parker
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA.
| | - Catelyn Anderson
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Mark Zeller
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Ahmad Tibi
- Biolab Diagnostic Laboratories, Amman, Jordan
| | - Jennifer L Havens
- Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, CA, USA
| | - Geneviève Laroche
- Department of Biochemistry, Microbiology and Immunology, and Center for Infection, Immunity, and Inflammation, University of Ottawa, Ottawa, ON, Canada
| | - Mehdi Benlarbi
- Department of Biochemistry, Microbiology and Immunology, and Center for Infection, Immunity, and Inflammation, University of Ottawa, Ottawa, ON, Canada
| | - Ardeshir Ariana
- Department of Biochemistry, Microbiology and Immunology, and Center for Infection, Immunity, and Inflammation, University of Ottawa, Ottawa, ON, Canada
| | - Refugio Robles-Sikisaka
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Alaa Abdel Latif
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | | | - Abdalla Awidi
- Cell Therapy Center, The University of Jordan, Amman, Jordan
- Thrombosis, haemostasis laboratory, School of Medicine, The University of Jordan, Amman, Jordan
| | - Saied A Jaradat
- Princess Haya Biotechnology Center, Jordan University of Science and Technology, Irbid, Jordan
| | - Karthik Gangavarapu
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Karthik Ramesh
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Ezra Kurzban
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Nathaniel L Matteson
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Alvin X Han
- Department of Medical Microbiology & Infection Prevention, Amsterdam UMC, University of Amsterdam, Amsterdam Institute for Infection and Immunity, Amsterdam, the Netherlands
| | - Laura D Hughes
- Department of Integrative, Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA
| | - Michelle McGraw
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Emily Spencer
- Scripps Research Translational Institute, La Jolla, CA, USA
| | | | | | - Marc A Suchard
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
| | - Joel O Wertheim
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Shirlee Wohl
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Marceline Côté
- Department of Biochemistry, Microbiology and Immunology, and Center for Infection, Immunity, and Inflammation, University of Ottawa, Ottawa, ON, Canada
| | | | - Kristian G Andersen
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Issa Abu-Dayyeh
- Biolab Diagnostic Laboratories, Amman, Jordan.
- Cell Therapy Center, The University of Jordan, Amman, Jordan.
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22
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Kumar S, Kumar GS, Maitra SS, Malý P, Bharadwaj S, Sharma P, Dwivedi VD. Viral informatics: bioinformatics-based solution for managing viral infections. Brief Bioinform 2022; 23:6659740. [PMID: 35947964 DOI: 10.1093/bib/bbac326] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 06/26/2022] [Accepted: 07/18/2022] [Indexed: 11/13/2022] Open
Abstract
Several new viral infections have emerged in the human population and establishing as global pandemics. With advancements in translation research, the scientific community has developed potential therapeutics to eradicate or control certain viral infections, such as smallpox and polio, responsible for billions of disabilities and deaths in the past. Unfortunately, some viral infections, such as dengue virus (DENV) and human immunodeficiency virus-1 (HIV-1), are still prevailing due to a lack of specific therapeutics, while new pathogenic viral strains or variants are emerging because of high genetic recombination or cross-species transmission. Consequently, to combat the emerging viral infections, bioinformatics-based potential strategies have been developed for viral characterization and developing new effective therapeutics for their eradication or management. This review attempts to provide a single platform for the available wide range of bioinformatics-based approaches, including bioinformatics methods for the identification and management of emerging or evolved viral strains, genome analysis concerning the pathogenicity and epidemiological analysis, computational methods for designing the viral therapeutics, and consolidated information in the form of databases against the known pathogenic viruses. This enriched review of the generally applicable viral informatics approaches aims to provide an overview of available resources capable of carrying out the desired task and may be utilized to expand additional strategies to improve the quality of translation viral informatics research.
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Affiliation(s)
- Sanjay Kumar
- School of Biotechnology, Jawaharlal Nehru University, New Delhi, India.,Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India
| | - Geethu S Kumar
- Department of Life Science, School of Basic Science and Research, Sharda University, Greater Noida, Uttar Pradesh, India.,Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India
| | | | - Petr Malý
- Laboratory of Ligand Engineering, Institute of Biotechnology of the Czech Academy of Sciences v.v.i., BIOCEV Research Center, Vestec, Czech Republic
| | - Shiv Bharadwaj
- Laboratory of Ligand Engineering, Institute of Biotechnology of the Czech Academy of Sciences v.v.i., BIOCEV Research Center, Vestec, Czech Republic
| | - Pradeep Sharma
- Department of Biophysics, All India Institute of Medical Sciences, New Delhi, India
| | - Vivek Dhar Dwivedi
- Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India.,Institute of Advanced Materials, IAAM, 59053 Ulrika, Sweden
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23
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Iddon C, Jones B, Sharpe P, Cevik M, Fitzgerald S. A population framework for predicting the proportion of people infected by the far-field airborne transmission of SARS-CoV-2 indoors. BUILDING AND ENVIRONMENT 2022; 221:109309. [PMID: 35757305 PMCID: PMC9212805 DOI: 10.1016/j.buildenv.2022.109309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 06/09/2022] [Accepted: 06/14/2022] [Indexed: 06/15/2023]
Abstract
The number of occupants in a space influences the risk of far-field airborne transmission of SARS-CoV-2 because the likelihood of having infectious and susceptible people both correlate with the number of occupants. This paper explores the relationship between occupancy and the probability of infection, and how this affects an individual person and a population of people. Mass-balance and dose-response models determine far-field transmission risks for an individual person and a population of people after sub-dividing a large reference space into 10 identical comparator spaces. For a single infected person, the dose received by an individual person in the comparator space is 10 times higher because the equivalent ventilation rate per infected person is lower when the per capita ventilation rate is preserved. However, accounting for population dispersion, such as the community prevalence of the virus, the probability of an infected person being present and uncertainty in their viral load, shows the transmission probability increases with occupancy and the reference space has a higher transmission risk. Also, far-field transmission is likely to be a rare event that requires a high emission rate, and there are a set of Goldilocks conditions that are just right when equivalent ventilation is effective at mitigating against transmission. These conditions depend on the viral load, because when they are very high or low, equivalent ventilation has little effect on transmission risk. Nevertheless, resilient buildings should deliver the equivalent ventilation rate required by standards as minimum.
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Affiliation(s)
- Christopher Iddon
- Department of Architecture and Built Environment, University of Nottingham, Nottingham, UK
| | - Benjamin Jones
- Department of Architecture and Built Environment, University of Nottingham, Nottingham, UK
| | - Patrick Sharpe
- Department of Architecture and Built Environment, University of Nottingham, Nottingham, UK
| | - Muge Cevik
- Department of Infection and Global Health, School of Medicine, University of St Andrews, St Andrews, UK
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24
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Goldenbogen B, Adler SO, Bodeit O, Wodke JAH, Escalera‐Fanjul X, Korman A, Krantz M, Bonn L, Morán‐Torres R, Haffner JEL, Karnetzki M, Maintz I, Mallis L, Prawitz H, Segelitz PS, Seeger M, Linding R, Klipp E. Control of COVID-19 Outbreaks under Stochastic Community Dynamics, Bimodality, or Limited Vaccination. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2200088. [PMID: 35607290 PMCID: PMC9348421 DOI: 10.1002/advs.202200088] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 03/24/2022] [Indexed: 06/15/2023]
Abstract
Reaching population immunity against COVID-19 is proving difficult even in countries with high vaccination levels. Thus, it is critical to identify limits of control and effective measures against future outbreaks. The effects of nonpharmaceutical interventions (NPIs) and vaccination strategies are analyzed with a detailed community-specific agent-based model (ABM). The authors demonstrate that the threshold for population immunity is not a unique number, but depends on the vaccination strategy. Prioritizing highly interactive people diminishes the risk for an infection wave, while prioritizing the elderly minimizes fatalities when vaccinations are low. Control over COVID-19 outbreaks requires adaptive combination of NPIs and targeted vaccination, exemplified for Germany for January-September 2021. Bimodality emerges from the heterogeneity and stochasticity of community-specific human-human interactions and infection networks, which can render the effects of limited NPIs uncertain. The authors' simulation platform can process and analyze dynamic COVID-19 epidemiological situations in diverse communities worldwide to predict pathways to population immunity even with limited vaccination.
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Affiliation(s)
- Björn Goldenbogen
- Theoretical BiophysicsHumboldt‐Universität zu BerlinInvalidenstr. 42Berlin10115Germany
| | - Stephan O. Adler
- Theoretical BiophysicsHumboldt‐Universität zu BerlinInvalidenstr. 42Berlin10115Germany
| | - Oliver Bodeit
- Theoretical BiophysicsHumboldt‐Universität zu BerlinInvalidenstr. 42Berlin10115Germany
- Institute of BiochemistryCharité – Universitätsmedizin BerlinVirchowweg 6Berlin10117Germany
- Institute of Quantitative and Theoretical BiologyHeinrich‐Heine‐UniversitätUniversitätsstraße 1Düsseldorf40225Germany
| | - Judith A. H. Wodke
- Theoretical BiophysicsHumboldt‐Universität zu BerlinInvalidenstr. 42Berlin10115Germany
| | | | - Aviv Korman
- Theoretical BiophysicsHumboldt‐Universität zu BerlinInvalidenstr. 42Berlin10115Germany
| | - Maria Krantz
- Theoretical BiophysicsHumboldt‐Universität zu BerlinInvalidenstr. 42Berlin10115Germany
| | - Lasse Bonn
- Theoretical BiophysicsHumboldt‐Universität zu BerlinInvalidenstr. 42Berlin10115Germany
| | - Rafael Morán‐Torres
- Theoretical BiophysicsHumboldt‐Universität zu BerlinInvalidenstr. 42Berlin10115Germany
| | - Johanna E. L. Haffner
- Theoretical BiophysicsHumboldt‐Universität zu BerlinInvalidenstr. 42Berlin10115Germany
| | - Maxim Karnetzki
- Theoretical BiophysicsHumboldt‐Universität zu BerlinInvalidenstr. 42Berlin10115Germany
| | - Ivo Maintz
- Theoretical BiophysicsHumboldt‐Universität zu BerlinInvalidenstr. 42Berlin10115Germany
| | - Lisa Mallis
- Theoretical BiophysicsHumboldt‐Universität zu BerlinInvalidenstr. 42Berlin10115Germany
| | - Hannah Prawitz
- Theoretical BiophysicsHumboldt‐Universität zu BerlinInvalidenstr. 42Berlin10115Germany
| | - Patrick S. Segelitz
- Theoretical BiophysicsHumboldt‐Universität zu BerlinInvalidenstr. 42Berlin10115Germany
| | - Martin Seeger
- Theoretical BiophysicsHumboldt‐Universität zu BerlinInvalidenstr. 42Berlin10115Germany
- Rewire TxHumboldt‐Universität zu BerlinInvalidenstr. 42Berlin10115Germany
| | - Rune Linding
- Theoretical BiophysicsHumboldt‐Universität zu BerlinInvalidenstr. 42Berlin10115Germany
- Rewire TxHumboldt‐Universität zu BerlinInvalidenstr. 42Berlin10115Germany
| | - Edda Klipp
- Theoretical BiophysicsHumboldt‐Universität zu BerlinInvalidenstr. 42Berlin10115Germany
- Rewire TxHumboldt‐Universität zu BerlinInvalidenstr. 42Berlin10115Germany
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25
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Song J, Cha B, Moon J, Jang H, Kim S, Jang J, Yong D, Kwon HJ, Lee IC, Lim EK, Jung J, Park HG, Kang T. Smartphone-Based SARS-CoV-2 and Variants Detection System using Colorimetric DNAzyme Reaction Triggered by Loop-Mediated Isothermal Amplification (LAMP) with Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR). ACS NANO 2022; 16:11300-11314. [PMID: 35735410 PMCID: PMC9236205 DOI: 10.1021/acsnano.2c04840] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Coronavirus disease (COVID-19) has affected people for over two years. Moreover, the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants has raised concerns regarding its accurate diagnosis. Here, we report a colorimetric DNAzyme reaction triggered by loop-mediated isothermal amplification (LAMP) with clustered regularly interspaced short palindromic repeats (CRISPR), referred to as DAMPR assay for detecting SARS-CoV-2 and variants genes with attomolar sensitivity within an hour. The CRISPR-associated protein 9 (Cas9) system eliminated false-positive signals of LAMP products, improving the accuracy of DAMPR assay. Further, we fabricated a portable DAMPR assay system using a three-dimensional printing technique and developed a machine learning (ML)-based smartphone application to routinely check diagnostic results of SARS-CoV-2 and variants. Among blind tests of 136 clinical samples, the proposed system successfully diagnosed COVID-19 patients with a clinical sensitivity and specificity of 100% each. More importantly, the D614G (variant-common), T478K (delta-specific), and A67V (omicron-specific) mutations of the SARS-CoV-2 S gene were detected selectively, enabling the diagnosis of 70 SARS-CoV-2 delta or omicron variant patients. The DAMPR assay system is expected to be employed for on-site, rapid, accurate detection of SARS-CoV-2 and its variants gene and employed in the diagnosis of various infectious diseases.
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Affiliation(s)
- Jayeon Song
- Bionanotechnology
Research Center, Korea Research Institute
of Bioscience and Biotechnology (KRIBB), 125 Gwahak-ro, Yuseong-gu,
Daejeon 34141, Republic
of Korea
| | - Baekdong Cha
- School
of Integrated Technology, Gwangju Institute
of Science and Technology (GIST), 123 Cheomdangwagi-ro, Buk-gu,
Gwangju 61005, Republic
of Korea
| | - Jeong Moon
- Bionanotechnology
Research Center, Korea Research Institute
of Bioscience and Biotechnology (KRIBB), 125 Gwahak-ro, Yuseong-gu,
Daejeon 34141, Republic
of Korea
- Department
of Chemical and Biomolecular Engineering (BK21+ Program), Korea Advanced Institute of Science and Technology
(KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Hyowon Jang
- Bionanotechnology
Research Center, Korea Research Institute
of Bioscience and Biotechnology (KRIBB), 125 Gwahak-ro, Yuseong-gu,
Daejeon 34141, Republic
of Korea
| | - Sunjoo Kim
- Department
of Laboratory Medicine, Gyeongsang National
University College of Medicine, 79 Gangnam-ro, Jinju-si, Gyeongsangnam-do 52727, Republic of Korea
- Gyeongnam
Center for Disease Control and Prevention, 300 Jungang-daero, Uichang-gu,
Changwon-si, Gyeongsangnamdo 51154, Republic of Korea
| | - Jieun Jang
- Gyeongnam
Center for Disease Control and Prevention, 300 Jungang-daero, Uichang-gu,
Changwon-si, Gyeongsangnamdo 51154, Republic of Korea
| | - Dongeun Yong
- Department
of Laboratory Medicine and Research Institute of Bacterial Resistance, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
| | - Hyung-Jun Kwon
- Functional
Biomaterial Research Center, KRIBB, 181 Ipsin-gil, Jeongeup-si, Jeollabuk-do 56212, Republic of Korea
| | - In-Chul Lee
- Functional
Biomaterial Research Center, KRIBB, 181 Ipsin-gil, Jeongeup-si, Jeollabuk-do 56212, Republic of Korea
| | - Eun-Kyung Lim
- Bionanotechnology
Research Center, Korea Research Institute
of Bioscience and Biotechnology (KRIBB), 125 Gwahak-ro, Yuseong-gu,
Daejeon 34141, Republic
of Korea
- Department
of Nanobiotechnology, KRIBB School of Biotechnology, University of Science and Technology (UST), 217 Gajeong-ro, Yuseong-gu,
Daejeon 34113, Republic
of Korea
| | - Juyeon Jung
- Bionanotechnology
Research Center, Korea Research Institute
of Bioscience and Biotechnology (KRIBB), 125 Gwahak-ro, Yuseong-gu,
Daejeon 34141, Republic
of Korea
| | - Hyun Gyu Park
- Department
of Chemical and Biomolecular Engineering (BK21+ Program), Korea Advanced Institute of Science and Technology
(KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Taejoon Kang
- Bionanotechnology
Research Center, Korea Research Institute
of Bioscience and Biotechnology (KRIBB), 125 Gwahak-ro, Yuseong-gu,
Daejeon 34141, Republic
of Korea
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26
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Du Z, Wang C, Liu C, Bai Y, Pei S, Adam DC, Wang L, Wu P, Lau EHY, Cowling BJ. Systematic review and meta-analyses of superspreading of SARS-CoV-2 infections. Transbound Emerg Dis 2022; 69:e3007-e3014. [PMID: 35799321 PMCID: PMC9349569 DOI: 10.1111/tbed.14655] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 07/03/2022] [Accepted: 07/04/2022] [Indexed: 11/29/2022]
Abstract
Superspreading, or overdispersion in transmission, is a feature of SARS-CoV-2 transmission which results in surging epidemics and large clusters of infection. The dispersion parameter is a statistical parameter used to characterize and quantify heterogeneity. In the context of measuring transmissibility, it is analogous to measures of superspreading potential among populations by assuming that collective offspring distribution follows a negative-binomial distribution. We conducted a systematic review and meta-analysis on globally reported dispersion parameters of SARS-CoV-2 infection. All searches were carried out on 10 September 2021 in PubMed for articles published from 1 January 2020 to 10 September 2021. Multiple estimates of the dispersion parameter have been published for 17 studies, which could be related to where and when the data was obtained, in 8 countries (e.g., China, USA, India, Indonesia, Israel, Japan, New Zealand, and Singapore). High heterogeneity was reported among the included studies. The mean estimates of dispersion parameters range from 0.06 to 2.97 over eight countries, the pooled estimate was 0.55 (95% CI: 0.30, 0.79), with changing means over countries and decreasing slightly with the increasing reproduction number. The expected proportion of cases accounting for 80% of all transmissions is 19% (95% CrI: 7, 34) globally. The study location and method were found to be important drivers for diversity in estimates of dispersion parameters. While under high potential of superspreading, larger outbreaks could still occur with the import of the COVID-19 virus by traveling even when an epidemic seems to be under control. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Zhanwei Du
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.,Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - Chunyu Wang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.,Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - Caifen Liu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.,Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - Yuan Bai
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.,Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, 10032, USA
| | - Dillon C Adam
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Lin Wang
- Department of Genetics, University of Cambridge, Cambridge, CB2 3EH, UK
| | - Peng Wu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.,Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - Eric H Y Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.,Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.,Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
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27
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Nielsen BF, Eilersen A, Simonsen L, Sneppen K. Lockdowns exert selection pressure on overdispersion of SARS-CoV-2 variants. Epidemics 2022; 40:100613. [PMID: 35939969 PMCID: PMC9338171 DOI: 10.1016/j.epidem.2022.100613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 06/23/2022] [Accepted: 07/20/2022] [Indexed: 11/22/2022] Open
Abstract
The SARS-CoV-2 ancestral strain has caused pronounced superspreading events, reflecting a disease characterized by overdispersion, where about 10% of infected people cause 80% of infections. New variants of the disease have different person-to-person variability in viral load, suggesting for example that the Alpha (B.1.1.7) variant is more infectious but relatively less prone to superspreading. Meanwhile, non-pharmaceutical mitigation of the pandemic has focused on limiting social contacts (lockdowns, regulations on gatherings) and decreasing transmission risk through mask wearing and social distancing. Using a mathematical model, we show that the competitive advantage of disease variants may heavily depend on the restrictions imposed. In particular, we find that lockdowns exert an evolutionary pressure which favours variants with lower levels of overdispersion. Our results suggest that overdispersion is an evolutionarily unstable trait, with a tendency for more homogeneously spreading variants to eventually dominate. Novel variants of SARS-CoV-2 appear to be less prone to superspreading. A new model shows that it is advantageous for the pathogen to spread homogeneously. Interventions exert a selective pressure towards developing homogeneous transmission. The results have implications for the assessment of novel variants. Adds to understanding of how behaviour and interventions shape pathogen evolution.
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28
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de Oliveira FF, Dias LA, Fernandes MAC. Proposal of Smith-Waterman algorithm on FPGA to accelerate the forward and backtracking steps. PLoS One 2022; 17:e0254736. [PMID: 35772072 PMCID: PMC9246398 DOI: 10.1371/journal.pone.0254736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Accepted: 06/11/2022] [Indexed: 11/19/2022] Open
Abstract
In bioinformatics, alignment is an essential technique for finding similarities between biological sequences. Usually, the alignment is performed with the Smith-Waterman (SW) algorithm, a well-known sequence alignment technique of high-level precision based on dynamic programming. However, given the massive data volume in biological databases and their continuous exponential increase, high-speed data processing is necessary. Therefore, this work proposes a parallel hardware design for the SW algorithm with a systolic array structure to accelerate the forward and backtracking steps. For this purpose, the architecture calculates and stores the paths in the forward stage for pre-organizing the alignment, which reduces the complexity of the backtracking stage. The backtracking starts from the maximum score position in the matrix and generates the optimal SW sequence alignment path. The architecture was validated on Field-Programmable Gate Array (FPGA), and synthesis analyses have shown that the proposed design reaches up to 79.5 Giga Cell Updates per Second (GCPUS).
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Affiliation(s)
- Fabio F. de Oliveira
- Laboratory of Machine Learning and Intelligent Instrumentation, nPITI/IMD, Federal University of Rio Grande do Norte, Natal, Brazil
- Bioinformatics Multidisciplinary Environment (BioME), Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil
| | - Leonardo A. Dias
- Centre for Cyber Security and Privacy, School of Computer Science, University of Birmingham, Birmingham, United Kingdom
| | - Marcelo A. C. Fernandes
- Laboratory of Machine Learning and Intelligent Instrumentation, nPITI/IMD, Federal University of Rio Grande do Norte, Natal, Brazil
- Department of Computer and Automation Engineering, Federal University of Rio Grande do Norte, Natal, Brazil
- Bioinformatics Multidisciplinary Environment (BioME), Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil
- * E-mail:
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29
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Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) delta variant transmits much more rapidly than prior SARS-CoV-2 viruses. The primary mode of transmission is via short range aerosols that are emitted from the respiratory tract of an index case. There is marked heterogeneity in the spread of this virus, with 10% to 20% of index cases contributing to 80% of secondary cases, while most index cases have no subsequent transmissions. Vaccination, ventilation, masking, eye protection, and rapid case identification with contact tracing and isolation can all decrease the transmission of this virus.
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Affiliation(s)
- Eric A Meyerowitz
- Montefiore Medical Center, 111 East 210th Street, Bronx, NY 10467, USA.
| | - Aaron Richterman
- Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA
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30
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Saravanan KA, Panigrahi M, Kumar H, Rajawat D, Nayak SS, Bhushan B, Dutt T. Role of genomics in combating COVID-19 pandemic. Gene 2022; 823:146387. [PMID: 35248659 PMCID: PMC8894692 DOI: 10.1016/j.gene.2022.146387] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 02/17/2022] [Accepted: 02/28/2022] [Indexed: 12/20/2022]
Abstract
The coronavirus disease 2019 (COVID-19) quickly swept over the world, becoming one of the most devastating outbreaks in human history. Being the first pandemic in the post-genomic era, advancements in genomics contributed significantly to scientific understanding and public health response to COVID-19. Genomic technologies have been employed by researchers all over the world to better understand the biology of SARS-CoV-2 and its origin, genomic diversity, and evolution. Worldwide genomic resources have greatly aided in the investigation of the COVID-19 pandemic. The pandemic has ushered in a new era of genomic surveillance, wherein scientists are tracking the changes of the SARS-CoV-2 genome in real-time at the international and national levels. Availability of genomic and proteomic information enables the rapid development of molecular diagnostics and therapeutics. The advent of high-throughput sequencing and genome editing technologies led to the development of modern vaccines. We briefly discuss the impact of genomics in the ongoing COVID-19 pandemic in this review.
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Affiliation(s)
- K A Saravanan
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Manjit Panigrahi
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India.
| | - Harshit Kumar
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Divya Rajawat
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Sonali Sonejita Nayak
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Bharat Bhushan
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Triveni Dutt
- Livestock Production and Management Section, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
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31
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Alkhamis MA, Fountain-Jones NM, Khajah MM, Alghounaim M, Al-Sabah SK. Comparative Phylodynamics Reveals the Evolutionary History of SARS-CoV-2 Emerging Variants in the Arabian Peninsula. Virus Evol 2022; 8:veac040. [PMID: 35677574 PMCID: PMC9129158 DOI: 10.1093/ve/veac040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 05/12/2022] [Accepted: 05/18/2022] [Indexed: 11/18/2022] Open
Abstract
Emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants continue to be responsible for an unprecedented worldwide public health and economic catastrophe. Accurate understanding and comparison of global and regional evolutionary epidemiology of novel SARS-CoV-2 variants are critical to guide current and future interventions. Here, we utilized a Bayesian phylodynamic pipeline to trace and compare the evolutionary dynamics, spatiotemporal origins, and spread of five variants (Alpha, Beta, Delta, Kappa, and Eta) across the Arabian Peninsula. We found variant-specific signatures of evolution and spread that are likely linked to air travel and disease control interventions in the region. Alpha, Beta, and Delta variants went through sequential periods of growth and decline, whereas we inferred inconclusive population growth patterns for the Kappa and Eta variants due to their sporadic introductions in the region. Non-pharmaceutical interventions imposed between mid-2020 and early 2021 likely played a role in reducing the epidemic progression of the Beta and the Alpha variants. In comparison, the combination of the non-pharmaceutical interventions and the rapid rollout of vaccination might have shaped Delta variant dynamics. We found that the Alpha and Beta variants were frequently introduced into the Arab peninsula between mid-2020 and early 2021 from Europe and Africa, respectively, whereas the Delta variant was frequently introduced between early 2021 and mid-2021 from East Asia. For these three variants, we also revealed significant and intense dispersal routes between the Arab region and Africa, Europe, Asia, and Oceania. In contrast, the restricted spread and stable effective population size of the Kappa and the Eta variants suggest that they no longer need to be targeted in genomic surveillance activities in the region. In contrast, the evolutionary characteristics of the Alpha, Beta, and Delta variants confirm the dominance of these variants in the recent outbreaks. Our study highlights the urgent need to establish regional molecular surveillance programs to ensure effective decision making related to the allocation of intervention activities targeted toward the most relevant variants.
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Affiliation(s)
- Moh A Alkhamis
- Department of Epidemiology and Biostatistics, Faculty of Public Health, Health Sciences Centre, Kuwait University, Kuwait City, Kuwait
| | | | - Mohammad M Khajah
- Systems and Software development Department, Kuwait Institute for Scientific Research, Kuwait
| | - Mohammad Alghounaim
- Departement of pediatrics, Amiri Hospital, Ministry of Health, Kuwait
- Jaber Al-Ahmad Al-Sabah Hospital, Ministry of Health, Kuwait
| | - Salman K Al-Sabah
- Jaber Al-Ahmad Al-Sabah Hospital, Ministry of Health, Kuwait
- Department of Surgery, Faculty of Medicine, Health Sciences Center, Kuwait University, Kuwait
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32
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Berrig C, Andreasen V, Frost Nielsen B. Heterogeneity in testing for infectious diseases. ROYAL SOCIETY OPEN SCIENCE 2022; 9:220129. [PMID: 35600424 PMCID: PMC9114977 DOI: 10.1098/rsos.220129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 04/28/2022] [Indexed: 05/03/2023]
Abstract
Testing strategies have varied widely between nation states during the COVID-19 pandemic, in intensity as well as methodology. Some countries have mainly performed diagnostic testing while others have opted for mass-screening for the presence of SARS-CoV-2 as well. COVID passport solutions have been introduced, in which access to several aspects of public life requires either testing, proof of vaccination or a combination thereof. This creates a coupling between personal activity levels and testing behaviour which, as we show in a mathematical model, leverages heterogeneous behaviours in a population and turns this heterogeneity from a disadvantage to an advantage for epidemic control.
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Affiliation(s)
- Christian Berrig
- Department of Science and Environment, Roskilde University, Universitetsvej 1, 4000 Roskilde, Denmark
| | - Viggo Andreasen
- Department of Science and Environment, Roskilde University, Universitetsvej 1, 4000 Roskilde, Denmark
| | - Bjarke Frost Nielsen
- Department of Science and Environment, Roskilde University, Universitetsvej 1, 4000 Roskilde, Denmark
- Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100 Copenhagen, Denmark
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33
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Cappello L, Kim J, Liu S, Palacios JA. Statistical Challenges in Tracking the Evolution of SARS-CoV-2. Stat Sci 2022; 37:162-182. [PMID: 36034090 PMCID: PMC9409356 DOI: 10.1214/22-sts853] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Genomic surveillance of SARS-CoV-2 has been instrumental in tracking the spread and evolution of the virus during the pandemic. The availability of SARS-CoV-2 molecular sequences isolated from infected individuals, coupled with phylodynamic methods, have provided insights into the origin of the virus, its evolutionary rate, the timing of introductions, the patterns of transmission, and the rise of novel variants that have spread through populations. Despite enormous global efforts of governments, laboratories, and researchers to collect and sequence molecular data, many challenges remain in analyzing and interpreting the data collected. Here, we describe the models and methods currently used to monitor the spread of SARS-CoV-2, discuss long-standing and new statistical challenges, and propose a method for tracking the rise of novel variants during the epidemic.
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Affiliation(s)
- Lorenzo Cappello
- Lorenzo Cappello is Assistant Professor, Departments of Economics and Business, Universitat Pompeu Fabra, 08005, Spain
| | - Jaehee Kim
- Jaehee Kim is Assistant Professor, Department of Computational Biology, Cornell University, Ithaca, New York 14853, USA
| | - Sifan Liu
- Sifan Liu is a Ph.D. student, Department of Statistics, Stanford University, Stanford, California 94305, USA
| | - Julia A. Palacios
- Julia A. Palacios is Assistant Professor, Departments of Statistics and Biomedical Data Sciences, Stanford University, Stanford, California 94305, USA
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34
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Chen C, Nadeau S, Topolsky I, Beerenwinkel N, Stadler T. Advancing genomic epidemiology by addressing the bioinformatics bottleneck: Challenges, design principles, and a Swiss example. Epidemics 2022; 39:100576. [PMID: 35605437 PMCID: PMC9107180 DOI: 10.1016/j.epidem.2022.100576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 04/05/2022] [Accepted: 05/05/2022] [Indexed: 11/28/2022] Open
Abstract
The SARS-CoV-2 pandemic led to a huge increase in global pathogen genome sequencing efforts, and the resulting data are becoming increasingly important to detect variants of concern, monitor outbreaks, and quantify transmission dynamics. However, this rapid up-scaling in data generation brought with it many IT infrastructure challenges. In this paper, we report about developing an improved system for genomic epidemiology. We (i) highlight key challenges that were exacerbated by the pandemic situation, (ii) provide data infrastructure design principles to address them, and (iii) give an implementation example developed by the Swiss SARS-CoV-2 Sequencing Consortium (S3C) in response to the COVID-19 pandemic. Finally, we discuss remaining challenges to data infrastructure for genomic epidemiology. Improving these infrastructures will help better detect, monitor, and respond to future public health threats.
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Affiliation(s)
- Chaoran Chen
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, CH 4058, Switzerland; Swiss Institute of Bioinformatics, Lausanne, CH 1015, Switzerland
| | - Sarah Nadeau
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, CH 4058, Switzerland; Swiss Institute of Bioinformatics, Lausanne, CH 1015, Switzerland
| | - Ivan Topolsky
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, CH 4058, Switzerland; Swiss Institute of Bioinformatics, Lausanne, CH 1015, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, CH 4058, Switzerland; Swiss Institute of Bioinformatics, Lausanne, CH 1015, Switzerland
| | - Tanja Stadler
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, CH 4058, Switzerland; Swiss Institute of Bioinformatics, Lausanne, CH 1015, Switzerland.
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35
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Glatman-Freedman A, Gur-Arie L, Sefty H, Kaufman Z, Bromberg M, Dichtiar R, Rosenberg A, Pando R, Nemet I, Kliker L, Mendelson E, Keinan-Boker L, Zuckerman NS, Mandelboim M. The impact of SARS-CoV-2 on respiratory syndromic and sentinel surveillance in Israel, 2020: a new perspective on established systems. EURO SURVEILLANCE : BULLETIN EUROPEEN SUR LES MALADIES TRANSMISSIBLES = EUROPEAN COMMUNICABLE DISEASE BULLETIN 2022; 27. [PMID: 35451365 PMCID: PMC9027148 DOI: 10.2807/1560-7917.es.2022.27.16.2100457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Background The COVID-19 pandemic presented new challenges for the existing respiratory surveillance systems, and adaptations were implemented. Systematic assessment of the syndromic and sentinel surveillance platforms during the pandemic is essential for understanding the value of each platform in the context of an emerging pathogen with rapid global spread. Aim We aimed to evaluate systematically the performance of various respiratory syndromic surveillance platforms and the sentinel surveillance system in Israel from 1 January to 31 December 2020. Methods We compared the 2020 syndromic surveillance trends to those of the previous 3 years, using Poisson regression adjusted for overdispersion. To assess the performance of the sentinel clinic system as compared with the national SARS-CoV-2 repository, a cubic spline with 7 knots and 95% confidence intervals were applied to the sentinel network's weekly percentage of positive SARS-CoV-2 cases. Results Syndromic surveillance trends changed substantially during 2020, with a statistically significant reduction in the rates of visits to physicians and emergency departments to below previous years' levels. Morbidity patterns of the syndromic surveillance platforms were inconsistent with the progress of the pandemic, while the sentinel surveillance platform was found to reflect the national circulation of SARS-CoV-2 in the population. Conclusion Our findings reveal the robustness of the sentinel clinics platform for the surveillance of the main respiratory viruses during the pandemic and possibly beyond. The robustness of the sentinel clinics platform during 2020 supports its use in locations with insufficient resources for widespread testing of respiratory viruses.
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Affiliation(s)
- Aharona Glatman-Freedman
- The Israel Center for Disease Control, Israel Ministry of Health, Tel Hashomer, Ramat Gan, Israel.,Department of Epidemiology and Preventive Medicine, School of Public Health, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Lea Gur-Arie
- The Israel Center for Disease Control, Israel Ministry of Health, Tel Hashomer, Ramat Gan, Israel
| | - Hanna Sefty
- The Israel Center for Disease Control, Israel Ministry of Health, Tel Hashomer, Ramat Gan, Israel
| | - Zalman Kaufman
- The Israel Center for Disease Control, Israel Ministry of Health, Tel Hashomer, Ramat Gan, Israel
| | - Michal Bromberg
- The Israel Center for Disease Control, Israel Ministry of Health, Tel Hashomer, Ramat Gan, Israel.,Department of Epidemiology and Preventive Medicine, School of Public Health, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Rita Dichtiar
- The Israel Center for Disease Control, Israel Ministry of Health, Tel Hashomer, Ramat Gan, Israel
| | - Alina Rosenberg
- The Israel Center for Disease Control, Israel Ministry of Health, Tel Hashomer, Ramat Gan, Israel
| | - Rakefet Pando
- The Israel Center for Disease Control, Israel Ministry of Health, Tel Hashomer, Ramat Gan, Israel.,The Central Virology Laboratory, Israel Ministry of Health, Tel Hashomer, Ramat Gan, Israel
| | - Ital Nemet
- The Central Virology Laboratory, Israel Ministry of Health, Tel Hashomer, Ramat Gan, Israel
| | - Limor Kliker
- The Central Virology Laboratory, Israel Ministry of Health, Tel Hashomer, Ramat Gan, Israel
| | - Ella Mendelson
- Department of Epidemiology and Preventive Medicine, School of Public Health, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,The Central Virology Laboratory, Israel Ministry of Health, Tel Hashomer, Ramat Gan, Israel
| | - Lital Keinan-Boker
- The Israel Center for Disease Control, Israel Ministry of Health, Tel Hashomer, Ramat Gan, Israel.,School of Public Health, University of Haifa, Israel
| | - Neta S Zuckerman
- The Central Virology Laboratory, Israel Ministry of Health, Tel Hashomer, Ramat Gan, Israel
| | - Michal Mandelboim
- Department of Epidemiology and Preventive Medicine, School of Public Health, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,The Central Virology Laboratory, Israel Ministry of Health, Tel Hashomer, Ramat Gan, Israel
| | -
- The Israeli Respiratory Viruses Surveillance Network (IRVSN) members are listed under Acknowledgements
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36
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Galaxy workflows for fragment-based virtual screening: a case study on the SARS-CoV-2 main protease. J Cheminform 2022; 14:22. [PMID: 35414112 PMCID: PMC9003163 DOI: 10.1186/s13321-022-00588-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 02/09/2022] [Indexed: 12/03/2022] Open
Abstract
We present several workflows for protein-ligand docking and free energy calculation for use in the workflow management system Galaxy. The workflows are composed of several widely used open-source tools, including rDock and GROMACS, and can be executed on public infrastructure using either Galaxy’s graphical interface or the command line. We demonstrate the utility of the workflows by running a high-throughput virtual screening of around 50000 compounds against the SARS-CoV-2 main protease, a system which has been the subject of intense study in the last year.
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37
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Abstract
We have come a long way since the start of the COVID-19 pandemic-from hoarding toilet paper and wiping down groceries to sending our children back to school and vaccinating billions. Over this period, the global community of epidemiologists and evolutionary biologists has also come a long way in understanding the complex and changing dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19. In this Review, we retrace our steps through the questions that this community faced as the pandemic unfolded. We focus on the key roles that mathematical modeling and quantitative analyses of empirical data have played in allowing us to address these questions and ultimately to better understand and control the pandemic.
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Affiliation(s)
- Katia Koelle
- Department of Biology, O. Wayne Rollins Research Center, Emory University, Atlanta, GA 30322, USA
| | - Michael A. Martin
- Department of Biology, O. Wayne Rollins Research Center, Emory University, Atlanta, GA 30322, USA
- Graduate Program in Population Biology, Ecology, and Evolution, Emory University, Atlanta, GA 30322, USA
| | - Rustom Antia
- Department of Biology, O. Wayne Rollins Research Center, Emory University, Atlanta, GA 30322, USA
| | - Ben Lopman
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Natalie E. Dean
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
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38
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Castañeda-Mogollón D, Kamaliddin C, Fine L, Oberding LK, Pillai DR. SARS-CoV-2 variant detection with ADSSpike. Diagn Microbiol Infect Dis 2022; 102:115606. [PMID: 34963097 PMCID: PMC8608664 DOI: 10.1016/j.diagmicrobio.2021.115606] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 11/06/2021] [Accepted: 11/16/2021] [Indexed: 11/29/2022]
Abstract
The SARS-CoV-2 coronavirus pandemic has been an unprecedented challenge to global pandemic response and preparedness. With the continuous appearance of new SARS-CoV-2 variants, it is imperative to implement tools for genomic surveillance and diagnosis in order to decrease viral transmission and prevalence. The ADSSpike workflow was developed with the goal of identifying signature SNPs from the S gene associated with SARS-CoV-2 variants through amplicon deep sequencing. Seventy-two samples were sequenced, and 30 mutations were identified. Among those, signature SNPs were linked to 2 Zeta-VOI (P.2) samples and one to the Alpha-VOC (B.1.17). An average depth of 700 reads was found to properlycorrectly identify all SNPs and deletions pertinent to SARS-CoV-2 mutants. ADSSpike is the first workflow to provide a practical, cost-effective, and scalable solution to diagnose SARS-CoV-2 VOC/VOI in the clinical laboratory, adding a valuable tool to public health measures to fight the COVID-19 pandemic for approximately $41.85 USD/reaction.
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39
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Katona P, Kullar R, Zhang K. Bringing Transmission of SARS-CoV-2 to the Surface: Is there a Role for Fomites? Clin Infect Dis 2022; 75:910-916. [PMID: 35218181 PMCID: PMC8903442 DOI: 10.1093/cid/ciac157] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Indexed: 01/22/2023] Open
Abstract
Understanding the contribution of routes of transmission, particularly the role of fomites in Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) transmission is important in developing and implementing successful public health infection prevention and control measures.This article will look at case reports, laboratory findings, animal studies, environmental factors, the need for disinfection, and differences in settings, as they relate to SARS-CoV-2 transmission.
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Affiliation(s)
- Peter Katona
- UCLA School of Medicine Dept. of Infectious Diseases and UCLA School of Public Health
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40
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Babiker A, Martin MA, Marvil C, Bellman S, Petit III RA, Bradley HL, Stittleburg VD, Ingersoll J, Kraft CS, Li Y, Zhang J, Paden CR, Read TD, Waggoner JJ, Koelle K, Piantadosi A. Unrecognized introductions of SARS-CoV-2 into the US state of Georgia shaped the early epidemic. Virus Evol 2022; 8:veac011. [PMID: 35317348 PMCID: PMC8933693 DOI: 10.1093/ve/veac011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 01/15/2022] [Accepted: 02/14/2022] [Indexed: 11/24/2022] Open
Abstract
In early 2020, as diagnostic and surveillance responses for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ramped up, attention focused primarily on returning international travelers. Here, we build on existing studies characterizing early patterns of SARS-CoV-2 spread within the USA by analyzing detailed clinical, molecular, and viral genomic data from the state of Georgia through March 2020. We find evidence for multiple early introductions into Georgia, despite relatively sparse sampling. Most sampled sequences likely stemmed from a single or small number of introductions from Asia three weeks prior to the state's first detected infection. Our analysis of sequences from domestic travelers demonstrates widespread circulation of closely related viruses in multiple US states by the end of March 2020. Our findings indicate that the exclusive focus on identifying SARS-CoV-2 in returning international travelers early in the pandemic may have led to a failure to recognize locally circulating infections for several weeks and point toward a critical need for implementing rapid, broadly targeted surveillance efforts for future pandemics.
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Affiliation(s)
- Ahmed Babiker
- Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, 201 Dowman Drive, Atlanta, GA 30322, USA
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, 201 Dowman Drive, Atlanta, GA 30322, USA
| | - Michael A Martin
- Department of Biology, Emory University, 201 Dowman Drive, Atlanta, GA 30322, USA
- Population Biology, Ecology, and Evolution Graduate Program, Laney Graduate School, Emory University, 201 Dowman Drive, Atlanta, GA 30322, USA
| | - Charles Marvil
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, 201 Dowman Drive, Atlanta, GA 30322, USA
| | - Stephanie Bellman
- Environmental Health Sciences PhD Program, Laney Graduate School, Emory University, 201 Dowman Drive, Atlanta, GA 30322, USA
| | - Robert A Petit III
- Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, 201 Dowman Drive, Atlanta, GA 30322, USA
| | - Heath L Bradley
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, 201 Dowman Drive, Atlanta, GA 30322, USA
| | - Victoria D Stittleburg
- Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, 201 Dowman Drive, Atlanta, GA 30322, USA
| | - Jessica Ingersoll
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, 201 Dowman Drive, Atlanta, GA 30322, USA
| | - Colleen S Kraft
- Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, 201 Dowman Drive, Atlanta, GA 30322, USA
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, 201 Dowman Drive, Atlanta, GA 30322, USA
| | - Yan Li
- Division of Viral Diseases, Centers for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA 30333, USA
| | - Jing Zhang
- Division of Viral Diseases, Centers for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA 30333, USA
| | - Clinton R Paden
- Division of Viral Diseases, Centers for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA 30333, USA
| | - Timothy D Read
- Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, 201 Dowman Drive, Atlanta, GA 30322, USA
| | - Jesse J Waggoner
- Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, 201 Dowman Drive, Atlanta, GA 30322, USA
| | - Katia Koelle
- Department of Biology, Emory University, 201 Dowman Drive, Atlanta, GA 30322, USA
| | - Anne Piantadosi
- Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, 201 Dowman Drive, Atlanta, GA 30322, USA
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, 201 Dowman Drive, Atlanta, GA 30322, USA
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41
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Pathak AK, Mishra GP, Uppili B, Walia S, Fatihi S, Abbas T, Banu S, Ghosh A, Kanampalliwar A, Jha A, Fatma S, Aggarwal S, Dhar MS, Marwal R, Radhakrishnan VS, Ponnusamy K, Kabra S, Rakshit P, Bhoyar RC, Jain A, Divakar MK, Imran M, Faruq M, Sowpati DT, Thukral L, Raghav SK, Mukerji M. Spatio-temporal dynamics of intra-host variability in SARS-CoV-2 genomes. Nucleic Acids Res 2022; 50:1551-1561. [PMID: 35048970 PMCID: PMC8860616 DOI: 10.1093/nar/gkab1297] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 12/09/2021] [Accepted: 01/13/2022] [Indexed: 12/13/2022] Open
Abstract
During the course of the COVID-19 pandemic, large-scale genome sequencing of SARS-CoV-2 has been useful in tracking its spread and in identifying variants of concern (VOC). Viral and host factors could contribute to variability within a host that can be captured in next-generation sequencing reads as intra-host single nucleotide variations (iSNVs). Analysing 1347 samples collected till June 2020, we recorded 16 410 iSNV sites throughout the SARS-CoV-2 genome. We found ∼42% of the iSNV sites to be reported as SNVs by 30 September 2020 in consensus sequences submitted to GISAID, which increased to ∼80% by 30th June 2021. Following this, analysis of another set of 1774 samples sequenced in India between November 2020 and May 2021 revealed that majority of the Delta (B.1.617.2) and Kappa (B.1.617.1) lineage-defining variations appeared as iSNVs before getting fixed in the population. Besides, mutations in RdRp as well as RNA-editing by APOBEC and ADAR deaminases seem to contribute to the differential prevalence of iSNVs in hosts. We also observe hyper-variability at functionally critical residues in Spike protein that could alter the antigenicity and may contribute to immune escape. Thus, tracking and functional annotation of iSNVs in ongoing genome surveillance programs could be important for early identification of potential variants of concern and actionable interventions.
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Affiliation(s)
- Ankit K Pathak
- CSIR - Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | | | - Bharathram Uppili
- CSIR - Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Safal Walia
- Institute of Life Sciences (ILS), Bhubaneswar, Odisha, India
| | - Saman Fatihi
- CSIR - Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Tahseen Abbas
- CSIR - Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Sofia Banu
- CSIR - Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, Telangana, India
| | - Arup Ghosh
- Institute of Life Sciences (ILS), Bhubaneswar, Odisha, India
| | | | - Atimukta Jha
- Institute of Life Sciences (ILS), Bhubaneswar, Odisha, India
| | - Sana Fatma
- Institute of Life Sciences (ILS), Bhubaneswar, Odisha, India
| | - Shifu Aggarwal
- Institute of Life Sciences (ILS), Bhubaneswar, Odisha, India
| | - Mahesh Shanker Dhar
- Biotechnology Division, National Centre for Disease Control (NCDC), New Delhi, India
| | - Robin Marwal
- Biotechnology Division, National Centre for Disease Control (NCDC), New Delhi, India
| | | | - Kalaiarasan Ponnusamy
- Biotechnology Division, National Centre for Disease Control (NCDC), New Delhi, India
| | - Sandhya Kabra
- Biotechnology Division, National Centre for Disease Control (NCDC), New Delhi, India
| | - Partha Rakshit
- Biotechnology Division, National Centre for Disease Control (NCDC), New Delhi, India
| | - Rahul C Bhoyar
- CSIR - Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Abhinav Jain
- CSIR - Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Mohit Kumar Divakar
- CSIR - Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Mohamed Imran
- CSIR - Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Mohammed Faruq
- CSIR - Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Divya Tej Sowpati
- CSIR - Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, Telangana, India
| | - Lipi Thukral
- CSIR - Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Sunil K Raghav
- Institute of Life Sciences (ILS), Bhubaneswar, Odisha, India
| | - Mitali Mukerji
- CSIR - Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India.,Indian Institute of Technology (IIT), Jodhpur, India
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42
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Nunner H, van de Rijt A, Buskens V. Prioritizing high-contact occupations raises effectiveness of vaccination campaigns. Sci Rep 2022; 12:737. [PMID: 35031651 PMCID: PMC8760242 DOI: 10.1038/s41598-021-04428-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 12/22/2021] [Indexed: 12/13/2022] Open
Abstract
A twenty-year-old idea from network science is that vaccination campaigns would be more effective if high-contact individuals were preferentially targeted. Implementation is impeded by the ethical and practical problem of differentiating vaccine access based on a personal characteristic that is hard-to-measure and private. Here, we propose the use of occupational category as a proxy for connectedness in a contact network. Using survey data on occupation-specific contact frequencies, we calibrate a model of disease propagation in populations undergoing varying vaccination campaigns. We find that vaccination campaigns that prioritize high-contact occupational groups achieve similar infection levels with half the number of vaccines, while also reducing and delaying peaks. The paper thus identifies a concrete, operational strategy for dramatically improving vaccination efficiency in ongoing pandemics.
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Affiliation(s)
- Hendrik Nunner
- Department of Sociology/ICS, Utrecht University, Utrecht, The Netherlands.
- Centre for Complex System Studies (CCSS), Utrecht University, Utrecht, The Netherlands.
| | - Arnout van de Rijt
- Department of Political and Social Sciences, European University Institute, Florence, Italy
| | - Vincent Buskens
- Department of Sociology/ICS, Utrecht University, Utrecht, The Netherlands
- Centre for Complex System Studies (CCSS), Utrecht University, Utrecht, The Netherlands
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43
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Kirkegaard JB, Sneppen K. Superspreading quantified from bursty epidemic trajectories. Sci Rep 2021; 11:24124. [PMID: 34916534 PMCID: PMC8677763 DOI: 10.1038/s41598-021-03126-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 11/26/2021] [Indexed: 11/20/2022] Open
Abstract
The quantification of spreading heterogeneity in the COVID-19 epidemic is crucial as it affects the choice of efficient mitigating strategies irrespective of whether its origin is biological or social. We present a method to deduce temporal and individual variations in the basic reproduction number directly from epidemic trajectories at a community level. Using epidemic data from the 98 districts in Denmark we estimate an overdispersion factor k for COVID-19 to be about 0.11 (95% confidence interval 0.08-0.18), implying that 10 % of the infected cause between 70 % and 87 % of all infections.
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Affiliation(s)
| | - Kim Sneppen
- Niels Bohr Institute, University of Copenhagen, 2100, Copenhagen, Denmark
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44
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Li J, Lai S, Gao GF, Shi W. The emergence, genomic diversity and global spread of SARS-CoV-2. Nature 2021; 600:408-418. [PMID: 34880490 DOI: 10.1038/s41586-021-04188-6] [Citation(s) in RCA: 187] [Impact Index Per Article: 62.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 10/26/2021] [Indexed: 12/11/2022]
Abstract
Since the first cases of COVID-19 were documented in Wuhan, China in 2019, the world has witnessed a devastating global pandemic, with more than 238 million cases, nearly 5 million fatalities and the daily number of people infected increasing rapidly. Here we describe the currently available data on the emergence of the SARS-CoV-2 virus, the causative agent of COVID-19, outline the early viral spread in Wuhan and its transmission patterns in China and across the rest of the world, and highlight how genomic surveillance, together with other data such as those on human mobility, has helped to trace the spread and genetic variation of the virus and has also comprised a key element for the control of the pandemic. We pay particular attention to characterizing and describing the international spread of the major variants of concern of SARS-CoV-2 that were first identified in late 2020 and demonstrate that virus evolution has entered a new phase. More broadly, we highlight our currently limited understanding of coronavirus diversity in nature, the rapid spread of the virus and its variants in such an increasingly connected world, the reduced protection of vaccines, and the urgent need for coordinated global surveillance using genomic techniques. In summary, we provide important information for the prevention and control of both the ongoing COVID-19 pandemic and any new diseases that will inevitably emerge in the human population in future generations.
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Affiliation(s)
- Juan Li
- School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an, China.,Key Laboratory of Etiology and Epidemiology of Emerging Infectious Diseases in the Universities of Shandong, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an, China
| | - Shengjie Lai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - George F Gao
- National Institute for Viral Disease Control and Prevention, China CDC, Beijing, China.,CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology,, Chinese Academy of Sciences, Beijing, China.,Center for Influenza Research and Early-warning (CASCIRE), CAS-TWAS Center of Excellence for Emerging Infectious Diseases (CEEID), Chinese Academy of Sciences, Beijing, China
| | - Weifeng Shi
- School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an, China. .,Key Laboratory of Etiology and Epidemiology of Emerging Infectious Diseases in the Universities of Shandong, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an, China.
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45
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Decrease in overdispersed secondary transmission of COVID-19 over time in Japan. Epidemiol Infect 2021; 150:e197. [PMID: 36377373 PMCID: PMC9744460 DOI: 10.1017/s0950268822001789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Coronavirus disease 2019 (COVID-19) has been described as having an overdispersed offspring distribution, i.e. high variation in the number of secondary transmissions of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) per single primary COVID-19 case. Accordingly, countermeasures focused on high-risk settings and contact tracing could efficiently reduce secondary transmissions. However, as variants of concern with elevated transmissibility continue to emerge, controlling COVID-19 with such focused approaches has become difficult. It is vital to quantify temporal variations in the offspring distribution dispersibility. Here, we investigated offspring distributions for periods when the ancestral variant was still dominant (summer, 2020; wave 2) and when Alpha variant (B.1.1.7) was prevailing (spring, 2021; wave 4). The dispersion parameter (k) was estimated by analysing contact tracing data and fitting a negative binomial distribution to empirically observed offspring distributions from Nagano, Japan. The offspring distribution was less dispersed in wave 4 (k = 0.32; 95% confidence interval (CI) 0.24-0.43) than in wave 2 (k = 0.21 (95% CI 0.13-0.36)). A high proportion of household transmission was observed in wave 4, although the proportion of secondary transmissions generating more than five secondary cases did not vary over time. With this decreased variation, the effectiveness of risk group-focused interventions may be diminished.
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46
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Chappleboim A, Joseph-Strauss D, Rahat A, Sharkia I, Adam M, Kitsberg D, Fialkoff G, Lotem M, Gershon O, Schmidtner AK, Oiknine-Djian E, Klochendler A, Sadeh R, Dor Y, Wolf D, Habib N, Friedman N. Early sample tagging and pooling enables simultaneous SARS-CoV-2 detection and variant sequencing. Sci Transl Med 2021; 13:eabj2266. [PMID: 34591660 PMCID: PMC9928115 DOI: 10.1126/scitranslmed.abj2266] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Most severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) diagnostic tests have relied on RNA extraction followed by reverse transcription quantitative polymerase chain reaction (RT-qPCR) assays. Whereas automation improved logistics and different pooling strategies increased testing capacity, highly multiplexed next-generation sequencing (NGS) diagnostics remain a largely untapped resource. NGS tests have the potential to markedly increase throughput while providing crucial SARS-CoV-2 variant information. Current NGS-based detection and genotyping assays for SARS-CoV-2 are costly, mostly due to parallel sample processing through multiple steps. Here, we have established ApharSeq, in which samples are barcoded in the lysis buffer and pooled before reverse transcription. We validated this assay by applying ApharSeq to more than 500 clinical samples from the Clinical Virology Laboratory at Hadassah hospital in a robotic workflow. The assay was linear across five orders of magnitude, and the limit of detection was Ct 33 (~1000 copies/ml, 95% sensitivity) with >99.5% specificity. ApharSeq provided targeted high-confidence genotype information due to unique molecular identifiers incorporated into this method. Because of early pooling, we were able to estimate a 10- to 100-fold reduction in labor, automated liquid handling, and reagent requirements in high-throughput settings compared to current testing methods. The protocol can be tailored to assay other host or pathogen RNA targets simultaneously. These results suggest that ApharSeq can be a promising tool for current and future mass diagnostic challenges.
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Affiliation(s)
- Alon Chappleboim
- Alexander Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel.,Rachel and Selim Benin School of Computer Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Daphna Joseph-Strauss
- Alexander Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel.,Rachel and Selim Benin School of Computer Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Ayelet Rahat
- Alexander Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel.,Rachel and Selim Benin School of Computer Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Israa Sharkia
- Alexander Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel.,Rachel and Selim Benin School of Computer Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Miriam Adam
- Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Daniel Kitsberg
- Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Gavriel Fialkoff
- Alexander Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel.,Rachel and Selim Benin School of Computer Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Matan Lotem
- Alexander Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel.,Rachel and Selim Benin School of Computer Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Omer Gershon
- Alexander Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel.,Rachel and Selim Benin School of Computer Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Anna-Kristina Schmidtner
- Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Esther Oiknine-Djian
- Hadassah Hebrew University Medical Center, Jerusalem 9112001, Israel.,Lautenberg Centre for Immunology and Cancer Research, IMRIC, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112001, Israel
| | - Agnes Klochendler
- Department of Developmental Biology and Cancer Research, IMRIC, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112001, Israel
| | - Ronen Sadeh
- Alexander Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel.,Rachel and Selim Benin School of Computer Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Yuval Dor
- Department of Developmental Biology and Cancer Research, IMRIC, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112001, Israel
| | - Dana Wolf
- Hadassah Hebrew University Medical Center, Jerusalem 9112001, Israel.,Lautenberg Centre for Immunology and Cancer Research, IMRIC, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112001, Israel
| | - Naomi Habib
- Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Nir Friedman
- Alexander Silberman Institute of Life Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel.,Rachel and Selim Benin School of Computer Science, Hebrew University of Jerusalem, Jerusalem 9190401, Israel
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47
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Currie DW, Moreno GK, Delahoy MJ, Pray IW, Jovaag A, Braun KM, Cole D, Shechter T, Fajardo GC, Griggs C, Yandell BS, Goldstein S, Bushman D, Segaloff HE, Kelly GP, Pitts C, Lee C, Grande KM, Kita-Yarbro A, Grogan B, Mader S, Baggott J, Bateman AC, Westergaard RP, Tate JE, Friedrich TC, Kirking HL, O'Connor DH, Killerby ME. Interventions to Disrupt Coronavirus Disease Transmission at a University, Wisconsin, USA, August-October 2020. Emerg Infect Dis 2021; 27:2776-2785. [PMID: 34586058 PMCID: PMC8544969 DOI: 10.3201/eid2711.211306] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
University settings have demonstrated potential for coronavirus disease (COVID-19) outbreaks; they combine congregate living, substantial social activity, and a young population predisposed to mild illness. Using genomic and epidemiologic data, we describe a COVID-19 outbreak at the University of Wisconsin-Madison, Madison, Wisconsin, USA. During August-October 2020, a total of 3,485 students, including 856/6,162 students living in dormitories, tested positive. Case counts began rising during move-in week, August 25-31, 2020, then rose rapidly during September 1-11, 2020. The university initiated multiple prevention efforts, including quarantining 2 dormitories; a subsequent decline in cases was observed. Genomic surveillance of cases from Dane County, in which the university is located, did not find evidence of transmission from a large cluster of cases in the 2 quarantined dorms during the outbreak. Coordinated implementation of prevention measures can reduce COVID-19 spread in university settings and may limit spillover to the surrounding community.
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48
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Muenchhoff M, Graf A, Krebs S, Quartucci C, Hasmann S, Hellmuth JC, Scherer C, Osterman A, Boehm S, Mandel C, Becker-Pennrich AS, Zoller M, Stubbe HC, Munker S, Munker D, Milger K, Gapp M, Schneider S, Ruhle A, Jocham L, Nicolai L, Pekayvaz K, Weinberger T, Mairhofer H, Khatamzas E, Hofmann K, Spaeth PM, Bender S, Kääb S, Zwissler B, Mayerle J, Behr J, von Bergwelt-Baildon M, Reincke M, Grabein B, Hinske CL, Blum H, Keppler OT. Genomic epidemiology reveals multiple introductions of SARS-CoV-2 followed by community and nosocomial spread, Germany, February to May 2020. ACTA ACUST UNITED AC 2021; 26. [PMID: 34713795 PMCID: PMC8555370 DOI: 10.2807/1560-7917.es.2021.26.43.2002066] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Background In the SARS-CoV-2 pandemic, viral genomes are available at unprecedented speed, but spatio-temporal bias in genome sequence sampling precludes phylogeographical inference without additional contextual data. Aim We applied genomic epidemiology to trace SARS-CoV-2 spread on an international, national and local level, to illustrate how transmission chains can be resolved to the level of a single event and single person using integrated sequence data and spatio-temporal metadata. Methods We investigated 289 COVID-19 cases at a university hospital in Munich, Germany, between 29 February and 27 May 2020. Using the ARTIC protocol, we obtained near full-length viral genomes from 174 SARS-CoV-2-positive respiratory samples. Phylogenetic analyses using the Auspice software were employed in combination with anamnestic reporting of travel history, interpersonal interactions and perceived high-risk exposures among patients and healthcare workers to characterise cluster outbreaks and establish likely scenarios and timelines of transmission. Results We identified multiple independent introductions in the Munich Metropolitan Region during the first weeks of the first pandemic wave, mainly by travellers returning from popular skiing areas in the Alps. In these early weeks, the rate of presumable hospital-acquired infections among patients and in particular healthcare workers was high (9.6% and 54%, respectively) and we illustrated how transmission chains can be dissected at high resolution combining virus sequences and spatio-temporal networks of human interactions. Conclusions Early spread of SARS-CoV-2 in Europe was catalysed by superspreading events and regional hotspots during the winter holiday season. Genomic epidemiology can be employed to trace viral spread and inform effective containment strategies.
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Affiliation(s)
- Maximilian Muenchhoff
- Max von Pettenkofer Institute and Gene Center, Virology, National Reference Center for Retroviruses, LMU München, Munich, Germany.,German Center for Infection Research (DZIF), partner site Munich, Munich, Germany.,COVID-19 Registry of the LMU Munich (CORKUM), University Hospital, LMU Munich, Munich, Germany
| | - Alexander Graf
- Laboratory for Functional Genome Analysis, Gene Center, LMU Munich, Munich, Germany
| | - Stefan Krebs
- Laboratory for Functional Genome Analysis, Gene Center, LMU Munich, Munich, Germany
| | - Caroline Quartucci
- Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, Munich, Germany.,Comprehensive Pneumology Center Munich (CPC-M), Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Sandra Hasmann
- COVID-19 Registry of the LMU Munich (CORKUM), University Hospital, LMU Munich, Munich, Germany.,Department of Medicine IV, University Hospital, LMU Munich, Munich, Germany
| | - Johannes C Hellmuth
- COVID-19 Registry of the LMU Munich (CORKUM), University Hospital, LMU Munich, Munich, Germany.,Department of Medicine III, University Hospital, LMU Munich, Munich, Germany.,German Cancer Consortium (DKTK), Munich, Germany
| | - Clemens Scherer
- Department of Medicine I, University Hospital, LMU Munich, Munich, Germany.,DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany.,COVID-19 Registry of the LMU Munich (CORKUM), University Hospital, LMU Munich, Munich, Germany
| | - Andreas Osterman
- Max von Pettenkofer Institute and Gene Center, Virology, National Reference Center for Retroviruses, LMU München, Munich, Germany
| | - Stephan Boehm
- Max von Pettenkofer Institute and Gene Center, Virology, National Reference Center for Retroviruses, LMU München, Munich, Germany
| | - Christopher Mandel
- COVID-19 Registry of the LMU Munich (CORKUM), University Hospital, LMU Munich, Munich, Germany.,Department of Medicine IV, University Hospital, LMU Munich, Munich, Germany
| | - Andrea Sabine Becker-Pennrich
- Department of Anesthesiology, University Hospital, LMU Munich, Munich, Germany.,Department of Medical Information Processing, Biometry and Epidemiology (IBE), LMU Munich, Munich, Germany
| | - Michael Zoller
- Department of Anesthesiology, University Hospital, LMU Munich, Munich, Germany.,COVID-19 Registry of the LMU Munich (CORKUM), University Hospital, LMU Munich, Munich, Germany
| | - Hans Christian Stubbe
- Department of Medicine II, University Hospital, LMU Munich, Munich, Germany.,COVID-19 Registry of the LMU Munich (CORKUM), University Hospital, LMU Munich, Munich, Germany
| | - Stefan Munker
- Department of Medicine II, University Hospital, LMU Munich, Munich, Germany.,COVID-19 Registry of the LMU Munich (CORKUM), University Hospital, LMU Munich, Munich, Germany
| | - Dieter Munker
- Department of Medicine V, University Hospital, LMU Munich, Comprehensive Pneumology Center Munich (CPC-M), Member of the German Center for Lung Research (DZL), Munich, Germany.,COVID-19 Registry of the LMU Munich (CORKUM), University Hospital, LMU Munich, Munich, Germany.,Comprehensive Pneumology Center Munich (CPC-M), Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Katrin Milger
- Department of Medicine V, University Hospital, LMU Munich, Comprehensive Pneumology Center Munich (CPC-M), Member of the German Center for Lung Research (DZL), Munich, Germany.,Comprehensive Pneumology Center Munich (CPC-M), Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Madeleine Gapp
- Max von Pettenkofer Institute and Gene Center, Virology, National Reference Center for Retroviruses, LMU München, Munich, Germany
| | - Stephanie Schneider
- Max von Pettenkofer Institute and Gene Center, Virology, National Reference Center for Retroviruses, LMU München, Munich, Germany
| | - Adrian Ruhle
- Max von Pettenkofer Institute and Gene Center, Virology, National Reference Center for Retroviruses, LMU München, Munich, Germany
| | - Linda Jocham
- Max von Pettenkofer Institute and Gene Center, Virology, National Reference Center for Retroviruses, LMU München, Munich, Germany
| | - Leo Nicolai
- Department of Medicine I, University Hospital, LMU Munich, Munich, Germany.,DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany.,COVID-19 Registry of the LMU Munich (CORKUM), University Hospital, LMU Munich, Munich, Germany
| | - Kami Pekayvaz
- Department of Medicine I, University Hospital, LMU Munich, Munich, Germany.,DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany.,COVID-19 Registry of the LMU Munich (CORKUM), University Hospital, LMU Munich, Munich, Germany
| | - Tobias Weinberger
- Department of Medicine I, University Hospital, LMU Munich, Munich, Germany.,DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany.,COVID-19 Registry of the LMU Munich (CORKUM), University Hospital, LMU Munich, Munich, Germany
| | - Helga Mairhofer
- Max von Pettenkofer Institute and Gene Center, Virology, National Reference Center for Retroviruses, LMU München, Munich, Germany
| | - Elham Khatamzas
- COVID-19 Registry of the LMU Munich (CORKUM), University Hospital, LMU Munich, Munich, Germany.,Department of Medicine III, University Hospital, LMU Munich, Munich, Germany.,German Cancer Consortium (DKTK), Munich, Germany
| | - Katharina Hofmann
- Max von Pettenkofer Institute and Gene Center, Virology, National Reference Center for Retroviruses, LMU München, Munich, Germany
| | - Patricia M Spaeth
- Max von Pettenkofer Institute and Gene Center, Virology, National Reference Center for Retroviruses, LMU München, Munich, Germany
| | - Sabine Bender
- Max von Pettenkofer Institute and Gene Center, Virology, National Reference Center for Retroviruses, LMU München, Munich, Germany
| | - Stefan Kääb
- Department of Medicine I, University Hospital, LMU Munich, Munich, Germany.,DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany.,COVID-19 Registry of the LMU Munich (CORKUM), University Hospital, LMU Munich, Munich, Germany
| | - Bernhard Zwissler
- Department of Anesthesiology, University Hospital, LMU Munich, Munich, Germany.,COVID-19 Registry of the LMU Munich (CORKUM), University Hospital, LMU Munich, Munich, Germany.,Comprehensive Pneumology Center Munich (CPC-M), Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Julia Mayerle
- Department of Medicine II, University Hospital, LMU Munich, Munich, Germany.,COVID-19 Registry of the LMU Munich (CORKUM), University Hospital, LMU Munich, Munich, Germany
| | - Juergen Behr
- Department of Medicine V, University Hospital, LMU Munich, Comprehensive Pneumology Center Munich (CPC-M), Member of the German Center for Lung Research (DZL), Munich, Germany.,COVID-19 Registry of the LMU Munich (CORKUM), University Hospital, LMU Munich, Munich, Germany.,Comprehensive Pneumology Center Munich (CPC-M), Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Michael von Bergwelt-Baildon
- COVID-19 Registry of the LMU Munich (CORKUM), University Hospital, LMU Munich, Munich, Germany.,Department of Medicine III, University Hospital, LMU Munich, Munich, Germany.,German Cancer Consortium (DKTK), Munich, Germany
| | - Martin Reincke
- COVID-19 Registry of the LMU Munich (CORKUM), University Hospital, LMU Munich, Munich, Germany.,Department of Medicine IV, University Hospital, LMU Munich, Munich, Germany
| | - Beatrice Grabein
- Department of Clinical Microbiology and Hospital Hygiene, University Hospital, LMU Munich, Munich, Germany
| | - Christian Ludwig Hinske
- Department of Anesthesiology, University Hospital, LMU Munich, Munich, Germany.,Department of Medical Information Processing, Biometry and Epidemiology (IBE), LMU Munich, Munich, Germany.,COVID-19 Registry of the LMU Munich (CORKUM), University Hospital, LMU Munich, Munich, Germany
| | - Helmut Blum
- Laboratory for Functional Genome Analysis, Gene Center, LMU Munich, Munich, Germany
| | - Oliver T Keppler
- Max von Pettenkofer Institute and Gene Center, Virology, National Reference Center for Retroviruses, LMU München, Munich, Germany.,German Center for Infection Research (DZIF), partner site Munich, Munich, Germany.,COVID-19 Registry of the LMU Munich (CORKUM), University Hospital, LMU Munich, Munich, Germany
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49
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Nielsen BF, Sneppen K, Simonsen L, Mathiesen J. Differences in social activity increase efficiency of contact tracing. THE EUROPEAN PHYSICAL JOURNAL. B 2021; 94:209. [PMID: 34690541 PMCID: PMC8523203 DOI: 10.1140/epjb/s10051-021-00222-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 10/02/2021] [Indexed: 05/07/2023]
Abstract
ABSTRACT Digital contact tracing has been suggested as an effective strategy for controlling an epidemic without severely limiting personal mobility. Here, we use smartphone proximity data to explore how social structure affects contact tracing of COVID-19. We model the spread of COVID-19 and find that the effectiveness of contact tracing depends strongly on social network structure and heterogeneous social activity. Contact tracing is shown to be remarkably effective in a workplace environment and the effectiveness depends strongly on the minimum duration of contact required to initiate quarantine. In a realistic social network, we find that forward contact tracing with immediate isolation can reduce an epidemic by more than 70%. In perspective, our findings highlight the necessity of incorporating social heterogeneity into models of mitigation strategies. GRAPHIC ABSTRACT SUPPLEMENTARY INFORMATION The online version supplementary material available at 10.1140/epjb/s10051-021-00222-8.
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Affiliation(s)
- Bjarke Frost Nielsen
- Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100 Copenhagen, Denmark
| | - Kim Sneppen
- Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100 Copenhagen, Denmark
| | - Lone Simonsen
- Department of Science and Environment, Roskilde University, 4000 Roskilde, Denmark
| | - Joachim Mathiesen
- Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100 Copenhagen, Denmark
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50
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St-Onge G, Sun H, Allard A, Hébert-Dufresne L, Bianconi G. Universal Nonlinear Infection Kernel from Heterogeneous Exposure on Higher-Order Networks. PHYSICAL REVIEW LETTERS 2021; 127:158301. [PMID: 34678024 PMCID: PMC9199393 DOI: 10.1103/physrevlett.127.158301] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 07/26/2021] [Accepted: 08/25/2021] [Indexed: 06/13/2023]
Abstract
The collocation of individuals in different environments is an important prerequisite for exposure to infectious diseases on a social network. Standard epidemic models fail to capture the potential complexity of this scenario by (1) neglecting the higher-order structure of contacts that typically occur through environments like workplaces, restaurants, and households, and (2) assuming a linear relationship between the exposure to infected contacts and the risk of infection. Here, we leverage a hypergraph model to embrace the heterogeneity of environments and the heterogeneity of individual participation in these environments. We find that combining heterogeneous exposure with the concept of minimal infective dose induces a universal nonlinear relationship between infected contacts and infection risk. Under nonlinear infection kernels, conventional epidemic wisdom breaks down with the emergence of discontinuous transitions, superexponential spread, and hysteresis.
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Affiliation(s)
- Guillaume St-Onge
- Département de physique, de génie physique et d’optique, Université Laval, Québec (Québec) G1V 0A6, Canada
- Centre interdisciplinaire en modélisation mathématique, Université Laval, Québec (Québec) G1V 0A6, Canada
| | - Hanlin Sun
- School of Mathematical Sciences, Queen Mary University of London, London E1 4NS, United Kingdom
| | - Antoine Allard
- Département de physique, de génie physique et d’optique, Université Laval, Québec (Québec) G1V 0A6, Canada
- Centre interdisciplinaire en modélisation mathématique, Université Laval, Québec (Québec) G1V 0A6, Canada
- Vermont Complex Systems Center, University of Vermont, Burlington, Vermont 05405, USA
| | - Laurent Hébert-Dufresne
- Département de physique, de génie physique et d’optique, Université Laval, Québec (Québec) G1V 0A6, Canada
- Vermont Complex Systems Center, University of Vermont, Burlington, Vermont 05405, USA
- Department of Computer Science, University of Vermont, Burlington, Vermont 05405, USA
| | - Ginestra Bianconi
- School of Mathematical Sciences, Queen Mary University of London, London E1 4NS, United Kingdom
- The Alan Turing Institute, 96 Euston Road, London NW1 2DB, United Kingdom
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