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Wardle J, Bhatia S, Cori A, Nouvellet P. Temporal variations in international air travel: implications for modelling the spread of infectious diseases. J Travel Med 2024:taae062. [PMID: 38630887 DOI: 10.1093/jtm/taae062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 03/25/2024] [Indexed: 04/19/2024]
Abstract
BACKGROUND The international flight network creates multiple routes by which pathogens can quickly spread across the globe. In the early stages of infectious disease outbreaks, analyses using flight passenger data to identify countries at risk of importing the pathogen are common and can help inform disease control efforts. A challenge faced in this modelling is that the latest aviation statistics (referred to as contemporary data) are typically not immediately available. Therefore, flight patterns from a previous year are often used (referred to as historical data). We explored the suitability of historical data for predicting the spatial spread of emerging epidemics. METHODS We analysed monthly flight passenger data from the International Air Transport Association to assess how baseline air travel patterns were affected in outbreaks of MERS, Zika, and SARS-CoV-2 over the past decade. We then used a stochastic discrete time SEIR metapopulation model to simulate global spread of different pathogens, comparing how epidemic dynamics differed in simulations based on historical and contemporary data. RESULTS We observed local, short-term disruptions to air travel from South Korea and Brazil for the MERS and Zika outbreaks we studied, whereas global and longer-term flight disruption occurred during the SARS-CoV-2 pandemic.For outbreak events that were accompanied by local, small, and short-term changes in air travel, epidemic models using historical flight data gave similar projections of timing and locations of disease spread as when using contemporary flight data. However, historical data were less reliable to model the spread of an atypical outbreak such as SARS-CoV-2 in which there were durable and extensive levels of global travel disruption. CONCLUSIONS The use of historical flight data as a proxy in epidemic models is an acceptable practice except in rare, large epidemics that lead to substantial disruptions to international travel.
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Affiliation(s)
- Jack Wardle
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London
| | - Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London
- NIHR Health Protection Research Unit in Modelling and Health Economics, Modelling & Economics Unit, UK Health Security Agency
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London
| | - Pierre Nouvellet
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London
- School of Life Sciences, University of Sussex
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2
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Kim Y, Fournié G, Métras R, Song D, Donnelly CA, Pfeiffer DU, Nouvellet P. Lessons for cross-species viral transmission surveillance from highly pathogenic avian influenza Korean cat shelter outbreaks. Nat Commun 2023; 14:6958. [PMID: 37907544 PMCID: PMC10618209 DOI: 10.1038/s41467-023-42738-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 10/17/2023] [Indexed: 11/02/2023] Open
Abstract
In this Comment, the authors describe recent outbreaks of highly pathogenic avian influenza in cat shelters in Seoul, South Korea. They discuss potential routes of transmission and describe implications for surveillance of spillover infections in animals in non-agricultural settings.
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Affiliation(s)
- Younjung Kim
- Sorbonne Université, INSERM, Institut Pierre Louis d'Épidémiologie et de Santé Publique (IPLESP), UMRS 1136, Paris, France.
- Department of Ecology and Evolution, School of Life Sciences, University of Sussex, Brighton and Hove, UK.
| | - Guillaume Fournié
- Department of Pathobiology and Population Sciences, Royal Veterinary College, London, UK
- Université de Lyon, INRAE, VetAgro Sup, UMR EPIA, Marcy l'Etoile, France
- Université Clermont Auvergne, INRAE, VetAgro Sup, UMR EPIA, Saint-Gènes-Champanelle, France
| | - Raphaëlle Métras
- Sorbonne Université, INSERM, Institut Pierre Louis d'Épidémiologie et de Santé Publique (IPLESP), UMRS 1136, Paris, France
| | - Daesub Song
- Department of Veterinary Medicine Virology Laboratory, College of Veterinary Medicine and Research Institute for Veterinary Science, Seoul National University, Seoul, Republic of Korea
| | - Christl A Donnelly
- Department of Statistics, University of Oxford, Oxford, UK
- Pandemic Sciences Institute, University of Oxford, Oxford, UK
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Dirk U Pfeiffer
- Department of Pathobiology and Population Sciences, Royal Veterinary College, London, UK
- City University of Hong Kong, Hong Kong SAR, China
| | - Pierre Nouvellet
- Department of Ecology and Evolution, School of Life Sciences, University of Sussex, Brighton and Hove, UK
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
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3
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Bhatia S, Parag KV, Wardle J, Nash RK, Imai N, Elsland SLV, Lassmann B, Brownstein JS, Desai A, Herringer M, Sewalk K, Loeb SC, Ramatowski J, Cuomo-Dannenburg G, Jauneikaite E, Unwin HJT, Riley S, Ferguson N, Donnelly CA, Cori A, Nouvellet P. Retrospective evaluation of real-time estimates of global COVID-19 transmission trends and mortality forecasts. PLoS One 2023; 18:e0286199. [PMID: 37851661 PMCID: PMC10584190 DOI: 10.1371/journal.pone.0286199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 05/11/2023] [Indexed: 10/20/2023] Open
Abstract
Since 8th March 2020 up to the time of writing, we have been producing near real-time weekly estimates of SARS-CoV-2 transmissibility and forecasts of deaths due to COVID-19 for all countries with evidence of sustained transmission, shared online. We also developed a novel heuristic to combine weekly estimates of transmissibility to produce forecasts over a 4-week horizon. Here we present a retrospective evaluation of the forecasts produced between 8th March to 29th November 2020 for 81 countries. We evaluated the robustness of the forecasts produced in real-time using relative error, coverage probability, and comparisons with null models. During the 39-week period covered by this study, both the short- and medium-term forecasts captured well the epidemic trajectory across different waves of COVID-19 infections with small relative errors over the forecast horizon. The model was well calibrated with 56.3% and 45.6% of the observations lying in the 50% Credible Interval in 1-week and 4-week ahead forecasts respectively. The retrospective evaluation of our models shows that simple transmission models calibrated using routine disease surveillance data can reliably capture the epidemic trajectory in multiple countries. The medium-term forecasts can be used in conjunction with the short-term forecasts of COVID-19 mortality as a useful planning tool as countries continue to relax public health measures.
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Affiliation(s)
- Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
- NIHR Health Protection Research Unit in Modelling and Health Economics, Modelling & Economics Unit, UK Health Security Agency, London, United Kingdom
| | - Kris V. Parag
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Jack Wardle
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Rebecca K. Nash
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Natsuko Imai
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Sabine L. Van Elsland
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Britta Lassmann
- ProMED-mail, International Society for Infectious Diseases, Brookline, MA, United States of America
| | - John S. Brownstein
- Boston Children’s Hospital, Computational Epidemiology Lab, Boston, MA, United States of America
| | - Angel Desai
- ProMED-mail, International Society for Infectious Diseases, Brookline, MA, United States of America
- Division of Infectious Diseases, Department of Internal Medicine, University of California Davis, Sacramento, California, United States of America
| | - Mark Herringer
- Healthsites.io, The Global Healthsites Mapping Project, London, United Kingdom
| | - Kara Sewalk
- Boston Children’s Hospital, Computational Epidemiology Lab, Boston, MA, United States of America
| | - Sarah Claire Loeb
- ProMED-mail, International Society for Infectious Diseases, Brookline, MA, United States of America
| | - John Ramatowski
- ProMED-mail, International Society for Infectious Diseases, Brookline, MA, United States of America
| | - Gina Cuomo-Dannenburg
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Elita Jauneikaite
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - H. Juliette T. Unwin
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Neil Ferguson
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Christl A. Donnelly
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Pierre Nouvellet
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
- School of Life Sciences, University of Sussex, Brighton, United Kingdom
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4
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Ledien J, Cucunubá ZM, Parra-Henao G, Rodríguez-Monguí E, Dobson AP, Adamo SB, Castellanos LG, Basáñez MG, Nouvellet P. From serological surveys to disease burden: a modelling pipeline for Chagas disease. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220278. [PMID: 37598701 PMCID: PMC10440172 DOI: 10.1098/rstb.2022.0278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 06/29/2023] [Indexed: 08/22/2023] Open
Abstract
In 2012, the World Health Organization (WHO) set the elimination of Chagas disease intradomiciliary vectorial transmission as a goal by 2020. After a decade, some progress has been made, but the new 2021-2030 WHO roadmap has set even more ambitious targets. Innovative and robust modelling methods are required to monitor progress towards these goals. We present a modelling pipeline using local seroprevalence data to obtain national disease burden estimates by disease stage. Firstly, local seroprevalence information is used to estimate spatio-temporal trends in the Force-of-Infection (FoI). FoI estimates are then used to predict such trends across larger and fine-scale geographical areas. Finally, predicted FoI values are used to estimate disease burden based on a disease progression model. Using Colombia as a case study, we estimated that the number of infected people would reach 506 000 (95% credible interval (CrI) = 395 000-648 000) in 2020 with a 1.0% (95%CrI = 0.8-1.3%) prevalence in the general population and 2400 (95%CrI = 1900-3400) deaths (approx. 0.5% of those infected). The interplay between a decrease in infection exposure (FoI and relative proportion of acute cases) was overcompensated by a large increase in population size and gradual population ageing, leading to an increase in the absolute number of Chagas disease cases over time. This article is part of the theme issue 'Challenges and opportunities in the fight against neglected tropical diseases: a decade from the London Declaration on NTDs'.
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Affiliation(s)
- Julia Ledien
- School of Life Sciences, University of Sussex, Falmer, Brighton BN1 9RH, UK
| | - Zulma M. Cucunubá
- Departamento de Epidemiología Clínica y Bioestadística, Facultad de Medicina, Universidad Pontificia Javeriana, 110231 Bogotá, Colombia
| | - Gabriel Parra-Henao
- Centro de Investigación en Salud para el Trópico, Universidad Cooperativa de Colombia, 470002, Santa Marta, Colombia
- National Institute of Health, 111321 Bogotá, Colombia
| | - Eliana Rodríguez-Monguí
- Departamento de Epidemiología Clínica y Bioestadística, Facultad de Medicina, Universidad Pontificia Javeriana, 110231 Bogotá, Colombia
- Independent consultant to the Neglected, Tropical and Vector Borne Diseases Program, Pan American Health Organization (PAHO), Colombia
| | - Andrew P. Dobson
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
| | - Susana B. Adamo
- Center for International Earth Science Information Network (CIESIN), Columbia Climate School, Columbia University, New York, NY 10025, USA
| | - Luis Gerardo Castellanos
- Department of Communicable Diseases and Environmental Determinants of Health, Pan American Health Organization (PAHO), Washington, DC 20037, USA
| | - María-Gloria Basáñez
- London Centre for Neglected Tropical Disease Research (LCNTDR) & MRC Centre for Global Infectious Disease Analysis (GIDA), Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London SW7 2AZ, UK
| | - Pierre Nouvellet
- School of Life Sciences, University of Sussex, Falmer, Brighton BN1 9RH, UK
- London Centre for Neglected Tropical Disease Research (LCNTDR) & MRC Centre for Global Infectious Disease Analysis (GIDA), Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London SW7 2AZ, UK
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5
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Bhatia S, Imai N, Watson OJ, Abbood A, Abdelmalik P, Cornelissen T, Ghozzi S, Lassmann B, Nagesh R, Ragonnet-Cronin ML, Schnitzler JC, Kraemer MU, Cauchemez S, Nouvellet P, Cori A. Lessons from COVID-19 for rescalable data collection. Lancet Infect Dis 2023; 23:e383-e388. [PMID: 37150186 PMCID: PMC10159580 DOI: 10.1016/s1473-3099(23)00121-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 01/25/2023] [Accepted: 02/06/2023] [Indexed: 05/09/2023]
Abstract
Novel data and analyses have had an important role in informing the public health response to the COVID-19 pandemic. Existing surveillance systems were scaled up, and in some instances new systems were developed to meet the challenges posed by the magnitude of the pandemic. We describe the routine and novel data that were used to address urgent public health questions during the pandemic, underscore the challenges in sustainability and equity in data generation, and highlight key lessons learnt for designing scalable data collection systems to support decision making during a public health crisis. As countries emerge from the acute phase of the pandemic, COVID-19 surveillance systems are being scaled down. However, SARS-CoV-2 resurgence remains a threat to global health security; therefore, a minimal cost-effective system needs to remain active that can be rapidly scaled up if necessary. We propose that a retrospective evaluation to identify the cost-benefit profile of the various data streams collected during the pandemic should be on the scientific research agenda.
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Affiliation(s)
- Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK; NIHR Health Protection Research Unit in Modelling and Health Economics, Imperial College London, UK Health Security Agency, and London School of Hygiene & Tropical Medicine, London, UK; Modelling and Economics Unit, UK Health Security Agency, London, UK
| | - Natsuko Imai
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Oliver J Watson
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK; Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | | | | | | | - Stéphane Ghozzi
- Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Britta Lassmann
- ProMED-mail, International Society for Infectious Diseases, Brookline, MA, USA
| | - Radhika Nagesh
- Blavatnik School of Government, University of Oxford, Oxford, UK
| | - Manon L Ragonnet-Cronin
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK; Department of Ecology & Evolution, University of Chicago, Chicago, IL, USA
| | | | - Moritz Ug Kraemer
- Department of Biology, University of Oxford, Oxford, UK; Pandemic Sciences Institute, University of Oxford, Oxford, UK
| | - Simon Cauchemez
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, CNRS UMR2000, Paris, France
| | - Pierre Nouvellet
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK; School of Life Sciences, University of Sussex, Brighton, UK
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK; NIHR Health Protection Research Unit in Modelling and Health Economics, Imperial College London, UK Health Security Agency, and London School of Hygiene & Tropical Medicine, London, UK.
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6
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Kim Y, Nouvellet P, Rogoll L, Staubach C, Schulz K, Sauter-Louis C, Pfeiffer DU, Fournié G. Contrasting seasonality of African swine fever outbreaks and its drivers. Epidemics 2023; 44:100703. [PMID: 37385853 DOI: 10.1016/j.epidem.2023.100703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 05/30/2023] [Accepted: 06/12/2023] [Indexed: 07/01/2023] Open
Abstract
The seasonality of African swine fever (ASF) outbreaks in domestic pigs differs between temperate and subtropical/tropical regions. We hypothesise that variations in the importance of wild boar-to-farm and farm-to-farm transmission routes shape these contrasting patterns, and we emphasise the implications for effective ASF control.
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Affiliation(s)
| | - Pierre Nouvellet
- University of Sussex, Brighton, UK; Imperial College London, London, UK
| | - Lisa Rogoll
- Friedrich-Loeffler-Institut, Greifswald-Insel Riems, Germany
| | | | - Katja Schulz
- Friedrich-Loeffler-Institut, Greifswald-Insel Riems, Germany
| | | | - Dirk Udo Pfeiffer
- The Royal Veterinary College, London, UK; City University of Hong Kong, Hong Kong SAR, Hong Kong
| | - Guillaume Fournié
- The Royal Veterinary College, London, UK; Université de Lyon, INRAE, VetAgro Sup, UMR EPIA, Marcy l'Etoile, France; Université Clermont Auvergne, INRAE, VetAgro Sup, UMR EPIA, Saint-Gènes-Champanelle, France.
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7
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Bhatia S, Wardle J, Nash RK, Nouvellet P, Cori A. Extending EpiEstim to estimate the transmission advantage of pathogen variants in real-time: SARS-CoV-2 as a case-study. Epidemics 2023; 44:100692. [PMID: 37399634 PMCID: PMC10284428 DOI: 10.1016/j.epidem.2023.100692] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 04/20/2023] [Accepted: 05/29/2023] [Indexed: 07/05/2023] Open
Abstract
The evolution of SARS-CoV-2 has demonstrated that emerging variants can set back the global COVID-19 response. The ability to rapidly assess the threat of new variants is critical for timely optimisation of control strategies. We present a novel method to estimate the effective transmission advantage of a new variant compared to a reference variant combining information across multiple locations and over time. Through an extensive simulation study designed to mimic real-time epidemic contexts, we show that our method performs well across a range of scenarios and provide guidance on its optimal use and interpretation of results. We also provide an open-source software implementation of our method. The computational speed of our tool enables users to rapidly explore spatial and temporal variations in the estimated transmission advantage. We estimate that the SARS-CoV-2 Alpha variant is 1.46 (95% Credible Interval 1.44-1.47) and 1.29 (95% CrI 1.29-1.30) times more transmissible than the wild type, using data from England and France respectively. We further estimate that Delta is 1.77 (95% CrI 1.69-1.85) times more transmissible than Alpha (England data). Our approach can be used as an important first step towards quantifying the threat of emerging or co-circulating variants of infectious pathogens in real-time.
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Affiliation(s)
- Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK; NIHR Health Protection Research Unit in Modelling and Health Economics, Modelling & Economics Unit, UK Health Security Agency, London, UK
| | - Jack Wardle
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
| | - Rebecca K Nash
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
| | - Pierre Nouvellet
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK; School of Life Sciences, University of Sussex, Brighton, UK
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK.
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8
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Nash RK, Bhatt S, Cori A, Nouvellet P. Estimating the epidemic reproduction number from temporally aggregated incidence data: A statistical modelling approach and software tool. PLoS Comput Biol 2023; 19:e1011439. [PMID: 37639484 PMCID: PMC10491397 DOI: 10.1371/journal.pcbi.1011439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 09/08/2023] [Accepted: 08/18/2023] [Indexed: 08/31/2023] Open
Abstract
The time-varying reproduction number (Rt) is an important measure of epidemic transmissibility that directly informs policy decisions and the optimisation of control measures. EpiEstim is a widely used opensource software tool that uses case incidence and the serial interval (SI, time between symptoms in a case and their infector) to estimate Rt in real-time. The incidence and the SI distribution must be provided at the same temporal resolution, which can limit the applicability of EpiEstim and other similar methods, e.g. for contexts where the time window of incidence reporting is longer than the mean SI. In the EpiEstim R package, we implement an expectation-maximisation algorithm to reconstruct daily incidence from temporally aggregated data, from which Rt can then be estimated. We assess the validity of our method using an extensive simulation study and apply it to COVID-19 and influenza data. For all datasets, the influence of intra-weekly variability in reported data was mitigated by using aggregated weekly data. Rt estimated on weekly sliding windows using incidence reconstructed from weekly data was strongly correlated with estimates from the original daily data. The simulation study revealed that Rt was well estimated in all scenarios and regardless of the temporal aggregation of the data. In the presence of weekend effects, Rt estimates from reconstructed data were more successful at recovering the true value of Rt than those obtained from reported daily data. These results show that this novel method allows Rt to be successfully recovered from aggregated data using a simple approach with very few data requirements. Additionally, by removing administrative noise when daily incidence data are reconstructed, the accuracy of Rt estimates can be improved.
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Affiliation(s)
- Rebecca K. Nash
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, United Kingdom
| | - Samir Bhatt
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, United Kingdom
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, United Kingdom
| | - Pierre Nouvellet
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, United Kingdom
- School of Life Sciences, University of Sussex, Brighton, United Kingdom
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9
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Thrift E, Nouvellet P, Mathews F. Plastic Entanglement Poses a Potential Hazard to European Hedgehogs Erinaceus europaeus in Great Britain. Animals (Basel) 2023; 13:2448. [PMID: 37570257 PMCID: PMC10417105 DOI: 10.3390/ani13152448] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 07/24/2023] [Accepted: 07/28/2023] [Indexed: 08/13/2023] Open
Abstract
A questionnaire to gather evidence on the plastic entanglement of the European hedgehog (Erinaceus europaeus) was sent to 160 wildlife rehabilitation centres in Great Britain. Fifty-four responses were received, and 184 individual admissions owing to plastic entanglement were reported. Death was the outcome for 46% (n = 86) of these cases. A high proportion of Britain's hedgehogs enter rehabilitation centres annually (approximately 5% of the national population and potentially 10% of the urban population), providing a robust basis for assessing the minimum impacts at a national level. We estimate that 4000-7000 hedgehog deaths per year are attributable to plastic, with the true rate likely being higher, since many entangled hedgehogs-in contrast to those involved in road traffic accidents-will not be found. Population modelling indicates that this excess mortality is sufficient to cause population declines. Although the scale of the impact is much lower than that attributable to traffic, it is nevertheless an additional pressure on a species that is already in decline and presents a significant welfare issue to a large number of individuals.
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Affiliation(s)
| | | | - Fiona Mathews
- School of Life Sciences, University of Sussex, Brighton BN1 9RH, UK; (E.T.); (P.N.)
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10
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Kim Y, Leopardi S, Scaravelli D, Zecchin B, Priori P, Festa F, Drzewnioková P, De Benedictis P, Nouvellet P. Transmission dynamics of lyssavirus in Myotis myotis: mechanistic modelling study based on longitudinal seroprevalence data. Proc Biol Sci 2023; 290:20230183. [PMID: 37072038 PMCID: PMC10113028 DOI: 10.1098/rspb.2023.0183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2023] Open
Abstract
We investigated the transmission dynamics of lyssavirus in Myotis myotis and Myotis blythii, using serological, virological, demographic and ecological data collected between 2015 and 2022 from two maternity colonies in northern Italian churches. Despite no lyssavirus detection in 556 bats sampled over 11 events by reverse transcription-polymerase chain reaction (RT-PCR), 36.3% of 837 bats sampled over 27 events showed neutralizing antibodies to European bat lyssavirus 1, with a significant increase in summers. By fitting sets of mechanistic models to seroprevalence data, we investigated factors that influenced lyssavirus transmission within and between years. Five models were selected as a group of final models: in one model, a proportion of exposed bats (median model estimate: 5.8%) became infectious and died while the other exposed bats recovered with immunity without becoming infectious; in the other four models, all exposed bats became infectious and recovered with immunity. The final models supported that the two colonies experienced seasonal outbreaks driven by: (i) immunity loss particularly during hibernation, (ii) density-dependent transmission, and (iii) a high transmission rate after synchronous birthing. These findings highlight the importance of understanding ecological factors, including colony size and synchronous birthing timing, and potential infection heterogeneities to enable more robust assessments of lyssavirus spillover risk.
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Affiliation(s)
- Younjung Kim
- Department of Evolution, Behaviour, and Environment, School of Life Sciences, University of Sussex, BN1 9RH Brighton, UK
| | - Stefania Leopardi
- FAO and National Reference Centre for Rabies, Istituto Zooprofilattico Sperimentale delle Venezie, Viale dell'Università 10, Legnaro, 35020 Padua, Italy
| | - Dino Scaravelli
- S.T.E.R.N.A. and Museo Ornitologico 'F. Foschi', via Pedrali 12, 47121 Forlì, Italy
- Department of Biological, Geological and Environmental Sciences, University of Bologna, via Selmi 3, 40126 Bologna, Italy
| | - Barbara Zecchin
- FAO and National Reference Centre for Rabies, Istituto Zooprofilattico Sperimentale delle Venezie, Viale dell'Università 10, Legnaro, 35020 Padua, Italy
| | - Pamela Priori
- S.T.E.R.N.A. and Museo Ornitologico 'F. Foschi', via Pedrali 12, 47121 Forlì, Italy
| | - Francesca Festa
- FAO and National Reference Centre for Rabies, Istituto Zooprofilattico Sperimentale delle Venezie, Viale dell'Università 10, Legnaro, 35020 Padua, Italy
| | - Petra Drzewnioková
- FAO and National Reference Centre for Rabies, Istituto Zooprofilattico Sperimentale delle Venezie, Viale dell'Università 10, Legnaro, 35020 Padua, Italy
| | - Paola De Benedictis
- FAO and National Reference Centre for Rabies, Istituto Zooprofilattico Sperimentale delle Venezie, Viale dell'Università 10, Legnaro, 35020 Padua, Italy
| | - Pierre Nouvellet
- Department of Evolution, Behaviour, and Environment, School of Life Sciences, University of Sussex, BN1 9RH Brighton, UK
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, SW7 2AZ London, UK
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11
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Kim Y, Donnelly CA, Nouvellet P. Drivers of SARS-CoV-2 testing behaviour: a modelling study using nationwide testing data in England. Nat Commun 2023; 14:2148. [PMID: 37059861 PMCID: PMC10103662 DOI: 10.1038/s41467-023-37813-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Accepted: 03/30/2023] [Indexed: 04/16/2023] Open
Abstract
During the COVID-19 pandemic, national testing programmes were conducted worldwide on unprecedented scales. While testing behaviour is generally recognised as dynamic and complex, current literature demonstrating and quantifying such relationships is scarce, despite its importance for infectious disease surveillance and control. Here, we characterise the impacts of SARS-CoV-2 transmission, disease susceptibility/severity, risk perception, and public health measures on SARS-CoV-2 PCR testing behaviour in England over 20 months of the pandemic, by linking testing trends to underlying epidemic trends and contextual meta-data within a systematic conceptual framework. The best-fitting model describing SARS-CoV-2 PCR testing behaviour explained close to 80% of the total deviance in NHS test data. Testing behaviour showed complex associations with factors reflecting transmission level, disease susceptibility/severity (e.g. age, dominant variant, and vaccination), public health measures (e.g. testing strategies and lockdown), and associated changes in risk perception, varying throughout the pandemic and differing between infected and non-infected people.
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Affiliation(s)
- Younjung Kim
- Department of Evolution, Behaviour, and Environment, School of Life Sciences, University of Sussex, Brighton, UK
| | - Christl A Donnelly
- Department of Statistics, University of Oxford, Oxford, UK
- Pandemic Sciences Institute, University of Oxford, Oxford, UK
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Pierre Nouvellet
- Department of Evolution, Behaviour, and Environment, School of Life Sciences, University of Sussex, Brighton, UK.
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK.
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12
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Cori A, Lassmann B, Nouvellet P. Data needs for better surveillance and response to infectious disease threats. Epidemics 2023:100685. [PMID: 37076350 PMCID: PMC10101508 DOI: 10.1016/j.epidem.2023.100685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/21/2023] Open
Affiliation(s)
- Anne Cori
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, UK.
| | - Britta Lassmann
- Emerging Infections Task Force, European Society of Clinical Microbiology and Infectious Diseases
| | - Pierre Nouvellet
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, UK; School of Life Sciences, University of Sussex, Brighton, UK
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13
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Wardle J, Bhatia S, Kraemer MUG, Nouvellet P, Cori A. Gaps in mobility data and implications for modelling epidemic spread: A scoping review and simulation study. Epidemics 2023; 42:100666. [PMID: 36689876 DOI: 10.1016/j.epidem.2023.100666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 11/18/2022] [Accepted: 01/06/2023] [Indexed: 01/13/2023] Open
Abstract
Reliable estimates of human mobility are important for understanding the spatial spread of infectious diseases and the effective targeting of control measures. However, when modelling infectious disease dynamics, data on human mobility at an appropriate temporal or spatial resolution are not always available, leading to the common use of model-derived mobility proxies. In this study we reviewed the different data sources and mobility models that have been used to characterise human movement in Africa. We then conducted a simulation study to better understand the implications of using human mobility proxies when predicting the spatial spread and dynamics of infectious diseases. We found major gaps in the availability of empirical measures of human mobility in Africa, leading to mobility proxies being used in place of data. Empirical data on subnational mobility were only available for 17/54 countries, and in most instances, these data characterised long-term movement patterns, which were unsuitable for modelling the spread of pathogens with short generation times (time between infection of a case and their infector). Results from our simulation study demonstrated that using mobility proxies can have a substantial impact on the predicted epidemic dynamics, with complex and non-intuitive biases. In particular, the predicted times and order of epidemic invasion, and the time of epidemic peak in different locations can be underestimated or overestimated, depending on the types of proxies used and the country of interest. Our work underscores the need for regularly updated empirical measures of population movement within and between countries to aid the prevention and control of infectious disease outbreaks. At the same time, there is a need to establish an evidence base to help understand which types of mobility data are most appropriate for describing the spread of emerging infectious diseases in different settings.
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Affiliation(s)
- Jack Wardle
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, UK
| | - Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, UK
| | | | - Pierre Nouvellet
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, UK; School of Life Sciences, University of Sussex, Brighton, UK
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, UK.
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14
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Charniga K, Cucunubá ZM, Walteros DM, Mercado M, Prieto F, Ospina M, Nouvellet P, Donnelly CA. Estimating Zika virus attack rates and risk of Zika virus-associated neurological complications in Colombian capital cities with a Bayesian model. R Soc Open Sci 2022; 9:220491. [PMID: 36465672 PMCID: PMC9709519 DOI: 10.1098/rsos.220491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 11/08/2022] [Indexed: 06/17/2023]
Abstract
Zika virus (ZIKV) is a mosquito-borne pathogen that caused a major epidemic in the Americas in 2015-2017. Although the majority of ZIKV infections are asymptomatic, the virus has been associated with congenital birth defects and neurological complications (NC) in adults. We combined multiple data sources to improve estimates of ZIKV infection attack rates (IARs), reporting rates of Zika virus disease (ZVD) and the risk of ZIKV-associated NC for 28 capital cities in Colombia. ZVD surveillance data were combined with post-epidemic seroprevalence data and a dataset on ZIKV-associated NC in a Bayesian hierarchical model. We found substantial heterogeneity in ZIKV IARs across cities. The overall estimated ZIKV IAR across the 28 cities was 0.38 (95% CrI: 0.17-0.92). The estimated ZVD reporting rate was 0.013 (95% CrI: 0.004-0.024), and 0.51 (95% CrI: 0.17-0.92) cases of ZIKV-associated NC were estimated to be reported per 10 000 ZIKV infections. When we assumed the same ZIKV IAR across sex or age group, we found important spatial heterogeneities in ZVD reporting rates and the risk of being reported as a ZVD case with NC. Our results highlight how additional data sources can be used to overcome biases in surveillance data and estimate key epidemiological parameters.
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Affiliation(s)
- Kelly Charniga
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Zulma M. Cucunubá
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | | | | | | | | | | | - Christl A. Donnelly
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
- Department of Statistics, University of Oxford, Oxford, UK
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15
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Nash RK, Nouvellet P, Cori A. Real-time estimation of the epidemic reproduction number: Scoping review of the applications and challenges. PLOS Digit Health 2022; 1:e0000052. [PMID: 36812522 PMCID: PMC9931334 DOI: 10.1371/journal.pdig.0000052] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 04/27/2022] [Indexed: 12/24/2022]
Abstract
The time-varying reproduction number (Rt) is an important measure of transmissibility during outbreaks. Estimating whether and how rapidly an outbreak is growing (Rt > 1) or declining (Rt < 1) can inform the design, monitoring and adjustment of control measures in real-time. We use a popular R package for Rt estimation, EpiEstim, as a case study to evaluate the contexts in which Rt estimation methods have been used and identify unmet needs which would enable broader applicability of these methods in real-time. A scoping review, complemented by a small EpiEstim user survey, highlight issues with the current approaches, including the quality of input incidence data, the inability to account for geographical factors, and other methodological issues. We summarise the methods and software developed to tackle the problems identified, but conclude that significant gaps remain which should be addressed to enable easier, more robust and applicable estimation of Rt during epidemics.
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Affiliation(s)
- Rebecca K. Nash
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London
| | - Pierre Nouvellet
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London
- School of Life Sciences, University of Sussex
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London
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16
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Ledien J, Cucunubá ZM, Parra-Henao G, Rodríguez-Monguí E, Dobson AP, Basáñez MG, Nouvellet P. Spatiotemporal variations in exposure: Chagas disease in Colombia as a case study. BMC Med Res Methodol 2022; 22:13. [PMID: 35027002 PMCID: PMC8759231 DOI: 10.1186/s12874-021-01477-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 11/15/2021] [Indexed: 11/29/2022] Open
Abstract
Age-stratified serosurvey data are often used to understand spatiotemporal trends in disease incidence and exposure through estimating the Force-of-Infection (FoI). Typically, median or mean FoI estimates are used as the response variable in predictive models, often overlooking the uncertainty in estimated FoI values when fitting models and evaluating their predictive ability. To assess how this uncertainty impact predictions, we compared three approaches with three levels of uncertainty integration. We propose a performance indicator to assess how predictions reflect initial uncertainty. In Colombia, 76 serosurveys (1980–2014) conducted at municipality level provided age-stratified Chagas disease prevalence data. The yearly FoI was estimated at the serosurvey level using a time-varying catalytic model. Environmental, demographic and entomological predictors were used to fit and predict the FoI at municipality level from 1980 to 2010 across Colombia. A stratified bootstrap method was used to fit the models without temporal autocorrelation at the serosurvey level. The predictive ability of each model was evaluated to select the best-fit models within urban, rural and (Amerindian) indigenous settings. Model averaging, with the 10 best-fit models identified, was used to generate predictions. Our analysis shows a risk of overconfidence in model predictions when median estimates of FoI alone are used to fit and evaluate models, failing to account for uncertainty in FoI estimates. Our proposed methodology fully propagates uncertainty in the estimated FoI onto the generated predictions, providing realistic assessments of both central tendency and current uncertainty surrounding exposure to Chagas disease.
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Affiliation(s)
- Julia Ledien
- School of Life Sciences, University of Sussex, Brighton, UK.
| | - Zulma M Cucunubá
- London Centre for Neglected Tropical Disease Research & MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.,Departamento de Epidemiología Clínica y Bioestadística, Facultad de Medicina, Universidad Javeriana, Bogotá, Colombia
| | - Gabriel Parra-Henao
- Centro de Investigación en Salud para el Trópico, Universidad Cooperativa de Colombia, Santa Marta, Colombia.,National Institute of Health, Bogotá, Colombia
| | - Eliana Rodríguez-Monguí
- Neglected, Tropical and Vector Borne Diseases Program, Pan American Health Organization (PAHO), Bogotá, Colombia
| | - Andrew P Dobson
- Ecology & Evolutionary Biology, Princeton University, Princeton, USA
| | - María-Gloria Basáñez
- London Centre for Neglected Tropical Disease Research & MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
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17
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Hayes S, Lushasi K, Sambo M, Changalucha J, Ferguson EA, Sikana L, Hampson K, Nouvellet P, Donnelly CA. Understanding the incidence and timing of rabies cases in domestic animals and wildlife in south-east Tanzania in the presence of widespread domestic dog vaccination campaigns. Vet Res 2022; 53:106. [PMID: 36510331 PMCID: PMC9743725 DOI: 10.1186/s13567-022-01121-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 10/17/2022] [Indexed: 12/14/2022] Open
Abstract
The "Zero by 30" strategic plan aims to eliminate human deaths from dog-mediated rabies by 2030 and domestic dog vaccination is a vital component of this strategic plan. In areas where domestic dog vaccination has been implemented, it is important to assess the impact of this intervention. Additionally, understanding temporal and seasonal trends in the incidence of animal rabies cases may assist in optimizing such interventions. Data on the incidence of probable rabies cases in domestic and wild animals were collected between January 2011 and December 2018 in thirteen districts of south-east Tanzania where jackals comprise over 40% of reported rabies cases. Vaccination coverage was estimated over this period, as five domestic dog vaccination campaigns took place in all thirteen districts between 2011 and 2016. Negative binomial generalized linear models were used to explore the impact of domestic dog vaccination on the annual incidence of animal rabies cases, whilst generalized additive models were used to investigate the presence of temporal and/or seasonal trends. Increases in domestic dog vaccination coverage were significantly associated with a decreased incidence of rabies cases in both domestic dogs and jackals. A 35% increase in vaccination coverage was associated with a reduction in the incidence of probable dog rabies cases of between 78.0 and 85.5% (95% confidence intervals ranged from 61.2 to 92.2%) and a reduction in the incidence of probable jackal rabies cases of between 75.3 and 91.2% (95% confidence intervals ranged from 53.0 to 96.1%). A statistically significant common seasonality was identified in the monthly incidence of probable rabies cases in both domestic dogs and jackals with the highest incidence from February to August and lowest incidence from September to January. These results align with evidence supporting the use of domestic dog vaccination as part of control strategies aimed at reducing animal rabies cases in both domestic dogs and jackals in this region. The presence of a common seasonal trend requires further investigation but may have implications for the timing of future vaccination campaigns.
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Affiliation(s)
- Sarah Hayes
- grid.7445.20000 0001 2113 8111Department of Infectious Disease Epidemiology, Faculty of Medicine, School of Public Health, Imperial College London, London, UK ,grid.4991.50000 0004 1936 8948Department of Statistics, University of Oxford, Oxford, UK
| | - Kennedy Lushasi
- grid.414543.30000 0000 9144 642XIfakara Health Institute, Ifakara, Tanzania ,grid.8756.c0000 0001 2193 314XInstitute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK ,grid.451346.10000 0004 0468 1595Nelson Mandela African Institution of Science and Technology, Arusha, Tanzania
| | - Maganga Sambo
- grid.414543.30000 0000 9144 642XIfakara Health Institute, Ifakara, Tanzania
| | - Joel Changalucha
- grid.414543.30000 0000 9144 642XIfakara Health Institute, Ifakara, Tanzania ,grid.8756.c0000 0001 2193 314XInstitute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK
| | - Elaine A. Ferguson
- grid.8756.c0000 0001 2193 314XInstitute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK
| | - Lwitiko Sikana
- grid.414543.30000 0000 9144 642XIfakara Health Institute, Ifakara, Tanzania ,grid.8756.c0000 0001 2193 314XInstitute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK
| | - Katie Hampson
- grid.414543.30000 0000 9144 642XIfakara Health Institute, Ifakara, Tanzania ,grid.8756.c0000 0001 2193 314XInstitute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK
| | - Pierre Nouvellet
- grid.12082.390000 0004 1936 7590School of Life Sciences, University of Sussex, Sussex, UK
| | - Christl A. Donnelly
- grid.7445.20000 0001 2113 8111Department of Infectious Disease Epidemiology, Faculty of Medicine, School of Public Health, Imperial College London, London, UK ,grid.4991.50000 0004 1936 8948Department of Statistics, University of Oxford, Oxford, UK
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18
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Lushasi K, Hayes S, Ferguson EA, Changalucha J, Cleaveland S, Govella NJ, Haydon DT, Sambo M, Mchau GJ, Mpolya EA, Mtema Z, Nonga HE, Steenson R, Nouvellet P, Donnelly CA, Hampson K. Reservoir dynamics of rabies in south-east Tanzania and the roles of cross-species transmission and domestic dog vaccination. J Appl Ecol 2021; 58:2673-2685. [PMID: 35221371 PMCID: PMC7612421 DOI: 10.1111/1365-2664.13983] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 06/24/2021] [Indexed: 12/27/2022]
Abstract
Understanding the role of different species in the transmission of multi-host pathogens, such as rabies virus, is vital for effective control strategies. Across most of sub-Saharan Africa domestic dogs Canis familiaris are considered the reservoir for rabies, but the role of wildlife has been long debated. Here we explore the multi-host transmission dynamics of rabies across south-east Tanzania.Between January 2011 and July 2019, data on probable rabies cases were collected in the regions of Lindi and Mtwara. Hospital records of animal-bite patients presenting to healthcare facilities were used as sentinels for animal contact tracing. The timing, location and species of probable rabid animals were used to reconstruct transmission trees to infer who infected whom and the relative frequencies of within- and between-species transmission.During the study, 688 probable human rabies exposures were identified, resulting in 47 deaths. Of these exposures, 389 were from domestic dogs (56.5%) and 262 from jackals (38.1%). Over the same period, 549 probable animal rabies cases were traced: 303 in domestic dogs (55.2%) and 221 in jackals (40.3%), with the remainder in domestic cats and other wildlife species.Although dog-to-dog transmission was most commonly inferred (40.5% of transmission events), a third of inferred events involved wildlife-to-wildlife transmission (32.6%), and evidence suggested some sustained transmission chains within jackal populations.A steady decline in probable rabies cases in both humans and animals coincided with the implementation of widespread domestic dog vaccination during the first 6 years of the study. Following the lapse of this program, dog rabies cases began to increase in one of the northernmost districts. Synthesis and applications. In south-east Tanzania, despite a relatively high incidence of rabies in wildlife and evidence of wildlife-to-wildlife transmission, domestic dogs remain essential to the reservoir of infection. Continued dog vaccination alongside improved surveillance would allow a fuller understanding of the role of wildlife in maintaining transmission in this area. Nonetheless, dog vaccination clearly suppressed rabies in both domestic dog and wildlife populations, reducing both public health and conservation risks and, if sustained, has potential to eliminate rabies from this region.
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Affiliation(s)
- Kennedy Lushasi
- Ifakara Health Institute, Ifakara, Tanzania
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK
- Nelson Mandela African Institution of Science and Technology, Arusha, Tanzania
| | - Sarah Hayes
- Department of Infectious Disease Epidemiology, Faculty of Medicine, School of Public Health, Imperial College London
| | - Elaine A. Ferguson
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK
| | | | - Sarah Cleaveland
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK
| | - Nicodem J. Govella
- Ifakara Health Institute, Ifakara, Tanzania
- Nelson Mandela African Institution of Science and Technology, Arusha, Tanzania
| | - Daniel T. Haydon
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK
| | | | - Geofrey J. Mchau
- Ministry of Health, Community Development, Gender, Elderly and Children, Dodoma, Tanzania
| | - Emmanuel A. Mpolya
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK
- Nelson Mandela African Institution of Science and Technology, Arusha, Tanzania
| | | | - Hezron E. Nonga
- Ministry of Livestock Development and Fisheries, Dodoma, Tanzania
| | - Rachel Steenson
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK
| | | | - Christl A. Donnelly
- Department of Infectious Disease Epidemiology, Faculty of Medicine, School of Public Health, Imperial College London
- Department of Statistics, University of Oxford, Oxford, UK
| | - Katie Hampson
- Ifakara Health Institute, Ifakara, Tanzania
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK
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19
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Desai A, Nouvellet P, Bhatia S, Cori A, Lassmann B. Data journalism and the COVID-19 pandemic: opportunities and challenges. Lancet Digit Health 2021; 3:e619-e621. [PMID: 34556290 PMCID: PMC8452266 DOI: 10.1016/s2589-7500(21)00178-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 07/09/2021] [Accepted: 08/02/2021] [Indexed: 02/05/2023]
Affiliation(s)
- Angel Desai
- Department of Internal Medicine, Division of Infectious Diseases, University of California Davis, Sacramento, CA 95817, USA.
| | | | - Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
| | - Britta Lassmann
- International Society for Infectious Diseases, Brookline, MA, USA
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20
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Gold S, Donnelly CA, Woodroffe R, Nouvellet P. Modelling the influence of naturally acquired immunity from subclinical infection on outbreak dynamics and persistence of rabies in domestic dogs. PLoS Negl Trop Dis 2021; 15:e0009581. [PMID: 34283827 PMCID: PMC8330898 DOI: 10.1371/journal.pntd.0009581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 08/03/2021] [Accepted: 06/21/2021] [Indexed: 12/04/2022] Open
Abstract
A number of mathematical models have been developed for canine rabies to explore dynamics and inform control strategies. A common assumption of these models is that naturally acquired immunity plays no role in rabies dynamics. However, empirical studies have detected rabies-specific antibodies in healthy, unvaccinated domestic dogs, potentially due to immunizing, non-lethal exposure. We developed a stochastic model for canine rabies, parameterised for Laikipia County, Kenya, to explore the implications of different scenarios for naturally acquired immunity to rabies in domestic dogs. Simulating these scenarios using a non-spatial model indicated that low levels of immunity can act to limit rabies incidence and prevent depletion of the domestic dog population, increasing the probability of disease persistence. However, incorporating spatial structure and human response to high rabies incidence allowed the virus to persist in the absence of immunity. While low levels of immunity therefore had limited influence under a more realistic approximation of rabies dynamics, high rates of exposure leading to immunizing non-lethal exposure were required to produce population-level seroprevalences comparable with those reported in empirical studies. False positives and/or spatial variation may contribute to high empirical seroprevalences. However, if high seroprevalences are related to high exposure rates, these findings support the need for high vaccination coverage to effectively control this disease.
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Affiliation(s)
- Susannah Gold
- Institute of Zoology, Zoological Society of London, London, United Kingdom
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
| | - Christl A. Donnelly
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
- Department of Statistics, University of Oxford, Oxfordshire, United Kingdom
| | - Rosie Woodroffe
- Institute of Zoology, Zoological Society of London, London, United Kingdom
| | - Pierre Nouvellet
- School of Life Sciences, University of Sussex, Falmer, Brighton, United Kingdom
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21
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Djaafara BA, Whittaker C, Watson OJ, Verity R, Brazeau NF, Widyastuti, Oktavia D, Adrian V, Salama N, Bhatia S, Nouvellet P, Sherrard-Smith E, Churcher TS, Surendra H, Lina RN, Ekawati LL, Lestari KD, Andrianto A, Thwaites G, Baird JK, Ghani AC, Elyazar IRF, Walker PGT. Using syndromic measures of mortality to capture the dynamics of COVID-19 in Java, Indonesia, in the context of vaccination rollout. BMC Med 2021; 19:146. [PMID: 34144715 PMCID: PMC8212796 DOI: 10.1186/s12916-021-02016-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 05/26/2021] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND As in many countries, quantifying COVID-19 spread in Indonesia remains challenging due to testing limitations. In Java, non-pharmaceutical interventions (NPIs) were implemented throughout 2020. However, as a vaccination campaign launches, cases and deaths are rising across the island. METHODS We used modelling to explore the extent to which data on burials in Jakarta using strict COVID-19 protocols (C19P) provide additional insight into the transmissibility of the disease, epidemic trajectory, and the impact of NPIs. We assess how implementation of NPIs in early 2021 will shape the epidemic during the period of likely vaccine rollout. RESULTS C19P burial data in Jakarta suggest a death toll approximately 3.3 times higher than reported. Transmission estimates using these data suggest earlier, larger, and more sustained impact of NPIs. Measures to reduce sub-national spread, particularly during Ramadan, substantially mitigated spread to more vulnerable rural areas. Given current trajectory, daily cases and deaths are likely to increase in most regions as the vaccine is rolled out. Transmission may peak in early 2021 in Jakarta if current levels of control are maintained. However, relaxation of control measures is likely to lead to a subsequent resurgence in the absence of an effective vaccination campaign. CONCLUSIONS Syndromic measures of mortality provide a more complete picture of COVID-19 severity upon which to base decision-making. The high potential impact of the vaccine in Java is attributable to reductions in transmission to date and dependent on these being maintained. Increases in control in the relatively short-term will likely yield large, synergistic increases in vaccine impact.
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Affiliation(s)
- Bimandra A Djaafara
- MRC Centre for Global Infectious Disease Analysis and the Abdul Latif Jameel Institute for Disease and Emergency Analytics, School of Public Health, Imperial College London St Mary's Campus, Norfolk Place, London, W2 1PG, UK.
- Eijkman-Oxford Clinical Research Unit, Jakarta, Indonesia.
| | - Charles Whittaker
- MRC Centre for Global Infectious Disease Analysis and the Abdul Latif Jameel Institute for Disease and Emergency Analytics, School of Public Health, Imperial College London St Mary's Campus, Norfolk Place, London, W2 1PG, UK
| | - Oliver J Watson
- MRC Centre for Global Infectious Disease Analysis and the Abdul Latif Jameel Institute for Disease and Emergency Analytics, School of Public Health, Imperial College London St Mary's Campus, Norfolk Place, London, W2 1PG, UK
| | - Robert Verity
- MRC Centre for Global Infectious Disease Analysis and the Abdul Latif Jameel Institute for Disease and Emergency Analytics, School of Public Health, Imperial College London St Mary's Campus, Norfolk Place, London, W2 1PG, UK
| | - Nicholas F Brazeau
- MRC Centre for Global Infectious Disease Analysis and the Abdul Latif Jameel Institute for Disease and Emergency Analytics, School of Public Health, Imperial College London St Mary's Campus, Norfolk Place, London, W2 1PG, UK
| | - Widyastuti
- Jakarta Provincial Department of Health, Jakarta, Indonesia
| | - Dwi Oktavia
- Jakarta Provincial Department of Health, Jakarta, Indonesia
| | - Verry Adrian
- Jakarta Provincial Department of Health, Jakarta, Indonesia
| | - Ngabila Salama
- Jakarta Provincial Department of Health, Jakarta, Indonesia
| | - Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis and the Abdul Latif Jameel Institute for Disease and Emergency Analytics, School of Public Health, Imperial College London St Mary's Campus, Norfolk Place, London, W2 1PG, UK
| | - Pierre Nouvellet
- MRC Centre for Global Infectious Disease Analysis and the Abdul Latif Jameel Institute for Disease and Emergency Analytics, School of Public Health, Imperial College London St Mary's Campus, Norfolk Place, London, W2 1PG, UK
- School of Life Sciences, University of Sussex, Brighton, UK
| | - Ellie Sherrard-Smith
- MRC Centre for Global Infectious Disease Analysis and the Abdul Latif Jameel Institute for Disease and Emergency Analytics, School of Public Health, Imperial College London St Mary's Campus, Norfolk Place, London, W2 1PG, UK
| | - Thomas S Churcher
- MRC Centre for Global Infectious Disease Analysis and the Abdul Latif Jameel Institute for Disease and Emergency Analytics, School of Public Health, Imperial College London St Mary's Campus, Norfolk Place, London, W2 1PG, UK
| | - Henry Surendra
- Eijkman-Oxford Clinical Research Unit, Jakarta, Indonesia
- Centre for Tropical Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Rosa N Lina
- Eijkman-Oxford Clinical Research Unit, Jakarta, Indonesia
| | | | | | - Adhi Andrianto
- Eijkman-Oxford Clinical Research Unit, Jakarta, Indonesia
| | - Guy Thwaites
- Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - J Kevin Baird
- Eijkman-Oxford Clinical Research Unit, Jakarta, Indonesia
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Azra C Ghani
- MRC Centre for Global Infectious Disease Analysis and the Abdul Latif Jameel Institute for Disease and Emergency Analytics, School of Public Health, Imperial College London St Mary's Campus, Norfolk Place, London, W2 1PG, UK
| | | | - Patrick G T Walker
- MRC Centre for Global Infectious Disease Analysis and the Abdul Latif Jameel Institute for Disease and Emergency Analytics, School of Public Health, Imperial College London St Mary's Campus, Norfolk Place, London, W2 1PG, UK
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Charniga K, Cucunubá ZM, Walteros DM, Mercado M, Prieto F, Ospina M, Nouvellet P, Donnelly CA. Descriptive analysis of surveillance data for Zika virus disease and Zika virus-associated neurological complications in Colombia, 2015-2017. PLoS One 2021; 16:e0252236. [PMID: 34133446 PMCID: PMC8208586 DOI: 10.1371/journal.pone.0252236] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 05/11/2021] [Indexed: 11/19/2022] Open
Abstract
Zika virus (ZIKV) is a mosquito-borne pathogen that recently caused a major epidemic in the Americas. Although the majority of ZIKV infections are asymptomatic, the virus has been associated with birth defects in fetuses and newborns of infected mothers as well as neurological complications in adults. We performed a descriptive analysis on approximately 106,000 suspected and laboratory-confirmed cases of Zika virus disease (ZVD) that were reported during the 2015-2017 epidemic in Colombia. We also analyzed a dataset containing patients with neurological complications and recent febrile illness compatible with ZVD. Females had higher cumulative incidence of ZVD than males. Compared to the general population, cases were more likely to be reported in young adults (20 to 39 years of age). We estimated the cumulative incidence of ZVD in pregnant females at 3,120 reported cases per 100,000 population (95% CI: 3,077-3,164), which was considerably higher than the incidence in both males and non-pregnant females. ZVD cases were reported in all 32 departments. Four-hundred and eighteen patients suffered from ZIKV-associated neurological complications, of which 85% were diagnosed with Guillain-Barré syndrome. The median age of ZIKV cases with neurological complications was 12 years older than that of ZVD cases. ZIKV-associated neurological complications increased with age, and the highest incidence was reported among individuals aged 75 and older. Even though neurological complications and deaths due to ZIKV were rare in this epidemic, better risk communication is needed for people living in or traveling to ZIKV-affected areas.
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Affiliation(s)
- Kelly Charniga
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Zulma M Cucunubá
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | | | | | | | | | - Pierre Nouvellet
- School of Life Sciences, University of Sussex, Brighton, United Kingdom
| | - Christl A Donnelly
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
- Department of Statistics, University of Oxford, Oxford, United Kingdom
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23
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Bhatia S, Lassmann B, Cohn E, Desai AN, Carrion M, Kraemer MUG, Herringer M, Brownstein J, Madoff L, Cori A, Nouvellet P. Using digital surveillance tools for near real-time mapping of the risk of infectious disease spread. NPJ Digit Med 2021; 4:73. [PMID: 33864009 PMCID: PMC8052406 DOI: 10.1038/s41746-021-00442-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 03/16/2021] [Indexed: 02/02/2023] Open
Abstract
Data from digital disease surveillance tools such as ProMED and HealthMap can complement the field surveillance during ongoing outbreaks. Our aim was to investigate the use of data collected through ProMED and HealthMap in real-time outbreak analysis. We developed a flexible statistical model to quantify spatial heterogeneity in the risk of spread of an outbreak and to forecast short term incidence trends. The model was applied retrospectively to data collected by ProMED and HealthMap during the 2013-2016 West African Ebola epidemic and for comparison, to WHO data. Using ProMED and HealthMap data, the model was able to robustly quantify the risk of disease spread 1-4 weeks in advance and for countries at risk of case importations, quantify where this risk comes from. Our study highlights that ProMED and HealthMap data could be used in real-time to quantify the spatial heterogeneity in risk of spread of an outbreak.
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Affiliation(s)
- Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, Faculty of Medicine, London, UK.
| | - Britta Lassmann
- ProMED, International Society for Infectious Diseases, Brookline, MA, USA
| | - Emily Cohn
- Computational Epidemiology Group, Division of Emergency Medicine, Boston Children's Hospital, Boston, MA, USA
| | - Angel N Desai
- ProMED, International Society for Infectious Diseases, Brookline, MA, USA
| | - Malwina Carrion
- ProMED, International Society for Infectious Diseases, Brookline, MA, USA
- Department of Health Science, Sargent College, Boston University, Boston, MA, USA
| | - Moritz U G Kraemer
- Computational Epidemiology Group, Division of Emergency Medicine, Boston Children's Hospital, Boston, MA, USA
- Department of Zoology, Tinbergen Building, Oxford University, Oxford, UK
- Department of Pediatrics, Harvard Medical School, Boston, USA
| | | | - John Brownstein
- Computational Epidemiology Group, Division of Emergency Medicine, Boston Children's Hospital, Boston, MA, USA
| | - Larry Madoff
- ProMED, International Society for Infectious Diseases, Brookline, MA, USA
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, Faculty of Medicine, London, UK
| | - Pierre Nouvellet
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, Faculty of Medicine, London, UK
- School of Life Sciences, University of Sussex, Brighton, UK
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24
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Nouvellet P, Bhatia S, Cori A, Ainslie KEC, Baguelin M, Bhatt S, Boonyasiri A, Brazeau NF, Cattarino L, Cooper LV, Coupland H, Cucunuba ZM, Cuomo-Dannenburg G, Dighe A, Djaafara BA, Dorigatti I, Eales OD, van Elsland SL, Nascimento FF, FitzJohn RG, Gaythorpe KAM, Geidelberg L, Green WD, Hamlet A, Hauck K, Hinsley W, Imai N, Jeffrey B, Knock E, Laydon DJ, Lees JA, Mangal T, Mellan TA, Nedjati-Gilani G, Parag KV, Pons-Salort M, Ragonnet-Cronin M, Riley S, Unwin HJT, Verity R, Vollmer MAC, Volz E, Walker PGT, Walters CE, Wang H, Watson OJ, Whittaker C, Whittles LK, Xi X, Ferguson NM, Donnelly CA. Reduction in mobility and COVID-19 transmission. Nat Commun 2021; 12:1090. [PMID: 33597546 PMCID: PMC7889876 DOI: 10.1038/s41467-021-21358-2] [Citation(s) in RCA: 252] [Impact Index Per Article: 84.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 01/13/2021] [Indexed: 01/02/2023] Open
Abstract
In response to the COVID-19 pandemic, countries have sought to control SARS-CoV-2 transmission by restricting population movement through social distancing interventions, thus reducing the number of contacts. Mobility data represent an important proxy measure of social distancing, and here, we characterise the relationship between transmission and mobility for 52 countries around the world. Transmission significantly decreased with the initial reduction in mobility in 73% of the countries analysed, but we found evidence of decoupling of transmission and mobility following the relaxation of strict control measures for 80% of countries. For the majority of countries, mobility explained a substantial proportion of the variation in transmissibility (median adjusted R-squared: 48%, interquartile range - IQR - across countries [27-77%]). Where a change in the relationship occurred, predictive ability decreased after the relaxation; from a median adjusted R-squared of 74% (IQR across countries [49-91%]) pre-relaxation, to a median adjusted R-squared of 30% (IQR across countries [12-48%]) post-relaxation. In countries with a clear relationship between mobility and transmission both before and after strict control measures were relaxed, mobility was associated with lower transmission rates after control measures were relaxed indicating that the beneficial effects of ongoing social distancing behaviours were substantial.
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Affiliation(s)
- Pierre Nouvellet
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK.
- School of Life Sciences, University of Sussex, Brighton, UK.
| | - Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Kylie E C Ainslie
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Marc Baguelin
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Samir Bhatt
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Adhiratha Boonyasiri
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Nicholas F Brazeau
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Lorenzo Cattarino
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Laura V Cooper
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Helen Coupland
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Zulma M Cucunuba
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Gina Cuomo-Dannenburg
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Amy Dighe
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Bimandra A Djaafara
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Ilaria Dorigatti
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Oliver D Eales
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Sabine L van Elsland
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Fabricia F Nascimento
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Richard G FitzJohn
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Katy A M Gaythorpe
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Lily Geidelberg
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - William D Green
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Arran Hamlet
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Katharina Hauck
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Wes Hinsley
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Natsuko Imai
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Benjamin Jeffrey
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Edward Knock
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Daniel J Laydon
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - John A Lees
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Tara Mangal
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Thomas A Mellan
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Gemma Nedjati-Gilani
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Kris V Parag
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Margarita Pons-Salort
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Manon Ragonnet-Cronin
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - H Juliette T Unwin
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Robert Verity
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Michaela A C Vollmer
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Erik Volz
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Patrick G T Walker
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Caroline E Walters
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Haowei Wang
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Oliver J Watson
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Charles Whittaker
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Lilith K Whittles
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Xiaoyue Xi
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Neil M Ferguson
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK
| | - Christl A Donnelly
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, UK.
- Department of Statistics, University of Oxford, Oxford, UK.
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25
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Fu H, Wang H, Xi X, Boonyasiri A, Wang Y, Hinsley W, Fraser KJ, McCabe R, Olivera Mesa D, Skarp J, Ledda A, Dewé T, Dighe A, Winskill P, van Elsland SL, Ainslie KEC, Baguelin M, Bhatt S, Boyd O, Brazeau NF, Cattarino L, Charles G, Coupland H, Cucunuba ZM, Cuomo-Dannenburg G, Donnelly CA, Dorigatti I, Eales OD, FitzJohn RG, Flaxman S, Gaythorpe KAM, Ghani AC, Green WD, Hamlet A, Hauck K, Haw DJ, Jeffrey B, Laydon DJ, Lees JA, Mellan T, Mishra S, Nedjati-Gilani G, Nouvellet P, Okell L, Parag KV, Ragonnet-Cronin M, Riley S, Schmit N, Thompson HA, Unwin HJT, Verity R, Vollmer MAC, Volz E, Walker PGT, Walters CE, Watson OJ, Whittaker C, Whittles LK, Imai N, Bhatia S, Ferguson NM. Database of epidemic trends and control measures during the first wave of COVID-19 in mainland China. Int J Infect Dis 2021; 102:463-471. [PMID: 33130212 PMCID: PMC7603985 DOI: 10.1016/j.ijid.2020.10.075] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 10/20/2020] [Accepted: 10/23/2020] [Indexed: 02/06/2023] Open
Abstract
OBJECTIVES In this data collation study, we aimed to provide a comprehensive database describing the epidemic trends and responses during the first wave of coronavirus disease 2019 (COVID-19) throughout the main provinces in China. METHODS From mid-January to March 2020, we extracted publicly available data regarding the spread and control of COVID-19 from 31 provincial health authorities and major media outlets in mainland China. Based on these data, we conducted descriptive analyses of the epidemic in the six most-affected provinces. RESULTS School closures, travel restrictions, community-level lockdown, and contact tracing were introduced concurrently around late January but subsequent epidemic trends differed among provinces. Compared with Hubei, the other five most-affected provinces reported a lower crude case fatality ratio and proportion of critical and severe hospitalised cases. From March 2020, as the local transmission of COVID-19 declined, switching the focus of measures to the testing and quarantine of inbound travellers may have helped to sustain the control of the epidemic. CONCLUSIONS Aggregated indicators of case notifications and severity distributions are essential for monitoring an epidemic. A publicly available database containing these indicators and information regarding control measures is a useful resource for further research and policy planning in response to the COVID-19 epidemic.
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Affiliation(s)
- Han Fu
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK.
| | - Haowei Wang
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Xiaoyue Xi
- Department of Mathematics, Imperial College London, London, UK
| | - Adhiratha Boonyasiri
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK
| | - Yuanrong Wang
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Wes Hinsley
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Keith J Fraser
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Ruth McCabe
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Daniela Olivera Mesa
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Janetta Skarp
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Alice Ledda
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Tamsin Dewé
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Amy Dighe
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Peter Winskill
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Sabine L van Elsland
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Kylie E C Ainslie
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Marc Baguelin
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Samir Bhatt
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Olivia Boyd
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Nicholas F Brazeau
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Lorenzo Cattarino
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Giovanni Charles
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Helen Coupland
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Zulma M Cucunuba
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Gina Cuomo-Dannenburg
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Christl A Donnelly
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK; Department of Statistics, University of Oxford, Oxford, UK
| | - Ilaria Dorigatti
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Oliver D Eales
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Richard G FitzJohn
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Seth Flaxman
- Department of Mathematics, Imperial College London, London, UK
| | - Katy A M Gaythorpe
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Azra C Ghani
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - William D Green
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Arran Hamlet
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Katharina Hauck
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - David J Haw
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Benjamin Jeffrey
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Daniel J Laydon
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - John A Lees
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Thomas Mellan
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Swapnil Mishra
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Gemma Nedjati-Gilani
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Pierre Nouvellet
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK; School of Life Sciences, University of Sussex, Brighton, UK
| | - Lucy Okell
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Kris V Parag
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Manon Ragonnet-Cronin
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Nora Schmit
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Hayley A Thompson
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - H Juliette T Unwin
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Robert Verity
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Michaela A C Vollmer
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Erik Volz
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Patrick G T Walker
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Caroline E Walters
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Oliver J Watson
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Charles Whittaker
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Lilith K Whittles
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Natsuko Imai
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Neil M Ferguson
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
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26
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Thompson HA, Imai N, Dighe A, Ainslie KEC, Baguelin M, Bhatia S, Bhatt S, Boonyasiri A, Boyd O, Brazeau NF, Cattarino L, Cooper LV, Coupland H, Cucunuba Z, Cuomo-Dannenburg G, Djaafara B, Dorigatti I, van Elsland S, FitzJohn R, Fu H, Gaythorpe KAM, Green W, Hallett T, Hamlet A, Haw D, Hayes S, Hinsley W, Jeffrey B, Knock E, Laydon DJ, Lees J, Mangal TD, Mellan T, Mishra S, Mousa A, Nedjati-Gilani G, Nouvellet P, Okell L, Parag KV, Ragonnet-Cronin M, Riley S, Unwin HJT, Verity R, Vollmer M, Volz E, Walker PGT, Walters C, Wang H, Wang Y, Watson OJ, Whittaker C, Whittles LK, Winskill P, Xi X, Donnelly CA, Ferguson NM. SARS-CoV-2 infection prevalence on repatriation flights from Wuhan City, China. J Travel Med 2020; 27:5896041. [PMID: 32830853 PMCID: PMC7499665 DOI: 10.1093/jtm/taaa135] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Accepted: 08/14/2020] [Indexed: 11/14/2022]
Affiliation(s)
- Hayley A Thompson
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Natsuko Imai
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Amy Dighe
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Kylie E C Ainslie
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Marc Baguelin
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Samir Bhatt
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Adhiratha Boonyasiri
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK
| | - Olivia Boyd
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Nicholas F Brazeau
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Lorenzo Cattarino
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Laura V Cooper
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Helen Coupland
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Zulma Cucunuba
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Gina Cuomo-Dannenburg
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Bimandra Djaafara
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Ilaria Dorigatti
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Sabine van Elsland
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Richard FitzJohn
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Han Fu
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Katy A M Gaythorpe
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Will Green
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Timothy Hallett
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Arran Hamlet
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - David Haw
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Sarah Hayes
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Wes Hinsley
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Benjamin Jeffrey
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Edward Knock
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Daniel J Laydon
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - John Lees
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Tara D Mangal
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Thomas Mellan
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Swapnil Mishra
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Andria Mousa
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Gemma Nedjati-Gilani
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Pierre Nouvellet
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.,School of Life Sciences, University of Sussex, Sussex, UK
| | - Lucy Okell
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Kris V Parag
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Manon Ragonnet-Cronin
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - H Juliette T Unwin
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Robert Verity
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Michaela Vollmer
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Erik Volz
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Patrick G T Walker
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Caroline Walters
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Haowei Wang
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Yuanrong Wang
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Oliver J Watson
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Charles Whittaker
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Lilith K Whittles
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Peter Winskill
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Xiaoyue Xi
- Department of Mathematics, Imperial College London, London, UK
| | - Christl A Donnelly
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.,Department of Statistics, University of Oxford, Oxford, UK
| | - Neil M Ferguson
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
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27
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Unwin HJT, Mishra S, Bradley VC, Gandy A, Mellan TA, Coupland H, Ish-Horowicz J, Vollmer MAC, Whittaker C, Filippi SL, Xi X, Monod M, Ratmann O, Hutchinson M, Valka F, Zhu H, Hawryluk I, Milton P, Ainslie KEC, Baguelin M, Boonyasiri A, Brazeau NF, Cattarino L, Cucunuba Z, Cuomo-Dannenburg G, Dorigatti I, Eales OD, Eaton JW, van Elsland SL, FitzJohn RG, Gaythorpe KAM, Green W, Hinsley W, Jeffrey B, Knock E, Laydon DJ, Lees J, Nedjati-Gilani G, Nouvellet P, Okell L, Parag KV, Siveroni I, Thompson HA, Walker P, Walters CE, Watson OJ, Whittles LK, Ghani AC, Ferguson NM, Riley S, Donnelly CA, Bhatt S, Flaxman S. State-level tracking of COVID-19 in the United States. Nat Commun 2020. [PMID: 33273462 DOI: 10.25561/78677] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2023] Open
Abstract
As of 1st June 2020, the US Centres for Disease Control and Prevention reported 104,232 confirmed or probable COVID-19-related deaths in the US. This was more than twice the number of deaths reported in the next most severely impacted country. We jointly model the US epidemic at the state-level, using publicly available death data within a Bayesian hierarchical semi-mechanistic framework. For each state, we estimate the number of individuals that have been infected, the number of individuals that are currently infectious and the time-varying reproduction number (the average number of secondary infections caused by an infected person). We use changes in mobility to capture the impact that non-pharmaceutical interventions and other behaviour changes have on the rate of transmission of SARS-CoV-2. We estimate that Rt was only below one in 23 states on 1st June. We also estimate that 3.7% [3.4%-4.0%] of the total population of the US had been infected, with wide variation between states, and approximately 0.01% of the population was infectious. We demonstrate good 3 week model forecasts of deaths with low error and good coverage of our credible intervals.
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Affiliation(s)
- H Juliette T Unwin
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK.
| | - Swapnil Mishra
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | | | - Axel Gandy
- Department of Mathematics, Imperial College, London, UK
| | - Thomas A Mellan
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Helen Coupland
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | | | - Michaela A C Vollmer
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Charles Whittaker
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | | | - Xiaoyue Xi
- Department of Mathematics, Imperial College, London, UK
| | - Mélodie Monod
- Department of Mathematics, Imperial College, London, UK
| | | | | | | | - Harrison Zhu
- Department of Mathematics, Imperial College, London, UK
| | - Iwona Hawryluk
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Philip Milton
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Kylie E C Ainslie
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Marc Baguelin
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Adhiratha Boonyasiri
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK
| | - Nick F Brazeau
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Lorenzo Cattarino
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Zulma Cucunuba
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Gina Cuomo-Dannenburg
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Ilaria Dorigatti
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Oliver D Eales
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Jeffrey W Eaton
- MRC Centre for Global Infectious Disease Analysis, Imperial College, London, UK
| | - Sabine L van Elsland
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Richard G FitzJohn
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Katy A M Gaythorpe
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - William Green
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Wes Hinsley
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Benjamin Jeffrey
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Edward Knock
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Daniel J Laydon
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - John Lees
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Gemma Nedjati-Gilani
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Pierre Nouvellet
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
- School of Life Sciences, University of Sussex, Brighton, UK
| | - Lucy Okell
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Kris V Parag
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Igor Siveroni
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Hayley A Thompson
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Patrick Walker
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Caroline E Walters
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Oliver J Watson
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
- Department of Laboratory Medicine and Pathology, Brown University, Providence, RI, USA
| | - Lilith K Whittles
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Azra C Ghani
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Neil M Ferguson
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Christl A Donnelly
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | - Samir Bhatt
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK.
| | - Seth Flaxman
- Department of Mathematics, Imperial College, London, UK.
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28
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Unwin HJT, Mishra S, Bradley VC, Gandy A, Mellan TA, Coupland H, Ish-Horowicz J, Vollmer MAC, Whittaker C, Filippi SL, Xi X, Monod M, Ratmann O, Hutchinson M, Valka F, Zhu H, Hawryluk I, Milton P, Ainslie KEC, Baguelin M, Boonyasiri A, Brazeau NF, Cattarino L, Cucunuba Z, Cuomo-Dannenburg G, Dorigatti I, Eales OD, Eaton JW, van Elsland SL, FitzJohn RG, Gaythorpe KAM, Green W, Hinsley W, Jeffrey B, Knock E, Laydon DJ, Lees J, Nedjati-Gilani G, Nouvellet P, Okell L, Parag KV, Siveroni I, Thompson HA, Walker P, Walters CE, Watson OJ, Whittles LK, Ghani AC, Ferguson NM, Riley S, Donnelly CA, Bhatt S, Flaxman S. State-level tracking of COVID-19 in the United States. Nat Commun 2020; 11:6189. [PMID: 33273462 PMCID: PMC7712910 DOI: 10.1038/s41467-020-19652-6] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 10/15/2020] [Indexed: 02/07/2023] Open
Abstract
As of 1st June 2020, the US Centres for Disease Control and Prevention reported 104,232 confirmed or probable COVID-19-related deaths in the US. This was more than twice the number of deaths reported in the next most severely impacted country. We jointly model the US epidemic at the state-level, using publicly available death data within a Bayesian hierarchical semi-mechanistic framework. For each state, we estimate the number of individuals that have been infected, the number of individuals that are currently infectious and the time-varying reproduction number (the average number of secondary infections caused by an infected person). We use changes in mobility to capture the impact that non-pharmaceutical interventions and other behaviour changes have on the rate of transmission of SARS-CoV-2. We estimate that Rt was only below one in 23 states on 1st June. We also estimate that 3.7% [3.4%-4.0%] of the total population of the US had been infected, with wide variation between states, and approximately 0.01% of the population was infectious. We demonstrate good 3 week model forecasts of deaths with low error and good coverage of our credible intervals.
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Affiliation(s)
- H Juliette T Unwin
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK.
| | - Swapnil Mishra
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | | | - Axel Gandy
- Department of Mathematics, Imperial College, London, UK
| | - Thomas A Mellan
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Helen Coupland
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | | | - Michaela A C Vollmer
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Charles Whittaker
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | | | - Xiaoyue Xi
- Department of Mathematics, Imperial College, London, UK
| | - Mélodie Monod
- Department of Mathematics, Imperial College, London, UK
| | | | | | | | - Harrison Zhu
- Department of Mathematics, Imperial College, London, UK
| | - Iwona Hawryluk
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Philip Milton
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Kylie E C Ainslie
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Marc Baguelin
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Adhiratha Boonyasiri
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK
| | - Nick F Brazeau
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Lorenzo Cattarino
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Zulma Cucunuba
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Gina Cuomo-Dannenburg
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Ilaria Dorigatti
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Oliver D Eales
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Jeffrey W Eaton
- MRC Centre for Global Infectious Disease Analysis, Imperial College, London, UK
| | - Sabine L van Elsland
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Richard G FitzJohn
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Katy A M Gaythorpe
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - William Green
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Wes Hinsley
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Benjamin Jeffrey
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Edward Knock
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Daniel J Laydon
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - John Lees
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Gemma Nedjati-Gilani
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Pierre Nouvellet
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
- School of Life Sciences, University of Sussex, Brighton, UK
| | - Lucy Okell
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Kris V Parag
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Igor Siveroni
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Hayley A Thompson
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Patrick Walker
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Caroline E Walters
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Oliver J Watson
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
- Department of Laboratory Medicine and Pathology, Brown University, Providence, RI, USA
| | - Lilith K Whittles
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Azra C Ghani
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Neil M Ferguson
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Christl A Donnelly
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | - Samir Bhatt
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK.
| | - Seth Flaxman
- Department of Mathematics, Imperial College, London, UK.
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Unwin HJT, Mishra S, Bradley VC, Gandy A, Mellan TA, Coupland H, Ish-Horowicz J, Vollmer MAC, Whittaker C, Filippi SL, Xi X, Monod M, Ratmann O, Hutchinson M, Valka F, Zhu H, Hawryluk I, Milton P, Ainslie KEC, Baguelin M, Boonyasiri A, Brazeau NF, Cattarino L, Cucunuba Z, Cuomo-Dannenburg G, Dorigatti I, Eales OD, Eaton JW, van Elsland SL, FitzJohn RG, Gaythorpe KAM, Green W, Hinsley W, Jeffrey B, Knock E, Laydon DJ, Lees J, Nedjati-Gilani G, Nouvellet P, Okell L, Parag KV, Siveroni I, Thompson HA, Walker P, Walters CE, Watson OJ, Whittles LK, Ghani AC, Ferguson NM, Riley S, Donnelly CA, Bhatt S, Flaxman S. State-level tracking of COVID-19 in the United States. Nat Commun 2020. [PMID: 33273462 DOI: 10.25561/79231] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/22/2023] Open
Abstract
As of 1st June 2020, the US Centres for Disease Control and Prevention reported 104,232 confirmed or probable COVID-19-related deaths in the US. This was more than twice the number of deaths reported in the next most severely impacted country. We jointly model the US epidemic at the state-level, using publicly available death data within a Bayesian hierarchical semi-mechanistic framework. For each state, we estimate the number of individuals that have been infected, the number of individuals that are currently infectious and the time-varying reproduction number (the average number of secondary infections caused by an infected person). We use changes in mobility to capture the impact that non-pharmaceutical interventions and other behaviour changes have on the rate of transmission of SARS-CoV-2. We estimate that Rt was only below one in 23 states on 1st June. We also estimate that 3.7% [3.4%-4.0%] of the total population of the US had been infected, with wide variation between states, and approximately 0.01% of the population was infectious. We demonstrate good 3 week model forecasts of deaths with low error and good coverage of our credible intervals.
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Affiliation(s)
- H Juliette T Unwin
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK.
| | - Swapnil Mishra
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | | | - Axel Gandy
- Department of Mathematics, Imperial College, London, UK
| | - Thomas A Mellan
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Helen Coupland
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | | | - Michaela A C Vollmer
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Charles Whittaker
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | | | - Xiaoyue Xi
- Department of Mathematics, Imperial College, London, UK
| | - Mélodie Monod
- Department of Mathematics, Imperial College, London, UK
| | | | | | | | - Harrison Zhu
- Department of Mathematics, Imperial College, London, UK
| | - Iwona Hawryluk
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Philip Milton
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Kylie E C Ainslie
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Marc Baguelin
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Adhiratha Boonyasiri
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK
| | - Nick F Brazeau
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Lorenzo Cattarino
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Zulma Cucunuba
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Gina Cuomo-Dannenburg
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Ilaria Dorigatti
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Oliver D Eales
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Jeffrey W Eaton
- MRC Centre for Global Infectious Disease Analysis, Imperial College, London, UK
| | - Sabine L van Elsland
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Richard G FitzJohn
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Katy A M Gaythorpe
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - William Green
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Wes Hinsley
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Benjamin Jeffrey
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Edward Knock
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Daniel J Laydon
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - John Lees
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Gemma Nedjati-Gilani
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Pierre Nouvellet
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
- School of Life Sciences, University of Sussex, Brighton, UK
| | - Lucy Okell
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Kris V Parag
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Igor Siveroni
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Hayley A Thompson
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Patrick Walker
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Caroline E Walters
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Oliver J Watson
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
- Department of Laboratory Medicine and Pathology, Brown University, Providence, RI, USA
| | - Lilith K Whittles
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Azra C Ghani
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Neil M Ferguson
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
| | - Christl A Donnelly
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | - Samir Bhatt
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College, London, UK.
| | - Seth Flaxman
- Department of Mathematics, Imperial College, London, UK.
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30
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Dighe A, Cattarino L, Cuomo-Dannenburg G, Skarp J, Imai N, Bhatia S, Gaythorpe KAM, Ainslie KEC, Baguelin M, Bhatt S, Boonyasiri A, Brazeau NF, Cooper LV, Coupland H, Cucunuba Z, Dorigatti I, Eales OD, van Elsland SL, FitzJohn RG, Green WD, Haw DJ, Hinsley W, Knock E, Laydon DJ, Mellan T, Mishra S, Nedjati-Gilani G, Nouvellet P, Pons-Salort M, Thompson HA, Unwin HJT, Verity R, Vollmer MAC, Walters CE, Watson OJ, Whittaker C, Whittles LK, Ghani AC, Donnelly CA, Ferguson NM, Riley S. Response to COVID-19 in South Korea and implications for lifting stringent interventions. BMC Med 2020. [PMID: 33032601 DOI: 10.25561/79388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/02/2023] Open
Abstract
BACKGROUND After experiencing a sharp growth in COVID-19 cases early in the pandemic, South Korea rapidly controlled transmission while implementing less stringent national social distancing measures than countries in Europe and the USA. This has led to substantial interest in their "test, trace, isolate" strategy. However, it is important to understand the epidemiological peculiarities of South Korea's outbreak and characterise their response before attempting to emulate these measures elsewhere. METHODS We systematically extracted numbers of suspected cases tested, PCR-confirmed cases, deaths, isolated confirmed cases, and numbers of confirmed cases with an identified epidemiological link from publicly available data. We estimated the time-varying reproduction number, Rt, using an established Bayesian framework, and reviewed the package of interventions implemented by South Korea using our extracted data, plus published literature and government sources. RESULTS We estimated that after the initial rapid growth in cases, Rt dropped below one in early April before increasing to a maximum of 1.94 (95%CrI, 1.64-2.27) in May following outbreaks in Seoul Metropolitan Region. By mid-June, Rt was back below one where it remained until the end of our study (July 13th). Despite less stringent "lockdown" measures, strong social distancing measures were implemented in high-incidence areas and studies measured a considerable national decrease in movement in late February. Testing the capacity was swiftly increased, and protocols were in place to isolate suspected and confirmed cases quickly; however, we could not estimate the delay to isolation using our data. Accounting for just 10% of cases, individual case-based contact tracing picked up a relatively minor proportion of total cases, with cluster investigations accounting for 66%. CONCLUSIONS Whilst early adoption of testing and contact tracing is likely to be important for South Korea's successful outbreak control, other factors including regional implementation of strong social distancing measures likely also contributed. The high volume of testing and the low number of deaths suggest that South Korea experienced a small epidemic relative to other countries. Caution is needed in attempting to replicate the South Korean response in populations with larger more geographically widespread epidemics where finding, testing, and isolating cases that are linked to clusters may be more difficult.
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Affiliation(s)
- Amy Dighe
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK.
| | - Lorenzo Cattarino
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Gina Cuomo-Dannenburg
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Janetta Skarp
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Natsuko Imai
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Katy A M Gaythorpe
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Kylie E C Ainslie
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Marc Baguelin
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Samir Bhatt
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Adhiratha Boonyasiri
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK
| | - Nicholas F Brazeau
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Laura V Cooper
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Helen Coupland
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Zulma Cucunuba
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Ilaria Dorigatti
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Oliver D Eales
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Sabine L van Elsland
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Richard G FitzJohn
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - William D Green
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - David J Haw
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Wes Hinsley
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Edward Knock
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Daniel J Laydon
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Thomas Mellan
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Swapnil Mishra
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Gemma Nedjati-Gilani
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Pierre Nouvellet
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
- School of Life Sciences, University of Sussex, Brighton, UK
| | - Margarita Pons-Salort
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Hayley A Thompson
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - H Juliette T Unwin
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Robert Verity
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Michaela A C Vollmer
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Caroline E Walters
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Oliver J Watson
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
- Department of Laboratory Medicine and Pathology, Brown University, Providence, RI, USA
| | - Charles Whittaker
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Lilith K Whittles
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Azra C Ghani
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Christl A Donnelly
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | - Neil M Ferguson
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK.
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK.
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31
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Dighe A, Cattarino L, Cuomo-Dannenburg G, Skarp J, Imai N, Bhatia S, Gaythorpe KAM, Ainslie KEC, Baguelin M, Bhatt S, Boonyasiri A, Brazeau NF, Cooper LV, Coupland H, Cucunuba Z, Dorigatti I, Eales OD, van Elsland SL, FitzJohn RG, Green WD, Haw DJ, Hinsley W, Knock E, Laydon DJ, Mellan T, Mishra S, Nedjati-Gilani G, Nouvellet P, Pons-Salort M, Thompson HA, Unwin HJT, Verity R, Vollmer MAC, Walters CE, Watson OJ, Whittaker C, Whittles LK, Ghani AC, Donnelly CA, Ferguson NM, Riley S. Response to COVID-19 in South Korea and implications for lifting stringent interventions. BMC Med 2020; 18:321. [PMID: 33032601 PMCID: PMC7544529 DOI: 10.1186/s12916-020-01791-8] [Citation(s) in RCA: 109] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Accepted: 09/22/2020] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND After experiencing a sharp growth in COVID-19 cases early in the pandemic, South Korea rapidly controlled transmission while implementing less stringent national social distancing measures than countries in Europe and the USA. This has led to substantial interest in their "test, trace, isolate" strategy. However, it is important to understand the epidemiological peculiarities of South Korea's outbreak and characterise their response before attempting to emulate these measures elsewhere. METHODS We systematically extracted numbers of suspected cases tested, PCR-confirmed cases, deaths, isolated confirmed cases, and numbers of confirmed cases with an identified epidemiological link from publicly available data. We estimated the time-varying reproduction number, Rt, using an established Bayesian framework, and reviewed the package of interventions implemented by South Korea using our extracted data, plus published literature and government sources. RESULTS We estimated that after the initial rapid growth in cases, Rt dropped below one in early April before increasing to a maximum of 1.94 (95%CrI, 1.64-2.27) in May following outbreaks in Seoul Metropolitan Region. By mid-June, Rt was back below one where it remained until the end of our study (July 13th). Despite less stringent "lockdown" measures, strong social distancing measures were implemented in high-incidence areas and studies measured a considerable national decrease in movement in late February. Testing the capacity was swiftly increased, and protocols were in place to isolate suspected and confirmed cases quickly; however, we could not estimate the delay to isolation using our data. Accounting for just 10% of cases, individual case-based contact tracing picked up a relatively minor proportion of total cases, with cluster investigations accounting for 66%. CONCLUSIONS Whilst early adoption of testing and contact tracing is likely to be important for South Korea's successful outbreak control, other factors including regional implementation of strong social distancing measures likely also contributed. The high volume of testing and the low number of deaths suggest that South Korea experienced a small epidemic relative to other countries. Caution is needed in attempting to replicate the South Korean response in populations with larger more geographically widespread epidemics where finding, testing, and isolating cases that are linked to clusters may be more difficult.
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Affiliation(s)
- Amy Dighe
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK.
| | - Lorenzo Cattarino
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Gina Cuomo-Dannenburg
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Janetta Skarp
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Natsuko Imai
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Katy A M Gaythorpe
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Kylie E C Ainslie
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Marc Baguelin
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Samir Bhatt
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Adhiratha Boonyasiri
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK
| | - Nicholas F Brazeau
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Laura V Cooper
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Helen Coupland
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Zulma Cucunuba
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Ilaria Dorigatti
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Oliver D Eales
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Sabine L van Elsland
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Richard G FitzJohn
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - William D Green
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - David J Haw
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Wes Hinsley
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Edward Knock
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Daniel J Laydon
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Thomas Mellan
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Swapnil Mishra
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Gemma Nedjati-Gilani
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Pierre Nouvellet
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
- School of Life Sciences, University of Sussex, Brighton, UK
| | - Margarita Pons-Salort
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Hayley A Thompson
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - H Juliette T Unwin
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Robert Verity
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Michaela A C Vollmer
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Caroline E Walters
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Oliver J Watson
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
- Department of Laboratory Medicine and Pathology, Brown University, Providence, RI, USA
| | - Charles Whittaker
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Lilith K Whittles
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Azra C Ghani
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
| | - Christl A Donnelly
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | - Neil M Ferguson
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK.
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, UK.
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Jeffrey B, Walters CE, Ainslie KEC, Eales O, Ciavarella C, Bhatia S, Hayes S, Baguelin M, Boonyasiri A, Brazeau NF, Cuomo-Dannenburg G, FitzJohn RG, Gaythorpe K, Green W, Imai N, Mellan TA, Mishra S, Nouvellet P, Unwin HJT, Verity R, Vollmer M, Whittaker C, Ferguson NM, Donnelly CA, Riley S. Anonymised and aggregated crowd level mobility data from mobile phones suggests that initial compliance with COVID-19 social distancing interventions was high and geographically consistent across the UK. Wellcome Open Res 2020; 5:170. [PMID: 32954015 PMCID: PMC7479499 DOI: 10.12688/wellcomeopenres.15997.1] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/29/2020] [Indexed: 01/08/2023] Open
Abstract
Background: Since early March 2020, the COVID-19 epidemic across the United Kingdom has led to a range of social distancing policies, which have resulted in reduced mobility across different regions. Crowd level data on mobile phone usage can be used as a proxy for actual population mobility patterns and provide a way of quantifying the impact of social distancing measures on changes in mobility. Methods: Here, we use two mobile phone-based datasets (anonymised and aggregated crowd level data from O2 and from the Facebook app on mobile phones) to assess changes in average mobility, both overall and broken down into high and low population density areas, and changes in the distribution of journey lengths. Results: We show that there was a substantial overall reduction in mobility, with the most rapid decline on the 24th March 2020, the day after the Prime Minister's announcement of an enforced lockdown. The reduction in mobility was highly synchronized across the UK. Although mobility has remained low since 26th March 2020, we detect a gradual increase since that time. We also show that the two different datasets produce similar trends, albeit with some location-specific differences. We see slightly larger reductions in average mobility in high-density areas than in low-density areas, with greater variation in mobility in the high-density areas: some high-density areas eliminated almost all mobility. Conclusions: These analyses form a baseline from which to observe changes in behaviour in the UK as social distancing is eased and inform policy towards the future control of SARS-CoV-2 in the UK.
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Affiliation(s)
- Benjamin Jeffrey
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Caroline E. Walters
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Kylie E. C. Ainslie
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Oliver Eales
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Constanze Ciavarella
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Sarah Hayes
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Marc Baguelin
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Adhiratha Boonyasiri
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK
| | - Nicholas F. Brazeau
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Gina Cuomo-Dannenburg
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Richard G. FitzJohn
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Katy Gaythorpe
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - William Green
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Natsuko Imai
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Thomas A. Mellan
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Swapnil Mishra
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Pierre Nouvellet
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
- School of Life Sciences, University of Sussex, Brighton, UK
| | - H. Juliette T. Unwin
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Robert Verity
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Michaela Vollmer
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Charles Whittaker
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Neil M. Ferguson
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
| | - Christl A. Donnelly
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA),, Imperial College London, London, UK
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Forna A, Nouvellet P, Dorigatti I, Donnelly CA. Case Fatality Ratio Estimates for the 2013-2016 West African Ebola Epidemic: Application of Boosted Regression Trees for Imputation. Clin Infect Dis 2020; 70:2476-2483. [PMID: 31328221 PMCID: PMC7286386 DOI: 10.1093/cid/ciz678] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Accepted: 07/17/2019] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND The 2013-2016 West African Ebola epidemic has been the largest to date with >11 000 deaths in the affected countries. The data collected have provided more insight into the case fatality ratio (CFR) and how it varies with age and other characteristics. However, the accuracy and precision of the naive CFR remain limited because 44% of survival outcomes were unreported. METHODS Using a boosted regression tree model, we imputed survival outcomes (ie, survival or death) when unreported, corrected for model imperfection to estimate the CFR without imputation, with imputation, and adjusted with imputation. The method allowed us to further identify and explore relevant clinical and demographic predictors of the CFR. RESULTS The out-of-sample performance (95% confidence interval [CI]) of our model was good: sensitivity, 69.7% (52.5-75.6%); specificity, 69.8% (54.1-75.6%); percentage correctly classified, 69.9% (53.7-75.5%); and area under the receiver operating characteristic curve, 76.0% (56.8-82.1%). The adjusted CFR estimates (95% CI) for the 2013-2016 West African epidemic were 82.8% (45.6-85.6%) overall and 89.1% (40.8-91.6%), 65.6% (61.3-69.6%), and 79.2% (45.4-84.1%) for Sierra Leone, Guinea, and Liberia, respectively. We found that district, hospitalisation status, age, case classification, and quarter (date of case reporting aggregated at three-month intervals) explained 93.6% of the variance in the naive CFR. CONCLUSIONS The adjusted CFR estimates improved the naive CFR estimates obtained without imputation and were more representative. Used in conjunction with other resources, adjusted estimates will inform public health contingency planning for future Ebola epidemics, and help better allocate resources and evaluate the effectiveness of future inventions.
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Affiliation(s)
- Alpha Forna
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Brighton, Brighton, United Kingdom, and Imperial College London, London, United Kingdom
| | - Pierre Nouvellet
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Brighton, Brighton, United Kingdom, and Imperial College London, London, United Kingdom
- School of Life Sciences, University of Sussex, Brighton, Brighton, United Kingdom
| | - Ilaria Dorigatti
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Brighton, Brighton, United Kingdom, and Imperial College London, London, United Kingdom
| | - Christl A Donnelly
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Brighton, Brighton, United Kingdom, and Imperial College London, London, United Kingdom
- Department of Statistics, University of Oxford, Oxford, United Kingdom
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Desai AN, Kraemer MUG, Bhatia S, Cori A, Nouvellet P, Herringer M, Cohn EL, Carrion M, Brownstein JS, Madoff LC, Lassmann B. Real-time Epidemic Forecasting: Challenges and Opportunities. Health Secur 2020; 17:268-275. [PMID: 31433279 PMCID: PMC6708259 DOI: 10.1089/hs.2019.0022] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 04/18/2019] [Accepted: 04/23/2019] [Indexed: 12/23/2022] Open
Abstract
Infectious disease outbreaks play an important role in global morbidity and mortality. Real-time epidemic forecasting provides an opportunity to predict geographic disease spread as well as case counts to better inform public health interventions when outbreaks occur. Challenges and recent advances in predictive modeling are discussed here. We identified data needs in the areas of epidemic surveillance, mobility, host and environmental susceptibility, pathogen transmissibility, population density, and healthcare capacity. Constraints in standardized case definitions and timely data sharing can limit the precision of predictive models. Resource-limited settings present particular challenges for accurate epidemic forecasting due to the lack of granular data available. Incorporating novel data streams into modeling efforts is an important consideration for the future as technology penetration continues to improve on a global level. Recent advances in machine-learning, increased collaboration between modelers, the use of stochastic semi-mechanistic models, real-time digital disease surveillance data, and open data sharing provide opportunities for refining forecasts for future epidemics. Epidemic forecasting using predictive modeling is an important tool for outbreak preparedness and response efforts. Despite the presence of some data gaps at present, opportunities and advancements in innovative data streams provide additional support for modeling future epidemics.
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Affiliation(s)
- Angel N. Desai
- Angel N. Desai, MD, is a Clinical Infectious Disease Research Fellow, Division of Infectious Diseases, Brigham & Women's Hospital, Boston, MA
| | - Moritz U. G. Kraemer
- Moritz U. G. Kraemer, DPhil, is in the Department of Zoology, University of Oxford, UK; Computational Epidemiology group, Boston Children's Hospital, Boston, MA; and Harvard Medical School, Harvard University, Boston, MA
| | - Sangeeta Bhatia
- Sangeeta Bhatia, PhD, is a Research Associate, and Anne Cori, PhD, is a Lecturer; both in the MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London
| | - Anne Cori
- Sangeeta Bhatia, PhD, is a Research Associate, and Anne Cori, PhD, is a Lecturer; both in the MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London
| | - Pierre Nouvellet
- Pierre Nouvellet, PhD, is a Reader, School of Life Sciences, University of Sussex, Brighton, UK
| | - Mark Herringer
- Mark Herringer, Business Analyst, is the Founder of the Global Healthsites Mapping Project, London
| | - Emily L. Cohn
- Emily L. Cohn, MPH, is Program Manager, Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA
| | - Malwina Carrion
- Malwina Carrion, MPH, is a Lecturer, Department of Health Science, Sargent College, Boston University, Boston, MA
| | - John S. Brownstein
- John S. Brownstein, PhD, is Chief Innovation Officer, Innovation and Digital Health Accelerator, Boston Children's Hospital, Boston, MA, and Professor, Department of Pediatrics, Harvard Medical School, Boston, MA
| | - Lawrence C. Madoff
- Lawrence C. Madoff, MD, is Professor of Medicine, University of Massachusetts Medical School, Worcester, MA, and Editor, ProMED, International Society for Infectious Diseases, Brookline, MA
| | - Britta Lassmann
- Britta Lassmann, MD, is Program Director, International Society for Infectious Diseases, Boston, MA
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Li C, Zhang Y, Nouvellet P, Okoro JO, Xiao W, Harder MK. Distance is a barrier to recycling - or is it? Surprises from a clean test. Waste Manag 2020; 108:183-188. [PMID: 32361534 DOI: 10.1016/j.wasman.2020.04.022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 04/05/2020] [Accepted: 04/13/2020] [Indexed: 06/11/2023]
Abstract
The distance of recycling bins from households is often considered important by practitioners, but published evidence for this uses only indirect and self-reported data. This study aims to provide such evidence by obtaining a clean test using measured distances in a walled community with 1200 households with the same building types, local governance, recycling and waste arrangements. The number of deposits each month of food waste for recycling at a designated site are logged via smart-cards allocated per household. The number of days per month that each household deposits showed a highly significant - but small - negative correlation with distance of the bin: fewer householders participate if further away, accounting for 3% of the variation. Surprisingly, there is no variation with distance among those who do participate: their recycling frequency does not vary. This second result is not consistent with the first in terms of cost/benefit concepts assumed by government planners, nor with the static theories of behaviour currently used in waste management research. We recommend that recycling practitioners note the smallness of the contribution of distance to recycling performance, and not overrate it. And we recommend that researchers make better use of non-static models (which model different stages towards behaviour change), which our second result appears to call for.
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Affiliation(s)
- Changjun Li
- Department of Environmental Science & Engineering, Fudan University, 2005 Songhu Road, Shanghai 200438, PR China
| | - Yi Zhang
- Department of Environmental Science & Engineering, Fudan University, 2005 Songhu Road, Shanghai 200438, PR China
| | | | - Joseph O Okoro
- Department of Environmental Science & Engineering, Fudan University, 2005 Songhu Road, Shanghai 200438, PR China
| | - Wang Xiao
- Department of Environmental Science & Engineering, Fudan University, 2005 Songhu Road, Shanghai 200438, PR China
| | - Marie K Harder
- Department of Environmental Science & Engineering, Fudan University, 2005 Songhu Road, Shanghai 200438, PR China; University of Brighton, Cockcroft Building, Lewes Road, Brighton BN2 4GJ, United Kingdom.
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36
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Polonsky JA, Baidjoe A, Kamvar ZN, Cori A, Durski K, Edmunds WJ, Eggo RM, Funk S, Kaiser L, Keating P, de Waroux OLP, Marks M, Moraga P, Morgan O, Nouvellet P, Ratnayake R, Roberts CH, Whitworth J, Jombart T. Outbreak analytics: a developing data science for informing the response to emerging pathogens. Philos Trans R Soc Lond B Biol Sci 2020; 374:20180276. [PMID: 31104603 PMCID: PMC6558557 DOI: 10.1098/rstb.2018.0276] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Despite continued efforts to improve health systems worldwide, emerging pathogen epidemics remain a major public health concern. Effective response to such outbreaks relies on timely intervention, ideally informed by all available sources of data. The collection, visualization and analysis of outbreak data are becoming increasingly complex, owing to the diversity in types of data, questions and available methods to address them. Recent advances have led to the rise of outbreak analytics, an emerging data science focused on the technological and methodological aspects of the outbreak data pipeline, from collection to analysis, modelling and reporting to inform outbreak response. In this article, we assess the current state of the field. After laying out the context of outbreak response, we critically review the most common analytics components, their inter-dependencies, data requirements and the type of information they can provide to inform operations in real time. We discuss some challenges and opportunities and conclude on the potential role of outbreak analytics for improving our understanding of, and response to outbreaks of emerging pathogens. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control‘. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.
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Affiliation(s)
- Jonathan A Polonsky
- 1 Department of Health Emergency Information and Risk Assessment, World Health Organization , Avenue Appia 20, 1211 Geneva , Switzerland.,3 Faculty of Medicine, University of Geneva , 1 rue Michel-Servet, 1211 Geneva , Switzerland
| | - Amrish Baidjoe
- 4 Department of Infectious Disease Epidemiology, School of Public Health, MRC Centre for Global Infectious Disease Analysis, Imperial College London , Medical School Building, St Mary's Campus, Norfolk Place London W2 1PG , UK
| | - Zhian N Kamvar
- 4 Department of Infectious Disease Epidemiology, School of Public Health, MRC Centre for Global Infectious Disease Analysis, Imperial College London , Medical School Building, St Mary's Campus, Norfolk Place London W2 1PG , UK
| | - Anne Cori
- 4 Department of Infectious Disease Epidemiology, School of Public Health, MRC Centre for Global Infectious Disease Analysis, Imperial College London , Medical School Building, St Mary's Campus, Norfolk Place London W2 1PG , UK
| | - Kara Durski
- 2 Department of Infectious Hazard Management, World Health Organization , Avenue Appia 20, 1211 Geneva , Switzerland
| | - W John Edmunds
- 5 Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine , Keppel St, London WC1E 7HT , UK.,6 Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine , Keppel St, London WC1E 7HT , UK
| | - Rosalind M Eggo
- 5 Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine , Keppel St, London WC1E 7HT , UK.,6 Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine , Keppel St, London WC1E 7HT , UK
| | - Sebastian Funk
- 5 Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine , Keppel St, London WC1E 7HT , UK.,6 Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine , Keppel St, London WC1E 7HT , UK
| | - Laurent Kaiser
- 3 Faculty of Medicine, University of Geneva , 1 rue Michel-Servet, 1211 Geneva , Switzerland
| | - Patrick Keating
- 5 Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine , Keppel St, London WC1E 7HT , UK.,8 UK Public Health Rapid Support Team , London School of Hygiene and Tropical Medicine, Keppel St, London WC1E 7HT , UK
| | - Olivier le Polain de Waroux
- 5 Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine , Keppel St, London WC1E 7HT , UK.,8 UK Public Health Rapid Support Team , London School of Hygiene and Tropical Medicine, Keppel St, London WC1E 7HT , UK.,9 Public Health England , Wellington House, 133-155 Waterloo Road, London SE1 8UG , UK
| | - Michael Marks
- 7 Clinical Research Department, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine , Keppel St, London WC1E 7HT , UK
| | - Paula Moraga
- 10 Centre for Health Informatics, Computing and Statistics (CHICAS), Lancaster Medical School, Lancaster University , Lancaster LA1 4YW , UK
| | - Oliver Morgan
- 1 Department of Health Emergency Information and Risk Assessment, World Health Organization , Avenue Appia 20, 1211 Geneva , Switzerland
| | - Pierre Nouvellet
- 4 Department of Infectious Disease Epidemiology, School of Public Health, MRC Centre for Global Infectious Disease Analysis, Imperial College London , Medical School Building, St Mary's Campus, Norfolk Place London W2 1PG , UK.,11 School of Life Sciences, University of Sussex , Sussex House, Brighton BN1 9RH , UK
| | - Ruwan Ratnayake
- 5 Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine , Keppel St, London WC1E 7HT , UK.,6 Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine , Keppel St, London WC1E 7HT , UK
| | - Chrissy H Roberts
- 7 Clinical Research Department, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine , Keppel St, London WC1E 7HT , UK
| | - Jimmy Whitworth
- 5 Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine , Keppel St, London WC1E 7HT , UK.,8 UK Public Health Rapid Support Team , London School of Hygiene and Tropical Medicine, Keppel St, London WC1E 7HT , UK
| | - Thibaut Jombart
- 4 Department of Infectious Disease Epidemiology, School of Public Health, MRC Centre for Global Infectious Disease Analysis, Imperial College London , Medical School Building, St Mary's Campus, Norfolk Place London W2 1PG , UK.,5 Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine , Keppel St, London WC1E 7HT , UK.,8 UK Public Health Rapid Support Team , London School of Hygiene and Tropical Medicine, Keppel St, London WC1E 7HT , UK
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Forna A, Dorigatti I, Nouvellet P, Donnelly CA. Spatiotemporal variability in case fatality ratios for the 2013-2016 Ebola epidemic in West Africa. Int J Infect Dis 2020; 93:48-55. [PMID: 32004692 PMCID: PMC7191269 DOI: 10.1016/j.ijid.2020.01.046] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Revised: 01/21/2020] [Accepted: 01/22/2020] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND For the 2013-2016 Ebola epidemic in West Africa, the largest Ebola virus disease (EVD) epidemic to date, we aim to analyse the patient mix in detail to characterise key sources of spatiotemporal heterogeneity in the case fatality ratios (CFR). METHODS We applied a non-parametric Boosted Regression Trees (BRT) imputation approach for patients with missing survival outcomes and adjusted for model imperfection. Semivariogram analysis and kriging were used to investigate spatiotemporal heterogeneities. RESULTS CFR estimates varied significantly between districts and over time over the course of the epidemic. BRT modelling accounted for most of the spatiotemporal variation and interactions in CFR, but moderate spatial autocorrelation remained for distances up to approximately 90 km. Combining district-level CFR estimates and kriged district-level residuals provided the best linear unbiased predicted map of CFR accounting for the both explained and unexplained spatial variation. Temporal autocorrelation was not observed in the district-level residuals from the BRT estimates. CONCLUSIONS This study provides new insight into the epidemiology of the 2013-2016 West African Ebola epidemic with a view of informing future public health contingency planning, resource allocation and impact assessment. The analytical framework developed in this analysis, coupled with key domain knowledge, could be deployed in real time to support the response to ongoing and future outbreaks.
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Affiliation(s)
- Alpha Forna
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
| | - Ilaria Dorigatti
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Pierre Nouvellet
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK; School of Life Sciences, University of Sussex, Brighton, UK
| | - Christl A Donnelly
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK; Department of Statistics, University of Oxford, Oxford, UK
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Gold S, Donnelly CA, Nouvellet P, Woodroffe R. Rabies virus-neutralising antibodies in healthy, unvaccinated individuals: What do they mean for rabies epidemiology? PLoS Negl Trop Dis 2020; 14:e0007933. [PMID: 32053628 PMCID: PMC7017994 DOI: 10.1371/journal.pntd.0007933] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Rabies has been a widely feared disease for thousands of years, with records of rabid dogs as early as ancient Egyptian and Mesopotamian texts. The reputation of rabies as being inevitably fatal, together with its ability to affect all mammalian species, contributes to the fear surrounding this disease. However, the widely held view that exposure to the rabies virus is always fatal has been repeatedly challenged. Although survival following clinical infection in humans has only been recorded on a handful of occasions, a number of studies have reported detection of rabies-specific antibodies in the sera of humans, domestic animals, and wildlife that are apparently healthy and unvaccinated. These 'seropositive' individuals provide possible evidence of exposure to the rabies virus that has not led to fatal disease. However, the variability in methods of detecting these antibodies and the difficulties of interpreting serology tests have contributed to an unclear picture of their importance. In this review, we consider the evidence for rabies-specific antibodies in healthy, unvaccinated individuals as indicators of nonlethal rabies exposure and the potential implications of this for rabies epidemiology. Our findings indicate that whilst there is substantial evidence that nonlethal rabies exposure does occur, serology studies that do not use appropriate controls and cutoffs are unlikely to provide an accurate estimate of the true prevalence of nonlethal rabies exposure.
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Affiliation(s)
- Susannah Gold
- Institute of Zoology, Zoological Society of London, London, United Kingdom
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
| | - Christl A. Donnelly
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Pierre Nouvellet
- School of Life Sciences, University of Sussex, Falmer, United Kingdom
| | - Rosie Woodroffe
- Institute of Zoology, Zoological Society of London, London, United Kingdom
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Cucunubá ZM, Nouvellet P, Peterson JK, Bartsch SM, Lee BY, Dobson AP, Basáñez MG. Complementary Paths to Chagas Disease Elimination: The Impact of Combining Vector Control With Etiological Treatment. Clin Infect Dis 2019; 66:S293-S300. [PMID: 29860294 PMCID: PMC5982731 DOI: 10.1093/cid/ciy006] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Background The World Health Organization’s 2020 goals for Chagas disease are (1) interrupting vector-borne intradomiciliary transmission and (2) having all infected people under care in endemic countries. Insecticide spraying has proved efficacious for reaching the first goal, but active transmission remains in several regions. For the second, treatment has mostly been restricted to recently infected patients, who comprise only a small proportion of all infected individuals. Methods We extended our previous dynamic transmission model to simulate a domestic Chagas disease transmission cycle and examined the effects of both vector control and etiological treatment on achieving the operational criterion proposed by the Pan American Health Organization for intradomiciliary, vectorial transmission interruption (ie, <2% seroprevalence in children <5 years of age). Results Depending on endemicity, an antivectorial intervention that decreases vector density by 90% annually would achieve the transmission interruption criterion in 2–3 years (low endemicity) to >30 years (high endemicity). When this strategy is combined with annual etiological treatment in 10% of the infected human population, the seroprevalence criterion would be achieved, respectively, in 1 and 11 years. Conclusions Combining highly effective vector control with etiological (trypanocidal) treatment in humans would substantially reduce time to transmission interruption as well as infection incidence and prevalence. However, the success of vector control may depend on prevailing vector species. It will be crucial to improve the coverage of screening programs, the performance of diagnostic tests, the proportion of people treated, and the efficacy of trypanocidal drugs. While screening and access can be incremented as part of strengthening the health systems response, improving diagnostics performance and drug efficacy will require further research.
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Affiliation(s)
- Zulma M Cucunubá
- London Centre for Neglected Tropical Disease Research, United Kingdom.,Medical Research Council Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, United Kingdom
| | - Pierre Nouvellet
- London Centre for Neglected Tropical Disease Research, United Kingdom.,Medical Research Council Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, United Kingdom
| | - Jennifer K Peterson
- Zoonotic Disease Research Center, Arequipa, Peru.,Department of Biostatistics, Epidemiology and Bioinformatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Sarah M Bartsch
- Public Health Computational and Operations Research, John Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Bruce Y Lee
- Public Health Computational and Operations Research, John Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Andrew P Dobson
- Department of Ecology and Evolutionary Biology, Princeton University, New Jersey
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Waxman D, Nouvellet P. Sub- or supercritical transmissibilities in a finite disease outbreak: Symmetry in outbreak properties of a disease conditioned on extinction. J Theor Biol 2019; 467:80-86. [PMID: 30711456 PMCID: PMC6408326 DOI: 10.1016/j.jtbi.2019.01.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Revised: 01/24/2019] [Accepted: 01/30/2019] [Indexed: 11/28/2022]
Abstract
This work is concerned with the transmissibility of a disease, on observation of an outbreak of limited size. When such an outbreak occurs, an accurate estimate of the transmissibility of the responsible pathogen is essential for an appropriate response to future outbreaks. Transmissibility is usually characterised in terms of the reproduction number, R, which is the mean number of new cases of infection produced by a single infectious individual. A subcritical reproduction number (R < 1) guarantees that an outbreak will eventually die out of its own accord. By contrast, a supercritical reproduction number (R > 1) does not guarantee spread of the disease, since even with appreciable transmissibility, an outbreak may become extinct due to stochastic effects associated with a small number of infected individuals. Once the number of infectious individuals is conditioned on extinction, we show that an exact symmetry of the underlying theory ensures two distinct values of R, one larger than unity, the other smaller than unity, for which all outbreak properties are identical, with no signature of difference. Therefore a disease with a subcritical R, or its supercritical counterpart, when conditioned on extinction, have, for a given outbreak, identical individual likelihoods. In the full likelihood, this symmetry is lost, since the individual likelihood for a subcritical R is weighted by an extinction probability of unity, but the individual likelihood of a supercritical R is weighted by a sub-unity extinction probability. However, the inference can still benefit from the underlying symmetry, since it yields a mapping of all supercritical reproduction numbers onto the subcritical domain (R < 1), thereby speeding up evaluation of the likelihood profile. The symmetry holds in the standard situation, where the distribution of secondary cases is Poisson, as well as where this distribution has a negative binomial form and super-spreading can occur.
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Affiliation(s)
- David Waxman
- Centre for Computational Systems Biology ISTBI, Fudan University, 220 Handan Road, Shanghai 200433, People's Republic of China
| | - Pierre Nouvellet
- School of Life Sciences, University of Sussex, Brighton BN1 9QG, UK.
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Zecchin B, De Nardi M, Nouvellet P, Vernesi C, Babbucci M, Crestanello B, Bagó Z, Bedeković T, Hostnik P, Milani A, Donnelly CA, Bargelloni L, Lorenzetto M, Citterio C, Obber F, De Benedictis P, Cattoli G. Genetic and spatial characterization of the red fox (Vulpes vulpes) population in the area stretching between the Eastern and Dinaric Alps and its relationship with rabies and canine distemper dynamics. PLoS One 2019; 14:e0213515. [PMID: 30861028 PMCID: PMC6413928 DOI: 10.1371/journal.pone.0213515] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Accepted: 02/24/2019] [Indexed: 01/02/2023] Open
Abstract
Information on the population dynamics of a reservoir species have been increasingly adopted to understand and eventually predict the dispersal patterns of infectious diseases throughout an area. Although potentially relevant, to date there are no studies which have investigated the genetic structure of the red fox population in relation to infectious disease dynamics. Therefore, we genetically and spatially characterised the red fox population in the area stretching between the Eastern and Dinaric Alps, which has been affected by both distemper and rabies at different time intervals. Red foxes collected from north-eastern Italy, Austria, Slovenia and Croatia between 2006–2012, were studied using a set of 21 microsatellite markers. We confirmed a weak genetic differentiation within the fox population using Bayesian clustering analyses, and we were able to differentiate the fox population into geographically segregated groups. Our finding might be due to the presence of geographical barriers that have likely influenced the distribution of the fox population, limiting in turn gene flow and spread of infectious diseases. Focusing on the Italian red fox population, we observed interesting variations in the prevalence of both diseases among distinct fox clusters, with the previously identified Italy 1 and Italy 2 rabies as well as distemper viruses preferentially affecting different sub-groups identified in the study. Knowledge of the regional-scale population structure can improve understanding of the epidemiology and spread of diseases. Our study paves the way for an integrated approach for disease control coupling pathogen, host and environmental data to inform targeted control programs in the future.
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Affiliation(s)
- Bianca Zecchin
- Department of Comparative Biomedical Sciences, Istituto Zooprofilattico Sperimentale delle Venezie (IZSVe), Legnaro, Italy
- * E-mail:
| | - Marco De Nardi
- Department of Comparative Biomedical Sciences, Istituto Zooprofilattico Sperimentale delle Venezie (IZSVe), Legnaro, Italy
| | - Pierre Nouvellet
- Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Cristiano Vernesi
- Department of Biodiversity and Molecular Ecology, Research and Innovation Centre, Fondazione Edmund Mach (FEM), San Michele all'Adige, Italy
| | - Massimiliano Babbucci
- Department of Comparative Biomedicine and Food Science (BCA), University of Padova, Legnaro, Italy
| | - Barbara Crestanello
- Department of Biodiversity and Molecular Ecology, Research and Innovation Centre, Fondazione Edmund Mach (FEM), San Michele all'Adige, Italy
| | - Zoltán Bagó
- Austrian Agency for Health and Food Safety (AGES), Institute for Veterinary Disease Control, Mödling, Austria
| | | | - Peter Hostnik
- Virology Unit, Veterinary Faculty, Institute of Microbiology and Parasitology, University of Ljubljana, Ljubljana, Slovenia
| | - Adelaide Milani
- Department of Comparative Biomedical Sciences, Istituto Zooprofilattico Sperimentale delle Venezie (IZSVe), Legnaro, Italy
| | - Christl Ann Donnelly
- Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
- National Institute for Health Research Health Protection Research Unit in Modelling Methodology, Imperial College London, London, United Kingdom
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Luca Bargelloni
- Department of Comparative Biomedicine and Food Science (BCA), University of Padova, Legnaro, Italy
| | - Monica Lorenzetto
- Department of Veterinary Epidemiology, Istituto Zooprofilattico Sperimentale delle Venezie (IZSVe), Legnaro, Italy
| | - Carlo Citterio
- SCT2 Belluno, Istituto Zooprofilattico Sperimentale delle Venezie (IZSVe), Belluno, Italy
| | - Federica Obber
- SCT2 Belluno, Istituto Zooprofilattico Sperimentale delle Venezie (IZSVe), Belluno, Italy
| | - Paola De Benedictis
- Department of Comparative Biomedical Sciences, Istituto Zooprofilattico Sperimentale delle Venezie (IZSVe), Legnaro, Italy
| | - Giovanni Cattoli
- Department of Comparative Biomedical Sciences, Istituto Zooprofilattico Sperimentale delle Venezie (IZSVe), Legnaro, Italy
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Forna A, Nouvellet P, Dorigatti I, Donnelly C. Case fatality ratio estimates for the 2013–2016 West African Ebola epidemic: application of Boosted Regression Trees for imputation. Int J Infect Dis 2019. [DOI: 10.1016/j.ijid.2018.11.313] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
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Bhatia S, Carrion M, Cohn E, Cori A, Nouvellet P, Lassmann B, Madoff L, Brownstein J. Big brother is watching - using digital disease surveillance tools for near real-time forecasting. Int J Infect Dis 2019. [DOI: 10.1016/j.ijid.2018.11.080] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
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Leopardi S, Nouvellet P, Priori P, Zecchin B, Salomoni A, Mancin M, Scaravelli D, de Benedictis P. Unraveling the role of Myotis myotis in the ecology and transmission of rabies-related lyssaviruses (RRLVs). Int J Infect Dis 2019. [DOI: 10.1016/j.ijid.2018.11.148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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Cori A, Nouvellet P, Garske T, Bourhy H, Nakouné E, Jombart T. A graph-based evidence synthesis approach to detecting outbreak clusters: An application to dog rabies. PLoS Comput Biol 2018; 14:e1006554. [PMID: 30557340 PMCID: PMC6312344 DOI: 10.1371/journal.pcbi.1006554] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Revised: 12/31/2018] [Accepted: 10/09/2018] [Indexed: 11/23/2022] Open
Abstract
Early assessment of infectious disease outbreaks is key to implementing timely and effective control measures. In particular, rapidly recognising whether infected individuals stem from a single outbreak sustained by local transmission, or from repeated introductions, is crucial to adopt effective interventions. In this study, we introduce a new framework for combining several data streams, e.g. temporal, spatial and genetic data, to identify clusters of related cases of an infectious disease. Our method explicitly accounts for underreporting, and allows incorporating preexisting information about the disease, such as its serial interval, spatial kernel, and mutation rate. We define, for each data stream, a graph connecting all cases, with edges weighted by the corresponding pairwise distance between cases. Each graph is then pruned by removing distances greater than a given cutoff, defined based on preexisting information on the disease and assumptions on the reporting rate. The pruned graphs corresponding to different data streams are then merged by intersection to combine all data types; connected components define clusters of cases related for all types of data. Estimates of the reproduction number (the average number of secondary cases infected by an infectious individual in a large population), and the rate of importation of the disease into the population, are also derived. We test our approach on simulated data and illustrate it using data on dog rabies in Central African Republic. We show that the outbreak clusters identified using our method are consistent with structures previously identified by more complex, computationally intensive approaches. Early assessment of infectious disease outbreaks is key to implementing timely and effective control measures. In particular, rapidly recognising whether infected individuals stem from a single outbreak sustained by local transmission, or from repeated introductions, is crucial to adopt effective interventions. In this study, we introduce a new approach which combines different types of data to identify clusters of related cases of an infectious disease. This approach relies on representing each type of data (e.g. temporal, spatial, or genetic) as a graph where nodes are cases, and two nodes are connected if the corresponding cases are closely related for this data. Our method then identifies clusters of cases which likely stem from the same introduction. Furthermore, we can use the size of these clusters to infer transmissibility of the disease and the number of importations of the pathogen into the population. We apply this approach to analyse dog rabies epidemics in Central African Republic. We show that outbreak clusters identified using our method are consistent with structures previously identified by more complex and computationally intensive approaches. Using simulated rabies epidemics, we show that our method has excellent potential for optimally detecting outbreak clusters. We also identify promising areas of research for transforming our method into a routine analysis tool for processing disease surveillance data.
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Affiliation(s)
- Anne Cori
- MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
- * E-mail: (AC); (TJ)
| | - Pierre Nouvellet
- MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
- School of Life Sciences, University of Sussex, Brighton, United Kingdom
| | - Tini Garske
- MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
| | - Hervé Bourhy
- Unit Lyssavirus Dynamics and Host Adaptation, WHO Collaborating Centre for Reference and Research on Rabies, Institut Pasteur, Paris, France
| | - Emmanuel Nakouné
- Département fièvres hémorragiques virales, Institut Pasteur de Bangui, Bangui, République Centrafricaine
| | - Thibaut Jombart
- MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
- * E-mail: (AC); (TJ)
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Hay JA, Nouvellet P, Donnelly CA, Riley S. Potential inconsistencies in Zika surveillance data and our understanding of risk during pregnancy. PLoS Negl Trop Dis 2018; 12:e0006991. [PMID: 30532143 PMCID: PMC6301717 DOI: 10.1371/journal.pntd.0006991] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2017] [Revised: 12/20/2018] [Accepted: 11/12/2018] [Indexed: 02/05/2023] Open
Abstract
Background A significant increase in microcephaly incidence was reported in Northeast Brazil at the end of 2015, which has since been attributed to an epidemic of Zika virus (ZIKV) infections earlier that year. Further incidence of congenital Zika syndrome (CZS) was expected following waves of ZIKV infection throughout Latin America; however, only modest increases in microcephaly and CZS incidence have since been observed. The quantitative relationship between ZIKV infection, gestational age and congenital outcome remains poorly understood. Methodology/Principle findings We characterised the gestational-age-varying risk of microcephaly given ZIKV infection using publicly available incidence data from multiple locations in Brazil and Colombia. We found that the relative timings and shapes of ZIKV infection and microcephaly incidence curves suggested different gestational risk profiles for different locations, varying in both the duration and magnitude of gestational risk. Data from Northeast Brazil suggested a narrow window of risk during the first trimester, whereas data from Colombia suggested persistent risk throughout pregnancy. We then used the model to estimate which combination of behavioural and reporting changes would have been sufficient to explain the absence of a second microcephaly incidence wave in Bahia, Brazil; a population for which we had two years of data. We found that a 18.9-fold increase in ZIKV infection reporting rate was consistent with observed patterns. Conclusions Our study illustrates how surveillance data may be used in principle to answer key questions in the absence of directed epidemiological studies. However, in this case, we suggest that currently available surveillance data are insufficient to accurately estimate the gestational-age-varying risk of microcephaly from ZIKV infection. The methods used here may be of use in future outbreaks and may help to inform improved surveillance and interpretation in countries yet to experience an outbreak of ZIKV infection. Zika virus (ZIKV) infection is associated with the rise of microcephaly cases observed in Northeast Brazil at the end of 2015. For women in endemic or at-risk areas, understanding how the relationship between time of infection and microcephaly risk varies through pregnancy is important in informing family planning. However, a relatively modest number of congenital Zika syndrome cases have been observed following subsequent waves of ZIKV infection, limiting our understanding of gestational risk. We used a mathematical model to quantify the shape and magnitude of the gestational-age-varying risk to a fetus. Although the risk profile should be conserved regardless of location, we estimated different profiles when using surveillance data from locations in Northeast Brazil and Colombia. Our results suggest that time-dependent reporting changes likely confound the interpretation of currently available surveillance data. Furthermore, we investigated a range of behavioural and reporting rate changes that could explain two waves of ZIKV infection in Bahia, Brazil despite only one wave of microcephaly. Plausible changes in reporting could explain these data whilst remaining consistent with the hypothesis that ZIKV infection carries a significant risk of microcephaly. Further evidence is needed to disentangle the true risk of congenital Zika syndrome from time-varying reporting changes.
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Affiliation(s)
- James A. Hay
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College, London, UK
| | - Pierre Nouvellet
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College, London, UK
- School of Life Sciences, University of Sussex, Brighton, UK
| | - Christl A. Donnelly
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College, London, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College, London, UK
- * E-mail:
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Barry A, Ahuka-Mundeke S, Ali Ahmed Y, Allarangar Y, Anoko J, Archer BN, Aruna Abedi A, Bagaria J, Belizaire MRD, Bhatia S, Bokenge T, Bruni E, Cori A, Dabire E, Diallo AM, Diallo B, Donnelly CA, Dorigatti I, Dorji TC, Escobar Corado Waeber AR, Fall IS, Ferguson NM, FitzJohn RG, Folefack Tengomo GL, Formenty PBH, Forna A, Fortin A, Garske T, Gaythorpe KAM, Gurry C, Hamblion E, Harouna Djingarey M, Haskew C, Hugonnet SAL, Imai N, Impouma B, Kabongo G, Kalenga OI, Kibangou E, Lee TMH, Lukoya CO, Ly O, Makiala-Mandanda S, Mamba A, Mbala-Kingebeni P, Mboussou FFR, Mlanda T, Mondonge Makuma V, Morgan O, Mujinga Mulumba A, Mukadi Kakoni P, Mukadi-Bamuleka D, Muyembe JJ, Bathé NT, Ndumbi Ngamala P, Ngom R, Ngoy G, Nouvellet P, Nsio J, Ousman KB, Peron E, Polonsky JA, Ryan MJ, Touré A, Towner R, Tshapenda G, Van De Weerdt R, Van Kerkhove M, Wendland A, Yao NKM, Yoti Z, Yuma E, Kalambayi Kabamba G, Lukwesa Mwati JDD, Mbuy G, Lubula L, Mutombo A, Mavila O, Lay Y, Kitenge E. Outbreak of Ebola virus disease in the Democratic Republic of the Congo, April-May, 2018: an epidemiological study. Lancet 2018; 392:213-221. [PMID: 30047375 DOI: 10.1016/s0140-6736(18)31387-4] [Citation(s) in RCA: 65] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Revised: 06/12/2018] [Accepted: 06/13/2018] [Indexed: 11/26/2022]
Abstract
BACKGROUND On May 8, 2018, the Government of the Democratic Republic of the Congo reported an outbreak of Ebola virus disease in Équateur Province in the northwest of the country. The remoteness of most affected communities and the involvement of an urban centre connected to the capital city and neighbouring countries makes this outbreak the most complex and high risk ever experienced by the Democratic Republic of the Congo. We provide early epidemiological information arising from the ongoing investigation of this outbreak. METHODS We classified cases as suspected, probable, or confirmed using national case definitions of the Democratic Republic of the Congo Ministère de la Santé Publique. We investigated all cases to obtain demographic characteristics, determine possible exposures, describe signs and symptoms, and identify contacts to be followed up for 21 days. We also estimated the reproduction number and projected number of cases for the 4-week period from May 25, to June 21, 2018. FINDINGS As of May 30, 2018, 50 cases (37 confirmed, 13 probable) of Zaire ebolavirus were reported in the Democratic Republic of the Congo. 21 (42%) were reported in Bikoro, 25 (50%) in Iboko, and four (8%) in Wangata health zones. Wangata is part of Mbandaka, the urban capital of Équateur Province, which is connected to major national and international transport routes. By May 30, 2018, 25 deaths from Ebola virus disease had been reported, with a case fatality ratio of 56% (95% CI 39-72) after adjustment for censoring. This case fatality ratio is consistent with estimates for the 2014-16 west African Ebola virus disease epidemic (p=0·427). The median age of people with confirmed or probable infection was 40 years (range 8-80) and 30 (60%) were male. The most commonly reported signs and symptoms in people with confirmed or probable Ebola virus disease were fever (40 [95%] of 42 cases), intense general fatigue (37 [90%] of 41 cases), and loss of appetite (37 [90%] of 41 cases). Gastrointestinal symptoms were frequently reported, and 14 (33%) of 43 people reported haemorrhagic signs. Time from illness onset and hospitalisation to sample testing decreased over time. By May 30, 2018, 1458 contacts had been identified, of which 746 (51%) remained under active follow-up. The estimated reproduction number was 1·03 (95% credible interval 0·83-1·37) and the cumulative case incidence for the outbreak by June 21, 2018, is projected to be 78 confirmed cases (37-281), assuming heterogeneous transmissibility. INTERPRETATION The ongoing Ebola virus outbreak in the Democratic Republic of the Congo has similar epidemiological features to previous Ebola virus disease outbreaks. Early detection, rapid patient isolation, contact tracing, and the ongoing vaccination programme should sufficiently control the outbreak. The forecast of the number of cases does not exceed the current capacity to respond if the epidemiological situation does not change. The information presented, although preliminary, has been essential in guiding the ongoing investigation and response to this outbreak. FUNDING None.
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Garske T, Cori A, Ariyarajah A, Blake IM, Dorigatti I, Eckmanns T, Fraser C, Hinsley W, Jombart T, Mills HL, Nedjati-Gilani G, Newton E, Nouvellet P, Perkins D, Riley S, Schumacher D, Shah A, Van Kerkhove MD, Dye C, Ferguson NM, Donnelly CA. Heterogeneities in the case fatality ratio in the West African Ebola outbreak 2013-2016. Philos Trans R Soc Lond B Biol Sci 2017; 372:rstb.2016.0308. [PMID: 28396479 PMCID: PMC5394646 DOI: 10.1098/rstb.2016.0308] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/11/2016] [Indexed: 11/23/2022] Open
Abstract
The 2013–2016 Ebola outbreak in West Africa is the largest on record with 28 616 confirmed, probable and suspected cases and 11 310 deaths officially recorded by 10 June 2016, the true burden probably considerably higher. The case fatality ratio (CFR: proportion of cases that are fatal) is a key indicator of disease severity useful for gauging the appropriate public health response and for evaluating treatment benefits, if estimated accurately. We analysed individual-level clinical outcome data from Guinea, Liberia and Sierra Leone officially reported to the World Health Organization. The overall mean CFR was 62.9% (95% CI: 61.9% to 64.0%) among confirmed cases with recorded clinical outcomes. Age was the most important modifier of survival probabilities, but country, stage of the epidemic and whether patients were hospitalized also played roles. We developed a statistical analysis to detect outliers in CFR between districts of residence and treatment centres (TCs), adjusting for known factors influencing survival and identified eight districts and three TCs with a CFR significantly different from the average. From the current dataset, we cannot determine whether the observed variation in CFR seen by district or treatment centre reflects real differences in survival, related to the quality of care or other factors or was caused by differences in reporting practices or case ascertainment. This article is part of the themed issue ‘The 2013–2016 West African Ebola epidemic: data, decision-making and disease control’.
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Affiliation(s)
- Tini Garske
- MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London W2 1PG, UK
| | - Anne Cori
- MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London W2 1PG, UK
| | | | - Isobel M Blake
- MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London W2 1PG, UK
| | - Ilaria Dorigatti
- MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London W2 1PG, UK
| | - Tim Eckmanns
- WHO, 1211 Geneva, Switzerland.,Robert Koch Institute, 13302 Berlin, Germany
| | - Christophe Fraser
- MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London W2 1PG, UK.,Big Data Institute, University of Oxford, Oxford OX3 7LF, UK
| | - Wes Hinsley
- MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London W2 1PG, UK
| | - Thibaut Jombart
- MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London W2 1PG, UK
| | - Harriet L Mills
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
| | - Gemma Nedjati-Gilani
- MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London W2 1PG, UK
| | | | - Pierre Nouvellet
- MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London W2 1PG, UK
| | | | - Steven Riley
- MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London W2 1PG, UK
| | | | | | - Maria D Van Kerkhove
- MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London W2 1PG, UK.,Center for Global Health Research and Education, Institut Pasteur, Paris 75015, France
| | | | - Neil M Ferguson
- MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London W2 1PG, UK
| | - Christl A Donnelly
- MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London W2 1PG, UK
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Cucunubá ZM, Nouvellet P, Conteh L, Vera MJ, Angulo VM, Dib JC, Parra-Henao GJ, Basáñez MG. Modelling historical changes in the force-of-infection of Chagas disease to inform control and elimination programmes: application in Colombia. BMJ Glob Health 2017; 2:e000345. [PMID: 29147578 PMCID: PMC5680445 DOI: 10.1136/bmjgh-2017-000345] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Revised: 07/23/2017] [Accepted: 07/24/2017] [Indexed: 12/26/2022] Open
Abstract
Background WHO's 2020 milestones for Chagas disease include having all endemic Latin American countries certified with no intradomiciliary Trypanosoma cruzi transmission, and infected patients under care. Evaluating the variation in historical exposure to infection is crucial for assessing progress and for understanding the priorities to achieve these milestones. Methods Focusing on Colombia, all the available age-structured serological surveys (undertaken between 1995 and 2014) were searched and compiled. A total of 109 serosurveys were found, comprising 83 742 individuals from rural (indigenous and non-indigenous) and urban settings in 14 (out of 32) administrative units (departments). Estimates of the force-of-infection (FoI) were obtained by fitting and comparing three catalytic models using Bayesian methods to reconstruct temporal and spatial patterns over the course of three decades (between 1984 and 2014). Results Significant downward changes in the FoI were identified over the course of the three decades, and in some specific locations the predicted current seroprevalence in children aged 0-5 years is <1%. However, pronounced heterogeneity exists within departments, especially between indigenous, rural and urban settings, with the former exhibiting the highest FoI (up to 66 new infections/1000 people susceptible/year). The FoI in most of the indigenous settings remain unchanged during the three decades investigated. Current prevalence in adults in these 15 departments varies between 10% and 90% depending on the dynamics of historical exposure. Conclusions Assessing progress towards the control of Chagas disease requires quantifying the impact of historical exposure on current age-specific prevalence at subnational level. In Colombia, despite the evident progress, there is a marked heterogeneity indicating that in some areas the vector control interventions have not been effective, hindering the possibility of achieving interruption by 2020. A substantial burden of chronic cases remains even in locations where serological criteria for transmission interruption may have been achieved, therefore still demanding diagnosis and treatment interventions.
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Affiliation(s)
- Zulma M Cucunubá
- Department of Infectious Disease Epidemiology, Faculty of Medicine (St Mary's campus), Imperial College London, London, UK.,Department of Infectious Disease Epidemiology, London Centre for Neglected Tropical Disease Research (LCNTDR), Imperial College London, London, UK.,Grupode Parasitología-RED CHAGAS, Instituto Nacional de Salud, Bogotá, Colombia
| | - Pierre Nouvellet
- Department of Infectious Disease Epidemiology, Faculty of Medicine (St Mary's campus), Imperial College London, London, UK.,Department of Infectious Disease Epidemiology, Faculty of Medicine (St Mary's campus), Medical Research Council Centre for Outbreak Analysis and Modelling, School of Public Health, Imperial College London, London, UK
| | - Lesong Conteh
- Department of Infectious Disease Epidemiology, Faculty of Medicine (St Mary's campus), Imperial College London, London, UK.,Department of Infectious Disease Epidemiology, Faculty of Medicine (St Mary's campus), Medical Research Council Centre for Outbreak Analysis and Modelling, School of Public Health, Imperial College London, London, UK.,Department of Infectious Disease Epidemiology, Faculty of Medicine (St Mary's campus), Health Economics Group, School of Public Health, Imperial College London, London, UK
| | - Mauricio Javier Vera
- Grupo de Enfermedades Endemo-Epidémicas, Subdirección Enfermedades Transmisibles, Ministerio de Salud y Protección Social, Bogotá, Colombia
| | - Victor Manuel Angulo
- Centro de Investigaciones en Enfermedades Tropicales (CINTROP), Universidad Industrial de Santander, Piedecuesta, Colombia
| | | | | | - María Gloria Basáñez
- Department of Infectious Disease Epidemiology, Faculty of Medicine (St Mary's campus), Imperial College London, London, UK.,Department of Infectious Disease Epidemiology, London Centre for Neglected Tropical Disease Research (LCNTDR), Imperial College London, London, UK
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Cori A, Donnelly CA, Dorigatti I, Ferguson NM, Fraser C, Garske T, Jombart T, Nedjati-Gilani G, Nouvellet P, Riley S, Van Kerkhove MD, Mills HL, Blake IM. Key data for outbreak evaluation: building on the Ebola experience. Philos Trans R Soc Lond B Biol Sci 2017; 372:20160371. [PMID: 28396480 PMCID: PMC5394647 DOI: 10.1098/rstb.2016.0371] [Citation(s) in RCA: 55] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/11/2016] [Indexed: 01/15/2023] Open
Abstract
Following the detection of an infectious disease outbreak, rapid epidemiological assessment is critical for guiding an effective public health response. To understand the transmission dynamics and potential impact of an outbreak, several types of data are necessary. Here we build on experience gained in the West African Ebola epidemic and prior emerging infectious disease outbreaks to set out a checklist of data needed to: (1) quantify severity and transmissibility; (2) characterize heterogeneities in transmission and their determinants; and (3) assess the effectiveness of different interventions. We differentiate data needs into individual-level data (e.g. a detailed list of reported cases), exposure data (e.g. identifying where/how cases may have been infected) and population-level data (e.g. size/demographics of the population(s) affected and when/where interventions were implemented). A remarkable amount of individual-level and exposure data was collected during the West African Ebola epidemic, which allowed the assessment of (1) and (2). However, gaps in population-level data (particularly around which interventions were applied when and where) posed challenges to the assessment of (3). Here we highlight recurrent data issues, give practical suggestions for addressing these issues and discuss priorities for improvements in data collection in future outbreaks.This article is part of the themed issue 'The 2013-2016 West African Ebola epidemic: data, decision-making and disease control'.
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Affiliation(s)
- Anne Cori
- Medical Research Council Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London W2 1PG, UK
| | - Christl A Donnelly
- Medical Research Council Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London W2 1PG, UK
| | - Ilaria Dorigatti
- Medical Research Council Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London W2 1PG, UK
| | - Neil M Ferguson
- Medical Research Council Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London W2 1PG, UK
| | - Christophe Fraser
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7FZ, UK
| | - Tini Garske
- Medical Research Council Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London W2 1PG, UK
| | - Thibaut Jombart
- Medical Research Council Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London W2 1PG, UK
| | - Gemma Nedjati-Gilani
- Medical Research Council Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London W2 1PG, UK
| | - Pierre Nouvellet
- Medical Research Council Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London W2 1PG, UK
| | - Steven Riley
- Medical Research Council Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London W2 1PG, UK
| | - Maria D Van Kerkhove
- Centre for Global Health, Institut Pasteur, 25-28 Rue du Dr Roux, 75015 Paris, France
| | - Harriet L Mills
- Medical Research Council Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London W2 1PG, UK
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK
- School of Veterinary Sciences, University of Bristol, Bristol BS40 5DU, UK
| | - Isobel M Blake
- Medical Research Council Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London W2 1PG, UK
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