Improved inference of time-varying reproduction numbers during infectious disease outbreaks
| Autor: | E. Miguel, Elisabeth Dahlqwist, Anne Cori, Zhian N. Kamvar, Thibaut Jombart, Jake Stockwin, Jonathan A. Polonsky, Justin Lessler, Simon Cauchemez, Robin N Thompson, S. Li, R.D. van Gaalen, P.A. Demarsh |
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| Přispěvatelé: | Medical Research Council (MRC), National Institute for Health Research, University of Oxford [Oxford], National Institute for Public Health and the Environment [Bilthoven] (RIVM), Organisation Mondiale de la Santé / World Health Organization Office (OMS / WHO), Université de Genève (UNIGE), Imperial College London, McGill University = Université McGill [Montréal, Canada], Public Health Agency of Canada, Karolinska Institutet [Stockholm], Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), London School of Hygiene and Tropical Medicine (LSHTM), Johns Hopkins Bloomberg School of Public Health [Baltimore], Johns Hopkins University (JHU), Modélisation mathématique des maladies infectieuses - Mathematical modelling of Infectious Diseases, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), The Hackout 3 meeting at the Institute for Data Science (University of California Berkeley) was funded by the NIHR Modelling Methodology Health Protection Research Unit (Imperial College London) and the MRC Centre for Outbreak Analysis and Modelling (Imperial College London). Additional funding for JES was obtained through the RECON project at the NIHR Modelling Methodology Health Protection Research Unit (Imperial College London). RNT thanks Christ Church (University of Oxford) for funding his research via a Junior Research Fellowship. SC acknowledges financial support from the AXA Research Fund, the Investissement d’Avenir program, the Laboratoire d’Excellence Integrative Biology of Emerging Infectious Diseases program (Grant ANR-10-LABX-62-IBEID), the Models of Infectious Disease Agent Study of the National Institute of General Medical Sciences and the INCEPTION project (PIA/ANR-16-CONV-0005). AC acknowledges joint centre funding from the UK Medical Research Council and Department for International Development, as well as funding from the United States Agency for International Development (USAID)., ANR-10-LABX-0062,IBEID,Integrative Biology of Emerging Infectious Diseases(2010), ANR-16-CONV-0005,INCEPTION,Institut Convergences pour l'étude de l'Emergence des Pathologies au Travers des Individus et des populatiONs(2016), University of Oxford, Université de Genève = University of Geneva (UNIGE), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS) |
| Rok vydání: | 2019 |
| Předmět: |
DYNAMICS
R software Time Factors Epidemiology Computer science MESH: Coronavirus Infections Reproduction number Basic Reproduction Number Inference Disease medicine.disease_cause Parameter inference Disease Outbreaks Influenza A Virus H1N1 Subtype 0302 clinical medicine Serial interval RESPIRATORY SYNDROME CORONAVIRUS [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] Disease control Statistics 030212 general & internal medicine MESH: Disease Outbreaks Mathematical modelling SERIAL INTERVALS MESH: Influenza Human Uncertainty Infectious Disease Epidemiology MOUTH EPIDEMIC 3. Good health Infectious Diseases Coronavirus Infections Life Sciences & Biomedicine MESH: Uncertainty Infectious disease epidemiology TRANSMISSION FOOT 030231 tropical medicine Microbiology Article EBOLA HEMORRHAGIC-FEVER 1117 Public Health and Health Services MESH: Influenza A Virus H1N1 Subtype 03 medical and health sciences Virology Influenza Human medicine Humans Ebola virus MESH: Humans Science & Technology REAL-TIME MESH: Time Factors Public Health Environmental and Occupational Health Outbreak 1103 Clinical Sciences INFLUENZA-A H1N1 MESH: Basic Reproduction Number Infectious disease (medical specialty) RISK-FACTORS Parasitology |
| Zdroj: | Epidemics Epidemics, Elsevier, 2019, 29, pp.100356. ⟨10.1016/j.epidem.2019.100356⟩ Epidemics, 2019, 29, pp.100356. ⟨10.1016/j.epidem.2019.100356⟩ |
| ISSN: | 1755-4365 1878-0067 |
| DOI: | 10.1016/j.epidem.2019.100356⟩ |
| Popis: | Highlights • Real-time estimation of reproduction numbers during outbreaks can guide control. • Using up-to-date serial interval data and accounting for imported cases is vital. • We develop a framework for estimating pathogen transmissibility appropriately. • We demonstrate it using data from outbreaks of influenza, Ebola and MERS. • Our approach is implemented in R package EpiEstim and online application EpiEstim App. Accurate estimation of the parameters characterising infectious disease transmission is vital for optimising control interventions during epidemics. A valuable metric for assessing the current threat posed by an outbreak is the time-dependent reproduction number, i.e. the expected number of secondary cases caused by each infected individual. This quantity can be estimated using data on the numbers of observed new cases at successive times during an epidemic and the distribution of the serial interval (the time between symptomatic cases in a transmission chain). Some methods for estimating the reproduction number rely on pre-existing estimates of the serial interval distribution and assume that the entire outbreak is driven by local transmission. Here we show that accurate inference of current transmissibility, and the uncertainty associated with this estimate, requires: (i) up-to-date observations of the serial interval to be included, and; (ii) cases arising from local transmission to be distinguished from those imported from elsewhere. We demonstrate how pathogen transmissibility can be inferred appropriately using datasets from outbreaks of H1N1 influenza, Ebola virus disease and Middle-East Respiratory Syndrome. We present a tool for estimating the reproduction number in real-time during infectious disease outbreaks accurately, which is available as an R software package (EpiEstim 2.2). It is also accessible as an interactive, user-friendly online interface (EpiEstim App), permitting its use by non-specialists. Our tool is easy to apply for assessing the transmission potential, and hence informing control, during future outbreaks of a wide range of invading pathogens. |
| Databáze: | OpenAIRE |
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