@article{fd561ac9787447698de217d9d9709ce1,
title = "Improved inference of time-varying reproduction numbers during infectious disease outbreaks",
abstract = "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.",
keywords = "Disease control, Infectious disease epidemiology, Mathematical modelling, Parameter inference, Reproduction number, Serial interval",
author = "Thompson, {R. N.} and Stockwin, {J. E.} and {van Gaalen}, {R. D.} and Polonsky, {J. A.} and Kamvar, {Z. N.} and Demarsh, {P. A.} and E. Dahlqwist and S. Li and E. Miguel and T. Jombart and J. Lessler and S. Cauchemez and A. Cori",
note = "Funding Information: 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{\textquoteright}Avenir program , the Laboratoire d{\textquoteright}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) . The results of this work do not necessarily reflect the views of USAID or any other funding body. Funding Information: 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). The results of this work do not necessarily re?ect the views of USAID or any other funding body. Publisher Copyright: {\textcopyright} 2019 The Authors",
year = "2019",
month = dec,
doi = "10.1016/j.epidem.2019.100356",
language = "English (US)",
volume = "29",
journal = "Epidemics",
issn = "1755-4365",
publisher = "Elsevier",
}