Improved inference of time-varying reproduction numbers during infectious disease outbreaks

TLDR

Accurate estimation of transmission parameters, especially the time‑dependent reproduction number, is essential for optimizing epidemic control and relies on case counts and serial‑interval distribution. The study aims to demonstrate that real‑time transmissibility estimates require up‑to‑date serial‑interval data and distinguishing local from imported cases, and to provide a user‑friendly tool (EpiEstim) for this purpose. The authors develop a real‑time estimation tool (EpiEstim) that incorporates up‑to‑date serial‑interval data, distinguishes local from imported cases, and is available as an R package and interactive web app. Using data from H1N1, Ebola, and MERS outbreaks, the tool accurately infers transmissibility, proving its ease of application for assessing transmission potential and informing control of diverse pathogens.

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.