A Bayesian nonparametric method for detecting rapid changes in disease transmission. (7th February 2023)

• Record Type: Journal Article
• Authors: Creswell, Richard; Robinson, Martin; Gavaghan, David; Parag, Kris V.; Lei, Chon Lok; Lambert, Ben
• Is Part Of: Journal of theoretical biology. Volume 558(2023)
• Publication Date: 2023-02-07
• Subjects: Reproduction number -- Bayesian nonparametrics -- Outbreaks -- Epidemiology -- COVID-19 -- Changepoint detection; Biology -- Periodicals; Biological Science Disciplines -- Periodicals; Biology -- Periodicals; Biologie -- Périodiques; Theoretische biologie; Biology; Periodicals; 571.05
• DOI: 10.1016/j.jtbi.2022.111351
• Languages: English
• ISSNs: 0022-5193

Abstract

Abstract: Whether an outbreak of infectious disease is likely to grow or dissipate is determined through the time-varying reproduction number, R t . Real-time or retrospective identification of changes in R t following the imposition or relaxation of interventions can thus contribute important evidence about disease transmission dynamics which can inform policymaking. Here, we present a method for estimating shifts in R t within a renewal model framework. Our method, which we call EpiCluster, is a Bayesian nonparametric model based on the Pitman–Yor process. We assume that R t is piecewise-constant, and the incidence data and priors determine when or whether R t should change and how many times it should do so throughout the series. We also introduce a prior which induces sparsity over the number of changepoints. Being Bayesian, our approach yields a measure of uncertainty in R t and its changepoints. EpiCluster is fast, straightforward to use, and we demonstrate that it provides automated detection of rapid changes in transmission, either in real-time or retrospectively, for synthetic data series where the R t profile is known. We illustrate the practical utility of our method by fitting it to case data of outbreaks of COVID-19 in Australia and Hong Kong, where it finds changepoints coinciding with the imposition of non-pharmaceutical interventions. Bayesian nonparametric methods, such as ours, allow the volume and complexity of the data to dictate the number of parameters required to approximate the process and should find wide application in epidemiology. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics". Highlights: Identifying changes in transmission is important for epidemic control strategies. We model the time-varying reproduction number, R t, as piecewise-constant. We develop a Bayesian nonparametric method (EpiCluster) to infer changepoints in R t . Our method is adept at inferring changepoints on simulated series. EpiCluster identifies abrupt changes in R t for COVID-19 outbreaks in several countries.