Assessing epidemic curves for evidence of superspreading.

ABSTRACT: The expected number of secondary infections arising from each index case, referred to as the reproduction or R number, is a vital summary statistic for understanding and managing epidemic diseases. There are many methods for estimating R ; however, few explicitly model heterogeneous disease reproduction, which gives rise to superspreading within the population. We propose a parsimonious discrete-time branching process model for epidemic curves that incorporates heterogeneous individual reproduction numbers. Our Bayesian approach to inference illustrates that this heterogeneity results in less certainty on estimates of the time-varying cohort reproduction number R t . We apply these methods to a COVID-19 epidemic curve for the Republic of Ireland and find support for heterogene

SUBMITTER: Meagher J 

PROVIDER: S-EPMC10092342 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature