2021/06/22 by Joe Meagher, Nial Friel, Meagher, Joe +1
Biochemistry, Genetics and Molecular Biology · Decision Sciences · Mathematics · #Applications (stat.AP) #COVID-19 epidemiological studies #Evolution and Genetic Dynamics #FOS: Biological sciences #FOS: Computer and information sciences #Populations and Evolution (q-bio.PE) #demographic modeling and climate adaptation
paper · pdf · doi:10.48550/arxiv.2106.12064
openalex publication_date 2021/06/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 Rt. We apply these methods to a COVID-19 epidemic curve for the Republic of Ireland and find support for heterogeneous disease reproduction. Our analysis allows us to estimate the expected proportion of secondary infections attributable to the most infectious proportion of the population. For example, we estimate that the 20% most infectious index cases account for approximately 75-98% of the expected secondary infections with 95% posterior probability. In addition, we highlight that heterogeneity is a vital consideration when estimating Rt.