Misspecification of the generation time distribution and its impact on R_t estimates in structured populations.

Authors

Ioana Bouros, Robin N Thompson, David Gavaghan, Ben Lambert

Published in

Journal of theoretical biology. Pages 112536. Jun 23, 2026. Epub Jun 23, 2026.

Abstract

Due to its ability to summarise 'real-time' epidemic behaviour, the time-dependent reproduction number, R_t, is a useful metric for tracking pathogen transmission and quantifying the effects of interventions during infectious disease outbreaks. The predominant models underlying inferred R_t trajectories are renewal equations, their success owing in part to the relatively few assumptions they require. One necessary assumption is the generation time distribution, which summarises the time periods between infections in infector-infectee transmission pairs. This distribution is typically assumed to be the same across all members of a population. In reality, however, it may vary systematically between population groups. In this study, we consider two R_t inference frameworks based on renewal equation models: one for a single, homogeneous group and another accounting for a structured population. We compare the estimates of R_t generated by the two models and investigate, both analytically and through simulations, under which conditions the conclusions drawn from these modelling paradigms differ. We also demonstrate a methodology for selecting the generation time for the one-group model that correctly encapsulates variations between different population groups; this allows us to use a renewal framework for a one-group model to infer R_t when, in fact, the population is structured. Finally, we use real epidemic data to demonstrate that practical R_t estimates can differ depending on whether the underlying model is the one-group model or the multi-group model. Our results motivate the need for rigorous collection of detailed epidemic data and consideration of differences between population groups to improve the accuracy of R_t estimates that are used to guide public health policy responses.

PMID:
42336268
Bibliographic data and abstract were imported from PubMed on 24 Jun 2026.

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