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

TL;DR

This paper examines how assuming a uniform generation time distribution across populations can bias Rt estimates and proposes methods to account for population structure, improving epidemic tracking accuracy.

Contribution

It introduces a framework to select appropriate generation time distributions for structured populations and compares single-group versus multi-group Rt inference models.

Findings

Structured populations can significantly bias Rt estimates if not properly modeled.

A methodology is proposed to choose generation time distributions that reflect population heterogeneity.

Real epidemic data shows differences in Rt estimates depending on model choice.

Abstract

Due to its ability to summarise 'real-time' epidemic behaviour, the time-dependent reproduction number, Rt, is a useful metric for tracking pathogen transmission and quantifying the effects of interventions during infectious disease outbreaks. The predominant models underlying inferred Rt 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 Rt 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 Rt 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 Rt when, in fact, the population is structured. Finally, we use real epidemic data to demonstrate that practical Rt 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 Rt estimates that are used to guide public health policy responses.