The time-dependent reproduction number for epidemics in heterogeneous populations

TL;DR

This paper develops a renewal equation framework that incorporates population heterogeneity by age and other demographics to improve the estimation of the time-dependent reproduction number Rt in epidemics.

Contribution

It derives a novel analytical expression for Rt that accounts for differences in contact patterns and infection risks across groups, extending traditional homogeneous models.

Findings

The new Rt expression accurately predicts epidemic outcomes in simulations.

The model links group-specific contact and risk data to epidemic dynamics.

It provides a basis for more realistic epidemic modeling in diverse populations.

Abstract

The time-dependent reproduction number Rt can be used to track pathogen transmission and to assess the efficacy of interventions. This quantity can be estimated by fitting renewal equation models to time series of infectious disease case counts. These models almost invariably assume a homogeneous population. Individuals are assumed not to differ systematically in the rates at which they come into contact with others. It is also assumed that the typical time that elapses between one case and those it causes (known as the generation time distribution) does not differ across groups. But contact patterns are known to widely differ by age and according to other demographic groupings, and infection risk and transmission rates have been shown to vary across groups for a range of directly transmitted diseases. Here, we derive from first principles a renewal equation framework which accounts for these differences in transmission across groups. We use a generalisation of the classic McKendrick-von Foerster equation to handle populations structured into interacting groups. This system of partial differential equations allows us to derive a simple analytical expression for Rt which involves only group-level contact patterns and infection risks. We show that the same expression emerges from both deterministic and stochastic discrete-time versions of the model and demonstrate via simulations that our Rt expression governs the long-run fate of epidemics. Our renewal equation model provides a basis from which to account for more realistic, diverse populations in epidemiological models and opens the door to inferential approaches which use known group characteristics to estimate Rt.