A note on metapopulation models

TL;DR

This paper examines how ignoring individual variation within patches in metapopulation models leads to underestimating disease spread potential and intervention efforts, and proposes a method to incorporate heterogeneity for better modeling, demonstrated through COVID-19 case study.

Contribution

It introduces a scheme to infer individual susceptibility distributions within patches, enhancing metapopulation models by accounting for in-patch heterogeneity, especially in epidemiological contexts.

Findings

Neglecting in-patch heterogeneity underestimates $ ext{R}_0$ and control efforts.

Proposed method infers susceptibility distributions from population stratifications.

Application to COVID-19 demonstrates improved modeling accuracy.

Abstract

Metapopulation models are commonly used in ecology, evolution, and epidemiology. These models usually entail homogeneity assumptions within patches and study networks of migration between patches to generate insights into conservation of species, differentiation of populations, and persistence of infectious diseases. Here, focusing on infectious disease epidemiology, we take a complementary approach and study the effects of individual variation within patches while neglecting any form of disease transmission between patches. Consistently with previous work on single populations, we show how metapopulation models that neglect in-patch heterogeneity also underestimate basic reproduction numbers ($\mathcal{R}_{0}$) and the effort required to control or eliminate infectious diseases by uniform interventions. We then go beyond this confirmatory result and introduce a scheme to infer distributions of individual susceptibility or exposure to infection based on suitable stratifications of a population into patches. We apply the resulting metapopulation models to a simple case study of the COVID-19 pandemic.