Epidemic modeling in metapopulation systems with heterogeneous coupling pattern: theory and simulations

TL;DR

This paper develops a theoretical framework and simulations to understand how heterogeneous transportation networks influence epidemic spread and invasion thresholds in metapopulation systems.

Contribution

It introduces a reaction-diffusion model with degree-based variables to analyze epidemic thresholds in heterogeneous networks, including an explicit invasion threshold formula.

Findings

Heterogeneous networks lower the invasion threshold.

The invasion threshold depends on network heterogeneity and disease parameters.

Simulations confirm analytical predictions across various network structures.

Abstract

The spatial structure of populations is a key element in the understanding of the large scale spreading of epidemics. Motivated by the recent empirical evidence on the heterogeneous properties of transportation and commuting patterns among urban areas, we present a thorough analysis of the behavior of infectious diseases in metapopulation models characterized by heterogeneous connectivity and mobility patterns. We derive the basic reaction-diffusion equation describing the metapopulation system at the mechanistic level and derive an early stage dynamics approximation for the subpopulation invasion dynamics. The analytical description uses degree block variables that allows us to take into account arbitrary degree distribution of the metapopulation network. We show that along with the usual single population epidemic threshold the metapopulation network exhibits a global threshold for the subpopulation invasion. We find an explicit analytic expression for the invasion threshold that determines the minimum number of individuals traveling among subpopulations in order to have the infection of a macroscopic number of subpopulations. The invasion threshold is a function of factors such as the basic reproductive number, the infectious period and the mobility process and it is found to decrease for increasing network heterogeneity. We provide extensive mechanistic numerical Monte Carlo simulations that recover the analytical finding in a wide range of metapopulation network connectivity patterns. The results can be useful in the understanding of recent data driven computational approaches to disease spreading in large transportation networks and the effect of containment measures such as travel restrictions.