Identifying influential subpopulations in metapopulation epidemic models using message-passing theory

TL;DR

This paper develops a message-passing theory for metapopulation epidemic models to identify influential subpopulations, aiding targeted interventions and pandemic control strategies.

Contribution

It introduces a rigorous mathematical method to determine influential spreaders in metapopulation models, incorporating heterogeneity factors.

Findings

Accurately predicts influential subpopulations in real-world data.

Identifies the most dangerous city as a potential pandemic seed.

Validates theory with extensive simulations on empirical and synthetic networks.

Abstract

Identifying influential subpopulations in metapopulation epidemic models has far-reaching potential implications for surveillance and intervention policies of a global pandemic. However, there is a lack of methods to determine influential nodes in metapopulation models based on a rigorous mathematical background. In this study, we derive the message-passing theory for metapopulation modeling and propose a method to determine influential spreaders. Based on our analysis, we identify the most dangerous city as a potential seed of a pandemic when applied to real-world data. Moreover, we particularly assess the relative importance of various sources of heterogeneity at the subpopulation level, e.g., the number of connections and mobility patterns, to determine properties of spreading processes. We validate our theory with extensive numerical simulations on empirical and synthetic networks considering various mobility and transmission probabilities. We confirm that our theory can accurately predict influential subpopulations in metapopulation models.