Human behavior-driven epidemic surveillance in urban landscapes

TL;DR

This paper presents a novel urban epidemic surveillance method that uses human mobility and behavior data to identify critical transmission pathways, enabling targeted testing and improved outbreak response in cities.

Contribution

It introduces a mobility-informed surveillance strategy that leverages human behavior data to optimize epidemic detection and response in urban environments.

Findings

Targeted testing at key transit stations improves detection efficiency.

Mobility data enhances early outbreak alerts.

Urban layout influences strategy effectiveness.

Abstract

We introduce a surveillance strategy specifically designed for urban areas to enhance preparedness and response to disease outbreaks by leveraging the unique characteristics of human behavior within urban contexts. By integrating data on individual residences and travel patterns, we construct a Mixing matrix that facilitates the identification of critical pathways that ease pathogen transmission across urban landscapes enabling targeted testing strategies. Our approach not only enhances public health systems' ability to provide early epidemiological alerts but also underscores the variability in strategy effectiveness based on urban layout. We prove the feasibility of our mobility-informed policies by mapping essential mobility flows to major transit stations, showing that few resources focused on specific stations yields a more effective surveillance than non-targeted approaches. This study emphasizes the critical role of integrating human behavioral patterns into epidemic management strategies to improve the preparedness and resilience of major cities against future outbreaks.