Modeling Infection with Multi-agent Dynamics

TL;DR

This paper presents a multi-agent Bayesian model to analyze infection spread in small populations, using real-world data from a student dormitory collected via cellular phones and symptom surveys.

Contribution

It introduces a novel approach combining proximity data and symptom reports to model infections in small communities, improving epidemic understanding.

Findings

Bayesian multi-agent model accurately tracks infection spread.

Proximity and symptom data provide key insights into transmission dynamics.

Model helps predict infection risks in small populations.

Abstract

Developing the ability to comprehensively study infections in small populations enables us to improve epidemic models and better advise individuals about potential risks to their health. We currently have a limited understanding of how infections spread within a small population because it has been difficult to closely track an infection within a complete community. The paper presents data closely tracking the spread of an infection centered on a student dormitory, collected by leveraging the residents' use of cellular phones. The data are based on daily symptom surveys taken over a period of four months and proximity tracking through cellular phones. We demonstrate that using a Bayesian, discrete-time multi-agent model of infection to model real-world symptom reports and proximity tracking records gives us important insights about infec-tions in small populations.