Simulating nationwide coupled disease and fear spread in an agent-based model
Joy Kitson, Prescott C. Alexander, Joseph Tuccillo, David J. Butts, Christa Brelsford, Abhinav Bhatele, Sara Y. Del Valle, and Timothy C. Germann

TL;DR
This paper introduces a comprehensive agent-based model that simulates the coupled spread of disease and fear, revealing complex feedback effects and multiple epidemic waves influenced by behavioral responses and media broadcast.
Contribution
The study develops a novel dynamic agent-based model coupling disease and fear spread, incorporating realistic individual interactions and media influence, to analyze their combined impact on epidemic dynamics.
Findings
Asymptomatic and pre-symptomatic states affect outbreak progression.
Non-local fear spread via media leads to multiple epidemic waves.
Behavioral responses significantly alter disease trajectory.
Abstract
Human cognitive responses, behavioral responses, and disease dynamics co-evolve over the course of any disease outbreak, and can result in complex feedbacks. We present a dynamic agent-based model that explicitly couples the spread of disease with the spread of fear surrounding the disease, implemented within the EpiCast simulation framework. EpiCast models transmission across a realistic synthetic population, capturing individual-level interactions. In our model, fear propagates through both in-person contact and broadcast media, prompting individuals to adopt protective behaviors that reduce disease spread. In order to better understand these coupled dynamics, we create and compare a range of compartmental surrogate models to analyze the impact of including various disease states. Additionally, we compare a range of behavioral scenarios within EpiCast, varying the level and intensity…
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Taxonomy
TopicsCOVID-19 epidemiological studies · Mathematical and Theoretical Epidemiology and Ecology Models · Data-Driven Disease Surveillance
MethodsADaptive gradient method with the OPTimal convergence rate
