Hybrid model reveals how jobs and compliance shape pandemic outcomes
ABM-SIRTEM: A Hybrid Agent-Based and Epidemiological Model for Pandemic Response
Multiagent SystemsComputer Science and Game Theory
Summary
The COVID-19 pandemic hit some groups harder than others, especially people in jobs needing lots of face-to-face contact. The authors created a new tool that combines two ways of studying disease spread: one looks at overall patterns in populations, and the other simulates individuals interacting. Their model includes different job types, how work affects the economy, and how people follow health rules over time. They tested the model with real COVID-19 data from four U.S. states to better understand how health measures and social behavior interact during a pandemic.
What this means in practice
- •For public health planners: Develop targeted pandemic policies by simulating how different job types and compliance rates affect disease spread and economy.
- •For economic policy teams: Evaluate the trade-offs between health interventions and economic output using a detailed model of workforce behavior and disease dynamics.
Authors
Sheryl Paul, Samuel Williams, Preetom K. Biswas, Giulia Pedrielli, Jyotirmoy V. Deshmukh
Abstract
The COVID-19 pandemic has had profound impacts on global health, social structures, and economies. It disproportionately affected lower socioeconomic groups and those reliant on interaction-based jobs. Regulatory bodies faced the challenge of designing policies that preserve public health while limiting disruption to economic stability and productivity. Epidemiological models such as SIR and agent-based models (ABMs) have been used to study disease dynamics and the socioeconomic impacts of disease and interventions. Population-level models often simplify individual heterogeneity, while detailed ABMs can become computationally expensive as the numbers of agents and interactions increase. We propose ABM-SIRTEM, a hybrid model that incorporates occupation categories, economic productivity, and welfare at the individual level while dynamically modeling compliance with government interventions. We calibrate the model against historical positive and negative test counts from four U.S. states and examine the resulting compliance dynamics. This framework provides a basis for studying the interaction between disease spread and socioeconomic behavior in pandemic-response planning.