Essential Workers at Risk: An Agent-Based Model (SAFE-ABM) with Bayesian Uncertainty Quantification

TL;DR

This paper introduces SAFE-ABM, an agent-based simulation framework with Bayesian uncertainty quantification, to evaluate and optimize intervention strategies protecting essential workers during pandemics.

Contribution

It presents a novel simulation framework combining agent-based modeling with Bayesian UQ to assess targeted interventions for essential workers in pandemic scenarios.

Findings

Workforce rotation with quarantine most effectively limits outbreaks.

Mobility restrictions reduce overall transmission but may impact societal functions.

The Bayesian UQ captures variability in key epidemiological parameters.

Abstract

Essential workers face elevated infection risks due to their critical roles during pandemics, and protecting them remains a significant challenge for public health planning. This study develops SAFE-ABM, a simulation-based framework using Agent-Based Modeling (ABM), to evaluate targeted intervention strategies, explicitly capturing structured interactions across families, workplaces, and schools. We simulate key scenarios such as unrestricted movement, school closures, mobility restrictions specific to essential workers, and workforce rotation, to assess their impact on disease transmission dynamics. To ensure robust uncertainty assessment, we integrate a novel Bayesian Uncertainty Quantification (UQ) framework, systematically capturing variability in transmission rates, recovery times, and mortality estimates. Our comparative analysis demonstrates that while general mobility restrictions reduce overall transmission, a workforce rotation strategy for essential workers, when combined with quarantine enforcement, most effectively limits workplace outbreaks and secondary family infections. Unlike other interventions, this approach preserves a portion of the susceptible population, resulting in a more controlled and sustainable epidemic trajectory. These findings offer critical insights for optimizing intervention strategies that mitigate disease spread while maintaining essential societal functions.