Stochastic agent-based modeling of infectious respiratory diseases with combined intervention strategies: A comprehensive framework for public health decision-making Stochastic ABM for disease control

Abstract

Traditional deterministic models for infectious diseases often overlook individual heterogeneity and stochastic events critical in outbreak dynamics. This study presents a comprehensive stochastic agent-based model to investigate the combined effects of face masks, quarantine, and vaccination on controlling respiratory diseases. The model simulates individual interactions in a population of 10,000 agents, capturing random transmission, intervention adherence variability, and complex contact patterns. Through extensive Monte Carlo simulations involving over 800 distinct scenario runs, we analyze outbreak probability, final epidemic size, peak prevalence, and intervention cost-effectiveness. Our results demonstrate that while combined interventions significantly reduce outbreak probability and scale, their effectiveness is highly sensitive to adherence levels and timing of implementation. The stochastic framework reveals substantial probabilities of major outbreaks even when the mean reproduction number is below unity, highlighting the critical importance of accounting for uncertainty in public health planning. Sensitivity analysis identifies contact rate and mask compliance as the most influential parameters, providing clear guidance for resource allocation. This approach provides crucial insights for designing robust, multi-layered intervention strategies that balance effectiveness with practical implementability in real-world settings.