Epidemic Spread and Control Strategies: A Spatial-individual Agent-based Modeling and Optimization Approach.

Authors

Xiang Yu Zhang, Zhi Dong Cao, Tian Yi Luo, Jiao Jiao Wang, Hong Bin Song, Li Gui Wang

Published in

Biomedical and environmental sciences : BES. Volume 39. Issue 7. Pages 745-757. Jul 20, 2026.

Abstract

Traditional disease prevention strategies that rely on fixed parameters and macro-level models struggle to capture the diversity of individual behaviors and environmental complexities. Indoor spaces with high population densities and poor ventilation, such as schools and hospitals, are particularly vulnerable to pathogen transmission. The coronavirus disease (COVID-19) pandemic highlighted the need for precise intervention strategies.
We developed a spatial-individual agent-based model that integrates fine-grained spatiotemporal dynamics, where transmission risk is quantified by the exact distance and duration of contact. This model was applied to a high-resolution case study of a university dormitory floor to evaluate various testing frequencies, scopes, and isolation intensities.
Simulations showed that a dormitory-wide isolation policy outperformed individual restrictions by protecting uninfected rooms. Counter-intuitively, every-three-day class-based testing lowered infection risks compared to daily class-based testing by minimizing high-density interactions. In spatially constrained environments, stricter isolation reduces the overall outbreak duration but increases the contact transmission rate among individuals sharing the same enclosed space.
Epidemic control in high-density environments requires balancing testing frequency and isolation stringency based on spatial constraints. Under strict isolation, frequent testing is vital for breaking transmission chains. In less restrictive settings, moderately reducing the testing frequency minimizes unnecessary contact. These findings provide data-driven guidance for optimizing public health policies on campuses.

PMID:
42559878
Bibliographic data and abstract were imported from PubMed on 06 Aug 2026.

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