https://orcid.org/0000-0001-5577-5880
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ABSTRACT
Close-range interpersonal interactions serve as a major channel for virus transmission, with higher infection risks indoors than outdoors. Thus, evaluating indoor infectious disease transmission risks is vital for effective epidemic prevention and control. However, collecting complete individual-level behavioral data faces challenges due to privacy concerns and data acquisition costs, impeding accurate indoor risk mapping. To address this, we propose an individual-centered, scenario-based simulation framework in this paper. This framework incorporates stepwise movement of agents to model crowd interactions in indoor spaces, enabling infection risk mapping through geographic scenario simulation. The simulation model’s core components encompass the generation of indoor environments, formulation of individual behavior rules, establishment of human-environment interaction logic, and simulation of virus transmission processes. Additionally, we outline the implementation algorithm for this simulation model. Lastly, we employ a high-risk university canteen as a case study to demonstrate the model’s capabilities in creating risk maps at different levels: spatial, spatiotemporal, and individual. The proposed framework achieves the construction and process simulation of multi-dimensional geographic scenarios at a microscale, introduces a behavior path model based on spatiotemporal cubes, and enhances the comprehensive analysis and mapping capabilities for indoor infectious disease risk, laying the foundation for precise prevention and control.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The experimental dataset and source codes are available on GitHub repository: https://github.com/Texas001/MOBIS.