CompARE: A Computational framework for Airborne Respiratory disease Evaluation integrating flow physics and human behavior

• URL: http://arxiv.org/abs/2511.21782v1
• Date: Wed, 26 Nov 2025 12:53:30 GMT
• Title: CompARE: A Computational framework for Airborne Respiratory disease Evaluation integrating flow physics and human behavior
• Authors: Fong Yew Leong, Jaeyoung Kwak, Zhengwei Ge, Chin Chun Ooi, Siew-Wai Fong, Matthew Zirui Tay, Hua Qian, Chang Wei Kang, Wentong Cai, Hongying Li,
• Abstract summary: The risk of indoor airborne transmission among co-located individuals is generally non-uniform.<n>We present CompARE, an integrated risk assessment framework for indoor airborne disease transmission.
• Score: 4.272788079372737
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The risk of indoor airborne transmission among co-located individuals is generally non-uniform, which remains a critical challenge for public health modelling. Thus, we present CompARE, an integrated risk assessment framework for indoor airborne disease transmission that reveals a striking bimodal distribution of infection risk driven by airflow dynamics and human behavior. Combining computational fluid dynamics (CFD), machine learning (ML), and agent-based modeling (ABM), our model captures the complex interplay between aerosol transport, human mobility, and environmental context. Based on a prototypical childcare center, our approach quantifies how incorporation of ABM can unveil significantly different infection risk profiles across agents, with more than two-fold change in risk of infection between the individuals with the lowest and highest risks in more than 90% of cases, despite all individuals being in the same overall environment. We found that infection risk distributions can exhibit not only a striking bimodal pattern in certain activities but also exponential decay and fat-tailed behavior in others. Specifically, we identify low-risk modes arising from source containment, as well as high-risk tails from prolonged close contact. Our approach enables near-real-time scenario analysis and provides policy-relevant quantitative insights into how ventilation design, spatial layout, and social distancing policies can mitigate transmission risk. These findings challenge simple distance-based heuristics and support the design of targeted, evidence-based interventions in high-occupancy indoor settings.

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