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

2025-11-26

AI Summary

Indoor airborne infection risk exhibits pronounced inter-individual heterogeneity, which conventional distance-based models fail to capture. This study develops a physics- and behavior-informed risk assessment framework integrating computational fluid dynamics (CFD), machine learning (ML), and agent-based modeling (ABM) to jointly simulate aerosol transport, human mobility, and environmental interactions. We uncover, for the first time, a bimodal risk distribution: a low-risk mode arising from effective source-zone containment, and a high-risk tail driven by prolonged close-proximity exposure—exhibiting both exponential decay and heavy-tailed characteristics. In a daycare center case study, over 90% of scenarios show more than twofold disparity between highest- and lowest-risk individuals. The framework enables near-real-time scenario analysis for ventilation optimization, spatial reconfiguration, and targeted social distancing, delivering quantifiable, evidence-based support for public health interventions.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Models indoor airborne disease transmission risk distribution
• Integrates airflow dynamics and human behavior via CFD, ML, ABM
• Evaluates ventilation, layout, distancing to mitigate infection risk

Innovation

• Integrates CFD, machine learning, and agent-based modeling
• Quantifies infection risk distribution using agent-based simulations
• Enables real-time scenario analysis for policy insights