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

TL;DR

CompARE is an integrated computational framework combining CFD, ML, and ABM to assess indoor airborne disease transmission risk, revealing complex bimodal and tail behaviors influenced by airflow and human activity.

Contribution

This work introduces a novel framework that combines fluid dynamics, machine learning, and agent-based modeling to evaluate infection risks in indoor environments, providing detailed risk profiles.

Findings

Risk profiles vary significantly across individuals within the same environment.

Infection risk distributions can be bimodal, exponential, or fat-tailed depending on activity.

Ventilation and spatial layout significantly influence transmission risk.

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.