@article{Leong2026, 
author = {Fong Yew Leong and Jaeyoung Kwak and Zhengwei Ge and Chin Chun Ooi and Siew-Wai Fong and Matthew Zirui Tay and Hua Qian and Chang Wei Kang and Wentong Cai and Hongying Li},
title = {Impact of flow and human behaviour on airborne disease transmission},
year = {2026},
journal = {Building Simulation},
volume = {19},
number = {6},
pages = {1591-1604},
keywords = {airborne respiratory disease transmission, Wells-Riley infection model, computational fluid dynamics, agent-based modelling, coupled CFD-ABM, machine learning surrogate},
url = {https://www.sciopen.com/article/10.1007/s12273-026-1448-6},
doi = {10.1007/s12273-026-1448-6},
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 behaviour. Combining computational fluid dynamics (CFD), machine learning (ML), and agent-based modelling (ABM), our model captures the complex interplay between aerosol transport, human mobility, and environmental context. Based on a prototypical childcare centre, our approach quantifies how incorporation of ABM can unveil significantly different infection risk profiles across agents, with a 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 behaviour in others. Specifically, we identify low-risk modes (<0.1% probability) arising from source containment, as well as high-risk tails (10× higher risk at >1% probability) from prolonged close contact during mobile activities. 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.}
}