The COVID-19 pandemic highlighted two key lessons:
- The urgent need to prevent and control disease transmission in large indoor spaces and at mass gathering events.
- The lack of effective technological tools to assess and evaluate infection risks in these settings, tools that could guide public health interventions to maximize health benefits while minimizing social and economic costs.
To address this critical gap, our project aims to develop an artificial intelligence (AI) application that enables building operators, event organizers, and health and safety managers to assess disease transmission risks in both existing and planned spaces.
This interdisciplinary project combines three powerful methods, Computational Fluid Dynamics (CFD), Agent-Based Modeling (ABM), and Artificial Intelligence (AI), supported by mathematical modeling and epidemiology.
- AI reconstructs floor plans and geometrical features of indoor spaces and event sites.
- These reconstructions create computational domains for CFD simulations, which solve fluid flow equations to generate detailed, three-dimensional insights into airflow patterns under different layouts, ventilation strategies, crowd densities, and wind conditions.
- The CFD results then inform an ABM framework, which models crowd movement and interactions to predict the risk of exposure to airborne diseases.
The resulting computational framework will be applicable across a wide range of indoor environments, schools, hospitals, workplaces, transportation systems, places of worship, sports facilities, shopping malls, as well as large events such as sporting matches and religious gatherings, in Ontario, Canada, and worldwide.
