A framework for airborne disease infection risk estimation with agent-based model and computational fluid dynamics - Ang, Boon Leng (Nanyang Technological University, 2024)
The emergence of the novel coronavirus disease (COVID-19) in late 2019 sparked a global pandemic, profoundly impacting societies and economies worldwide. To mitigate its spread, governments have implemented various preventive measures, prompting extensive research into assessing infection risk. To estimate airborne disease infection risk, we developed a framework integrating agent-based modelling (ABM) and Computational Fluid Dynamics (CFD). ABM simulated individual movements and behaviours, while CFD calculated their exposure level to virus particles. Using a preschool COVID-19 cluster in Singapore as a case study, individuals were classified into three groups based on their initial targets. Experimental data showed that an individual’s exposure level is nearly the same for all three groups but changes across time depending on their degree of active movement in the scenario. An evaluation of the framework showed moderate usability, high performance, and moderate scalability. This study contributes to the advancement of simulation modelling techniques for studying airborne disease transmission in crowded indoor spaces, facilitating informed decision-making in public health interventions.