Villaplana, H. D. L., Kwak, J., Lees, M. H., Li, H., & Cai, W. (2024). Application of Generative Artificial Intelligence for Epidemic Modeling. 2024 Winter Simulation Conference (WSC), 2727–2738. https://doi.org/10.1109/wsc63780.2024.10838918
Abstract:
Epidemic models have become increasingly useful, especially in the wake of the recent COVID-19 pandemic, emphasizing the crucial role of human behavior in the spread of disease. There has been a recent rise in the usage and popularity of generative artificial intelligence (GenAI), such as ChatGPT especially with its ability to mimic human behavior. In this study, we demonstrate a novel application of GenAI for epidemic modeling. We employed GenAI for creating agents living in a hypothetical town in simulations and simulating their behavior within the context of an ongoing pandemic. We performed a series of simulations to quantify the impact of agent traits and the availability of information for health condition, virus, and government guidelines on the disease spread patterns in terms of peak time and epidemic duration. We also characterized the most influential factors in agents' decision-making using random forest model.
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Funding Info:
This research / project is supported by the Singapore Ministry of Health’s National Medical Research Council - National Epidemic Preparedness and Response R&D Funding Initiative Programme for Research in Epidemic Preparedness And REsponse (PREPARE)
Grant Reference no. : MOH-001041