Game theory has been an effective tool in the control of disease spread and in suggesting optimal policies at both individual and area levels. In this AMS Notices article, we focus on the decision-making development for the intervention of COVID-19, aiming to provide mathematical models and efficient machine learning methods, and justifications for related policies that have been implemented in the past and explain how the authorities' decisions affect their neighboring regions from a game theory viewpoint.
@article{arxiv.2208.08646,
title = {Pandemic Control, Game Theory and Machine Learning},
author = {Yao Xuan and Robert Balkin and Jiequn Han and Ruimeng Hu and Hector D. Ceniceros},
journal= {arXiv preprint arXiv:2208.08646},
year = {2022}
}