Pandemic Control, Game Theory and Machine Learning

TL;DR

This paper explores how game theory and machine learning can inform COVID-19 intervention policies, analyzing decision-making processes and regional impacts to improve disease control strategies.

Contribution

It introduces mathematical models and machine learning methods for COVID-19 intervention decision-making from a game theory perspective.

Findings

Analysis of authorities' decisions on regional COVID-19 policies

Mathematical models explaining policy impacts

Machine learning methods for policy optimization

Abstract

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.