Pandemic Control, Game Theory and Machine Learning
This work addresses pandemic control for policymakers and researchers, but appears incremental as it builds on existing game theory and ML approaches without claiming major breakthroughs.
The paper tackles the problem of COVID-19 intervention decision-making by developing mathematical models and machine learning methods to analyze policies and their regional impacts from a game theory perspective, but does not report specific numerical results.
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.