COVID-19 Vaccine Distribution Policy Design with Reinforcement Learning

COVID-19 has become a global crisis and the vaccine has been seen as an effective approach to stop the epidemic spread. However, the resources for distributing and allocating different types of vaccines are limited and we need a better vaccine distribution policy design to prevent the spread of COVID-19 more efficiently. In this study, a pipeline of combing a random forest model and a DQN model is proposed. The random forest model is built to predict the daily new confirmed cases with the vaccine data as the inputs. And the DQN model is built to design the daily allocation ratio of three types of vaccines, with the aim to minimize the new confirmed cases. The experimental results based on the real-world datasets collected in San Diego validate the effectiveness of the proposed pipeline.