Exploring individuals’ effective preventive measures against epidemics through reinforcement learning* *Project supported by the National Key Technology Research and Development Program of China (Grant No. 2018YFF0301000) and the National Natural Science Foundation of China (Grant Nos. 71673161 and 71790613).

Abstract

Individuals’ preventive measures, as an effective way to suppress epidemic transmission and to protect themselves from infection, have attracted much academic concern, especially during the COVID-19 pandemic. In this paper, a reinforcement learning-based model is proposed to explore individuals’ effective preventive measures against epidemics. Through extensive simulations, we find that the cost of preventive measures influences the epidemic transmission process significantly. The infection scale increases as the cost of preventive measures grows, which means that the government needs to provide preventive measures with low cost to suppress the epidemic transmission. In addition, the effective preventive measures vary from individual to individual according to the social contacts. Individuals who contact with others frequently in daily life are highly recommended to take strict preventive measures to protect themselves from infection, while those who have little social contacts do not need to take any measures considering the inevitable cost. Our research contributes to exploring the effective measures for individuals, which can provide the government and individuals useful suggestions in response to epidemics.