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Chin. Phys. B, 2021, Vol. 30(4): 048901    DOI: 10.1088/1674-1056/abcfa5

Exploring individuals' effective preventive measures against epidemics through reinforcement learning

Ya-Peng Cui(崔亚鹏)1,2,3, Shun-Jiang Ni (倪顺江)1,2,3,†, and Shi-Fei Shen(申世飞)1,2,3
1 Institute of Public Safety Research, Tsinghua University, Beijing 100084, China; 2 Department of Engineering Physics, Tsinghua University, Beijing 100084, China; 3 Beijing Key Laboratory of City Integrated Emergency Response Science, Beijing 100084, China
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.
Keywords:  epidemic simulation      complex networks      reinforcement learning      preventive measures  
Received:  22 September 2020      Revised:  21 October 2020      Accepted manuscript online:  02 December 2020
PACS:  89.75.Hc (Networks and genealogical trees)  
Fund: 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).
Corresponding Authors:  Corresponding author. E-mail:   

Cite this article: 

Ya-Peng Cui(崔亚鹏), Shun-Jiang Ni (倪顺江), and Shi-Fei Shen(申世飞) Exploring individuals' effective preventive measures against epidemics through reinforcement learning 2021 Chin. Phys. B 30 048901

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