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Understanding Effective Virus Control Policies for Covid-19 with the Q-learning Method

Author

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  • Yasin Khadem Charvadeh

    (University of Western Ontario)

  • Grace Y. Yi

    (University of Western Ontario
    University of Western Ontario)

Abstract

The case fatality rate of COVID-19 is a useful measure to describe the disease severity, which, however, changes considerably from country to country and from time to time. To reduce the detrimental impact of COVID-19, it is imperative to understand how different mitigation policies adopted by different countries may help lower the COVID-19 case fatality rate. Using data from 175 countries from January 13 of 2020 to March 9 of 2021, we investigate possible factors associated with the case fatality rate and use the Q-learning algorithm to assess optimal preventive policies adopted by individual countries to reduce their COVID-19 case fatality rates. Our analytical results suggest that country-specific characteristics and the baseline information of COVID-19 determine optimal preventive policies in the goal of lowering the case fatality rate. The study reveals that the factors significantly associated with the COVID-19 case fatality rate include the population proportion of elders ages 65 and over, gross domestic product per capita, obesity prevalence, substance use prevalence, and health system quality.

Suggested Citation

  • Yasin Khadem Charvadeh & Grace Y. Yi, 2024. "Understanding Effective Virus Control Policies for Covid-19 with the Q-learning Method," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 16(1), pages 265-289, April.
    • Handle: RePEc:spr:stabio:v:16:y:2024:i:1:d:10.1007_s12561-023-09382-w
    DOI: 10.1007/s12561-023-09382-w
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