| Citation: |
Susu Jia, Xinzhu Meng, Tonghua Zhang. THE EFFECTIVENESS OF HUMAN INTERVENTIONS AGAINST COVID-19 BASED ON EVOLUTIONARY GAME THEORY[J]. Journal of Applied Analysis & Computation, 2022, 12(5): 1748-1762. doi: 10.11948/20210269
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THE EFFECTIVENESS OF HUMAN INTERVENTIONS AGAINST COVID-19 BASED ON EVOLUTIONARY GAME THEORY
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Abstract
Social distancing strategy (including Six-Foot Rule, wearing masks, and other easy-to-operate measures) and quarantine measures have played a critical role in the early stage of the COVID-19 epidemic. In order to explore the mechanisms of these two human interventions accurately, we develop a coupling epidemiological-behavioral model based on evolutionary game theory. Individuals decide whether to take strategy measures based on rational consideration of payoffs. Moreover, authorities also balance the costs and effectiveness of the interventions at the public level. Our simulation shows that social distancing strategy can suppress every single outbreak effectively. In the early stage of an epidemic, the implementation of the quarantine measures determines the scale of the epidemic. Timely and effective quarantine measures can control recurrent outbreaks without social lockdown. Support policy for individual-level intervention or high diagnosis rates are beneficial to control the epidemic but require long-term social lockdown.
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Keywords:
- COVID-19 /
- mathematical epidemiology /
- imitation dynamics /
- quarantine /
- social distancing
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References
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Susu Jia, Xinzhu Meng, Tonghua Zhang. THE EFFECTIVENESS OF HUMAN INTERVENTIONS AGAINST COVID-19 BASED ON EVOLUTIONARY GAME THEORY[J]. Journal of Applied Analysis & Computation, 2022, 12(5): 1748-1762. doi: 10.11948/20210269
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- Figure 1. The epidemic flow between epidemiological classes (represented by solid black lines) and imitation flow between strategy classes (represented by gray dotted lines).
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Figure 2. Imitation speed
$ \rho $ sequence plots of each index. (a) The value of FIS with imitation speed axis$ \rho $ . (b) The value of A-peak and C-peak with imitation speed axis$ \rho $ . (c). The value of Lockdown with imitation speed axis$ \rho $ . (d). The scatter of delay with imitation speed axis$ \rho $ . - Figure 3. The time series plots of COVID-19 confirmed cases in Beijing and Liaoning province.
- Figure 4. Comparative simulation of quarantine measures. (a) The baseline scenario with quarantine. (b) The baseline scenario without quarantine. (c) Bar comparison chart of baseline scenario indicators on quarantine measures. (d). Bar comparison of indicators on quarantine measures at a higher diagnosis rate.
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Figure 5. The sensitivity analysis of
$ R_0 $ in baseline scenarios. (a) The sensitivity analysis of the scenario with quarantine measures. (b) The sensitivity analysis of the scenario without quarantine measures. - Figure 6. Percentage of changes of epidemic prevention indicators of support policy. (a) The effectiveness of support policy. (b) The comparison between quarantine and support policies quarantine. Percentage of changes in epidemic prevention indies of support policy.
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Figure 7. Simulation of interaction between two levels of human interventions. (a) The FIS and two peaks in the scenario with quarantine measures. (b) The FIS and the two peak values in the scenario without quarantine measures.
$ q_1\in(0.13,0.27) $ ,$ q_2\in(0.13,0.27) $ and$ \rho\in(50,1500) $ , and other parameters of the baseline scenario are reported in Table 1.