The COVID-19 pandemic has caused more than 145 million cases and 3 million deaths worldwide to date. No therapeutic drugs are currently available for this novel coronavirus, and only recently several vaccines have been approved for emergency use. All measures to prevent the spread of COVID-19 were thus based on reducing contact between infected and susceptible individuals. Most of these measures such as quarantine and self-isolation require voluntary compliance by the population. However, humans may act in their (perceived) self-interest only. We construct a mathematical model of COVID-19 transmission with quarantine and hospitalization coupled with a dynamic game model of adaptive human behavior. Susceptible and infected individuals adopt various behavioral strategies based on perceived prevalence and burden of the disease and sensitivity to isolation measures, and they evolve their strategies using a social learning algorithm (imitation dynamics). This results in complex interplay between the epidemiological model, which affects success of different strategies, and the game-theoretic behavioral model, which in turn affects the spread of the disease. We found that the second wave of the pandemic, which has been observed in the US, can be attributed to rational behavior of susceptible individuals, and that multiple waves of the pandemic are possible if the rate of social learning of infected individuals is sufficiently high. To reduce the burden of the disease on the society, it is necessary to incentivize such altruistic behavior by infected individuals as voluntary self-isolation.
Link to the preprint: [ Ссылка ]
This is a keynote presentation for the 2021 Richmond Area Mathematical Sciences (RAMS) Conference taking place virtually on May 1, 2021 at the Virginia Commonwealth University: [ Ссылка ]
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