Impacts of people's learning behavior in fighting the COVID-19 epidemic

Cheng, Baolian; Wang, Yi-Ming

This work presents a mathematical model that captures time-dependent social-distancing effects and presents examples of the consequences of relaxing social-distancing restrictions in the fight against the novel coronavirus epidemic. Without social distancing, the spread of COVID-19 will grow exponentially, but social distancing and people's learning behavior (isolating, staying at home, wearing face masks, washing hands, restricting the size and frequency of group gatherings, etc.) can significantly impede the epidemic spread, flatten the infection curve, and change the final outcome of the COVID-19 outbreak. Our results demonstrate that strict social distancing and people's learning behavior can be effective in slowing the spread rate and significantly reducing the total number of infections, daily infection rate, peak of daily infections, and duration of the epidemic. Under strict social distancing, the rise and fall of infections would be nearly symmetric about the peak of of daily infections, and the epidemic spread would be essentially over within 60 days. Relaxing social distancing and people learning behaviors will significantly increase the total and daily numbers of infections and prolong the course of the outbreak. These results have immediate applications for the implementation of various social-distancing policies and general significance for ongoing outbreaks and similar infectious disease epidemics in the future.