Modeling COVID-19 Transmission Dynamics With Self-Learning Population Behavioral Change

Many regions observed recurrent outbreaks of COVID-19 cases after relaxing social distancing measures. It suggests that maintaining sufficient social distancing is important for limiting the spread of COVID-19. The change of population behavior responding to the social distancing measures becomes an important factor for the pandemic prediction. In this paper, we develop a SEAIR model for studying the dynamics of COVID-19 transmission with population behavioral change. In our model, the population is divided into several groups with their own social behavior in response to the delayed information about the number of the infected population. The transmission rate depends on the behavioral changes of all the population groups, forming a feedback loop to affect the COVID-19 dynamics. Based on the data of Hong Kong, our simulations demonstrate how the perceived cost after infection and the information delay affect the level and the time period of the COVID-19 waves.

Automated summary

This study examines the recurrent outbreaks of COVID-19 cases observed in many regions after relaxing social distancing measures, highlighting the importance of maintaining sufficient social distancing in limiting the spread of the virus. The research also emphasizes that population behavioral changes in response to social distancing measures play a crucial role in pandemic prediction. By developing a SEAIR model, the study demonstrates how perceived cost after infection and information delay impact the level and duration of COVID-19 waves, using data from Hong Kong.