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

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

8 Scopus Citations
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Detail(s)

Original languageEnglish
Article number768852
Journal / PublicationFrontiers in Public Health
Volume9
Online published22 Dec 2021
Publication statusPublished - Dec 2021

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Abstract

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.

Research Area(s)

  • COVID-19, delay differential equation, mathematical modeling, pandemic in Hong Kong, population behavioral change

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