Modeling Covid-19 Transmission Dynamics With Self-Learning Population Behavioral Change
Bibliographic Data
| ID | 22087737 |
|---|---|
| Authors | Tsz-Lik Chan (City University of Hong Kong), Hsiang-Yu Yuan (0000-0003-0061-0012, City University of Hong Kong), Wing-Cheong Lo (0000-0001-7771-5213, City University of Hong Kong, corresponding author) |
| Year | 2021 |
| Volume | 9 |
| Pages | 768852-768852 |
| Publication date | 2021-12-22 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2021.768852 |
| PMID | 35004580 |
| OpenAlex | W4200062041 |
| Language | EN |
| Citations received | 2 |
| References cited | 40 |
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
Disease · Environmental health · Pandemic · Population · Social distance · Telecommunications · Communication · Complex Network Analysis Techniques · Computer Science · COVID-19 epidemiological studies · Medicine · Mental Health Research Topics · Psychology
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| Citations per year | 0.67 |
|---|---|
| Citation span | 2023 - 2025 (3) |
| Highly cited | No |