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Optimal control analysis of a multigroup SEAIHRD model for COVID-19 epidemic
1School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China.
Risk Analysis : an Official Publication of the Society for Risk Analysis
|September 13, 2022
Summary
This study models COVID-19 spread using a SEAIHRD model across three age groups. Optimal control strategies involving nonpharmaceutical interventions and vaccination can contain the outbreak, prioritizing the prime age group.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Public Health
Background:
- The COVID-19 pandemic poses significant global public health and economic threats.
- Understanding disease dynamics across different age demographics is crucial for effective control.
Purpose of the Study:
- To develop and analyze optimal control strategies for mitigating COVID-19 spread and its associated impacts.
- To evaluate the effectiveness of nonpharmaceutical interventions (NPIs) and vaccination in achieving herd immunity.
Main Methods:
- A multigroup susceptible-exposed-asymptomatic-infectious-hospitalized-recovered-dead (SEAIHRD) compartment model was constructed for three age groups: young, prime, and elderly.
- An optimal control problem with free terminal time and partially fixed terminal state was formulated to minimize deaths and costs.
- SARS-CoV-2 transmission rates were calibrated using early-stage US epidemic data.
Main Results:
- Numerical simulations indicate that controlling the prime age group's transmission is key to containing the COVID-19 outbreak.
- Implementing strict control measures for young and elderly populations alongside prime age group control is effective.
- Prioritizing strict nonpharmaceutical interventions before vaccine availability is essential.
Conclusions:
- Targeted control strategies, focusing on the prime age demographic, can effectively manage COVID-19 outbreaks.
- A combination of NPIs and vaccination, strategically applied across age groups, is vital for pandemic control.
- Mathematical modeling provides valuable insights for optimizing public health interventions during epidemics.
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