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Balancing mitigation strategies for viral outbreaks
Hamed Karami1, Pejman Sanaei1, Alexandra Smirnova1
1Department of Mathematics & Statistics, Georgia State University, Atlanta, USA.
Mathematical Biosciences and Engineering : MBE
|January 14, 2025
Summary
This study models infectious disease control, comparing scenarios with no intervention, social distancing, vaccination, and both. Combining social distancing and vaccination offers the most effective strategy for managing viral outbreaks.
Area of Science:
- Epidemiology
- Mathematical Biology
- Public Health
Background:
- Infectious disease outbreaks necessitate effective control and prevention strategies.
- Post-vaccination management is crucial for mitigating viral spread.
- Integrating social distancing and vaccination enhances disease control.
Purpose of the Study:
- To examine biological models for the post-vaccination stage of viral outbreaks.
- To analyze the combined impact of social distancing and vaccination on disease dynamics.
- To develop a computational algorithm for optimal control problems in public health.
Main Methods:
- Modeling five epidemic progression scenarios: no control, reconstructed, social distancing, vaccine, and combined controls.
- Utilizing computational algorithms for optimal control problems.
- Validating numerical results with real-world data from the COVID-19 Delta variant in the US.
Main Results:
- The 'both controls concurrently' scenario demonstrated superior effectiveness in managing epidemic spread.
- Social distancing reduces transmission rates, while vaccination boosts immunity.
- The study provides a robust algorithm for solving optimal control problems.
Conclusions:
- Combined social distancing and vaccination strategies are indispensable for controlling infectious diseases.
- The developed models offer valuable insights into disease dynamics and public health interventions.
- The findings support data-driven public health regulations for pandemic management.
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