Balancing mitigation strategies for viral outbreaks
Control and prevention strategies are indispensable tools for managing the spread of infectious diseases. This paper examined biological models for the post-vaccination stage of a viral outbreak that integrate two important mitigation tools: social distancing, aimed at reducing the disease transmiss...
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| Format: | Article |
| Language: | English |
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AIMS Press
2024-12-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2024337 |
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| author | Hamed Karami Pejman Sanaei Alexandra Smirnova |
| author_facet | Hamed Karami Pejman Sanaei Alexandra Smirnova |
| author_sort | Hamed Karami |
| collection | DOAJ |
| description | Control and prevention strategies are indispensable tools for managing the spread of infectious diseases. This paper examined biological models for the post-vaccination stage of a viral outbreak that integrate two important mitigation tools: social distancing, aimed at reducing the disease transmission rate, and vaccination, which boosts the immune system. Five different scenarios of epidemic progression were considered: (ⅰ) the 'no control' scenario, reflecting the natural evolution of a disease without any safety measures in place, (ⅱ) the 'reconstructed' scenario, representing real-world data and interventions, (ⅲ) the 'social distancing control' scenario covering a broad set of behavioral changes, (ⅳ) the 'vaccine control' scenario demonstrating the impact of vaccination on epidemic spread, and (ⅴ) the 'both controls concurrently' scenario incorporating social distancing and vaccine controls simultaneously. By comparing these scenarios, we provided a comprehensive analysis of various intervention strategies, offering valuable insights into disease dynamics. Our innovative approach to modeling the cost of control gave rise to a robust computational algorithm for solving optimal control problems associated with different public health regulations. Numerical results were supported by real data for the Delta variant of the COVID-19 pandemic in the United States. |
| format | Article |
| id | doaj-art-865282db56b74b2b86fde7eb04ff6f89 |
| institution | Kabale University |
| issn | 1551-0018 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj-art-865282db56b74b2b86fde7eb04ff6f892025-01-23T05:05:30ZengAIMS PressMathematical Biosciences and Engineering1551-00182024-12-0121127650768710.3934/mbe.2024337Balancing mitigation strategies for viral outbreaksHamed Karami0Pejman Sanaei1Alexandra Smirnova2Department of Mathematics & Statistics, Georgia State University, Atlanta, USADepartment of Mathematics & Statistics, Georgia State University, Atlanta, USADepartment of Mathematics & Statistics, Georgia State University, Atlanta, USAControl and prevention strategies are indispensable tools for managing the spread of infectious diseases. This paper examined biological models for the post-vaccination stage of a viral outbreak that integrate two important mitigation tools: social distancing, aimed at reducing the disease transmission rate, and vaccination, which boosts the immune system. Five different scenarios of epidemic progression were considered: (ⅰ) the 'no control' scenario, reflecting the natural evolution of a disease without any safety measures in place, (ⅱ) the 'reconstructed' scenario, representing real-world data and interventions, (ⅲ) the 'social distancing control' scenario covering a broad set of behavioral changes, (ⅳ) the 'vaccine control' scenario demonstrating the impact of vaccination on epidemic spread, and (ⅴ) the 'both controls concurrently' scenario incorporating social distancing and vaccine controls simultaneously. By comparing these scenarios, we provided a comprehensive analysis of various intervention strategies, offering valuable insights into disease dynamics. Our innovative approach to modeling the cost of control gave rise to a robust computational algorithm for solving optimal control problems associated with different public health regulations. Numerical results were supported by real data for the Delta variant of the COVID-19 pandemic in the United States.https://www.aimspress.com/article/doi/10.3934/mbe.2024337epidemiologycompartmental modeltransmission dynamicoptimal control |
| spellingShingle | Hamed Karami Pejman Sanaei Alexandra Smirnova Balancing mitigation strategies for viral outbreaks Mathematical Biosciences and Engineering epidemiology compartmental model transmission dynamic optimal control |
| title | Balancing mitigation strategies for viral outbreaks |
| title_full | Balancing mitigation strategies for viral outbreaks |
| title_fullStr | Balancing mitigation strategies for viral outbreaks |
| title_full_unstemmed | Balancing mitigation strategies for viral outbreaks |
| title_short | Balancing mitigation strategies for viral outbreaks |
| title_sort | balancing mitigation strategies for viral outbreaks |
| topic | epidemiology compartmental model transmission dynamic optimal control |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2024337 |
| work_keys_str_mv | AT hamedkarami balancingmitigationstrategiesforviraloutbreaks AT pejmansanaei balancingmitigationstrategiesforviraloutbreaks AT alexandrasmirnova balancingmitigationstrategiesforviraloutbreaks |