Karami Hamed, Sanaei Pejman, Smirnova Alexandra
Department of Mathematics & Statistics, Georgia State University, Atlanta, USA.
Math Biosci Eng. 2024 Dec 4;21(12):7650-7687. doi: 10.3934/mbe.2024337.
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.
控制和预防策略是应对传染病传播的不可或缺的工具。本文研究了病毒爆发后疫苗接种阶段的生物学模型,该模型整合了两种重要的缓解工具:旨在降低疾病传播率的社交距离措施,以及增强免疫系统的疫苗接种。考虑了五种不同的疫情发展情景:(ⅰ)“无控制”情景,反映了在没有任何安全措施的情况下疾病的自然演变;(ⅱ)“重建”情景,代表现实世界的数据和干预措施;(ⅲ)涵盖广泛行为变化的“社交距离控制”情景;(ⅳ)展示疫苗接种对疫情传播影响的“疫苗控制”情景;(ⅴ)同时纳入社交距离和疫苗控制的“两种控制同时进行”情景。通过比较这些情景,我们对各种干预策略进行了全面分析,为疾病动态提供了有价值的见解。我们对控制成本建模的创新方法产生了一种强大的计算算法,用于解决与不同公共卫生法规相关的最优控制问题。数值结果得到了美国新冠疫情德尔塔变种的真实数据的支持。