Kadri Abdeldjalil, Boudaoui Ahmed, Ullah Saif, Asiri Mohammed, Saqib Abdul Baseer, Riaz Muhammad Bilal
Laboratory of Mathematics Modeling and Applications, University of Adrar, Adrar, Algeria.
Department of Mathematics, University of Peshawar, Peshawar, KP, 25000, Pakistan.
Sci Rep. 2025 Apr 5;15(1):11710. doi: 10.1038/s41598-025-96127-y.
This paper presents a comparative analysis of deterministic and stochastic computational modeling approaches for the optimal control of COVID-19. We formulate a compartmental epidemic model with perturbation by white noise that incorporates various factors influencing disease transmission. By incorporating stochastic effects, the model accounts for uncertainties inherent in real-world epidemic data. We establish the mathematical properties of the model, such as well-posedness and the existence of stationary distributions, which are crucial for understanding long-term epidemic dynamics. Moreover, the study presents an optimal control strategies to mitigate the epidemic's impact, both in deterministic and stochastic sceneries. Reported data from Algeria are used to parameterize the model, ensuring its relevance and applicability to practical satiation. Through numerical simulations, the study provides insights into the effectiveness of different control measures in managing COVID-19 outbreaks. This research contributes to advancing our understanding of epidemic dynamics and informs decision-making processes for epidemic controlling interventions.
本文对用于新冠肺炎疫情最优控制的确定性和随机计算建模方法进行了比较分析。我们构建了一个受白噪声扰动的分区流行模型，该模型纳入了影响疾病传播的各种因素。通过纳入随机效应，该模型考虑了现实世界疫情数据中固有的不确定性。我们建立了模型的数学性质，如适定性和稳态分布的存在性，这对于理解长期疫情动态至关重要。此外，该研究还提出了在确定性和随机情况下减轻疫情影响的最优控制策略。来自阿尔及利亚的报告数据用于对模型进行参数化，确保其与实际情况相关且适用。通过数值模拟，该研究深入了解了不同控制措施在管理新冠肺炎疫情爆发中的有效性。这项研究有助于增进我们对疫情动态的理解，并为疫情控制干预的决策过程提供参考。
