Department of Computer Science, Applied College, Taibah University, Medina, 42353, Kingdom of Saudi Arabia.
Department of Mathematics, Faculty of Science, University of Maragheh, Maragheh, 83111-55181, Iran.
Sci Rep. 2024 Apr 11;14(1):8466. doi: 10.1038/s41598-024-57356-9.
This paper presents a novel approach to analyzing the dynamics of COVID-19 using nonstandard finite difference (NSFD) schemes. Our model incorporates both asymptomatic and symptomatic infected individuals, allowing for a more comprehensive understanding of the epidemic's spread. We introduce an unconditionally stable NSFD system that eliminates the need for traditional Runge-Kutta methods, ensuring dynamical consistency and numerical accuracy. Through rigorous numerical analysis, we evaluate the performance of different NSFD strategies and validate our analytical findings. Our work demonstrates the benefits of using NSFD schemes for modeling infectious diseases, offering advantages in terms of stability and efficiency. We further illustrate the dynamic behavior of COVID-19 under various conditions using numerical simulations. The results from these simulations demonstrate the effectiveness of the proposed approach in capturing the epidemic's complex dynamics.
本文提出了一种使用非标准有限差分 (NSFD) 方法分析 COVID-19 动力学的新方法。我们的模型同时包含了无症状和有症状的感染者，从而可以更全面地了解疫情的传播。我们引入了一个无条件稳定的 NSFD 系统，该系统消除了对传统龙格-库塔方法的需求，确保了动力一致性和数值准确性。通过严格的数值分析，我们评估了不同 NSFD 策略的性能，并验证了我们的分析结果。我们的工作表明，使用 NSFD 方法来模拟传染病具有优势，在稳定性和效率方面具有优势。我们进一步使用数值模拟说明了在各种条件下 COVID-19 的动态行为。这些模拟结果表明，所提出的方法在捕捉疫情的复杂动态方面是有效的。
