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基于 Legendre 谱方法的随机 Zika 病毒最优控制模型的计算研究。

Computational investigation of stochastic Zika virus optimal control model using Legendre spectral method.

机构信息

School of Mathematics, Shandong University, Jinan, 250100, China.

School of Mathematics and Data Sciences, Changji University, Changji, 831100, China.

出版信息

Sci Rep. 2024 Aug 5;14(1):18112. doi: 10.1038/s41598-024-69096-x.

DOI:10.1038/s41598-024-69096-x
PMID:39103482
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11300638/
Abstract

This study presents a computational investigation of a stochastic Zika virus along with optimal control model using the Legendre spectral collocation method (LSCM). By accumulation of stochasticity into the model through the proposed stochastic differential equations, we appropriating the random fluctuations essential in the progression and disease transmission. The stability, convergence and accuracy properties of the LSCM are conscientiously analyzed and also demonstrating its strength for solving the complex epidemiological models. Moreover, the study evaluates the various control strategies, such as treatment, prevention and treatment pesticide control, and identifies optimal combinations that the intervention costs and also minimize the proposed infection rates. The basic properties of the given model, such as the reproduction number, were determined with and without the presence of the control strategies. For , the model satisfies the disease-free equilibrium, in this case the disease die out after some time, while for , then endemic equilibrium is satisfied, in this case the disease spread in the population at higher scale. The fundamental findings acknowledge the significant impact of stochastic phonemes on the robustness and effectiveness of control strategies that accelerating the need for cost-effective and multi-faceted approaches. In last the results provide the valuable insights for public health department to enabling more impressive mitigation of Zika virus outbreaks and management in real-world scenarios.

摘要

本研究采用 Legendre 谱配置法(LSCM)对随机 Zika 病毒及其最优控制模型进行了计算研究。通过在模型中通过提出的随机微分方程积累随机性,我们适当考虑了在进展和疾病传播中必不可少的随机波动。认真分析了 LSCM 的稳定性、收敛性和准确性特性,并展示了其解决复杂传染病模型的优势。此外,该研究还评估了各种控制策略,如治疗、预防和治疗农药控制,并确定了干预成本最小化和提出的感染率最小化的最佳组合。在存在和不存在控制策略的情况下,确定了所给模型的基本特性,如繁殖数。对于 ,模型满足无病平衡点,在这种情况下,疾病会在一段时间后消失,而对于 ,则满足地方病平衡点,在这种情况下,疾病会在人群中以更高的规模传播。这些基本发现承认了随机音素对控制策略的稳健性和有效性的重大影响,这加速了对具有成本效益和多方面方法的需求。最后,结果为公共卫生部门提供了有价值的见解,使 Zika 病毒爆发和管理在现实场景中得到更有效的缓解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/682f/11300638/565926497028/41598_2024_69096_Fig10_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/682f/11300638/565926497028/41598_2024_69096_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/682f/11300638/407e952f355e/41598_2024_69096_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/682f/11300638/f3304dd9400a/41598_2024_69096_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/682f/11300638/20a11051a67c/41598_2024_69096_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/682f/11300638/13051863f283/41598_2024_69096_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/682f/11300638/939367c8bd27/41598_2024_69096_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/682f/11300638/a316ce82103b/41598_2024_69096_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/682f/11300638/49a1ffbe3ddf/41598_2024_69096_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/682f/11300638/0e055cf7228c/41598_2024_69096_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/682f/11300638/61ad0e1fcd9f/41598_2024_69096_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/682f/11300638/565926497028/41598_2024_69096_Fig10_HTML.jpg

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