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Mathematics > Optimization and Control

arXiv:2106.09528 (math)
[Submitted on 17 Jun 2021 ]

Title: Optimal control strategies to tailor antivirals for acute infectious diseases in the host

Title: 针对宿主体内急性传染病的抗病毒药物定制最优控制策略

Authors:Mara Perez, Pablo Abuin, Marcelo Actis, Antonio Ferramosca, Esteban A. Hernandez-Vargas, Alejandro H. Gonzalez
Abstract: Several mathematical models in SARS-CoV-2 have shown how target-cell model can help to understand the spread of the virus in the host and how potential candidates of antiviral treatments can help to control the virus. Concepts as equilibrium and stability show to be crucial to qualitatively determine the best alternatives to schedule drugs, according to effectivity in inhibiting the virus infection and replication rates. Important biological events such as rebounds of the infections (when antivirals are incorrectly interrupted) can also be explained by means of a dynamic study of the target-cell model. In this work, a full characterization of the dynamical behavior of the target-cell models under control actions is made and, based on this characterization, the optimal fixed-dose antiviral schedule that produces the smallest amount of dead cells (without viral load rebounds) is computed. Several simulation results - performed by considering real patient data - show the potential benefits of both, the model characterization and the control strategy.
Abstract: 几个针对SARS-CoV-2的数学模型已经展示了靶细胞模型如何有助于理解病毒在宿主体内的传播,以及抗病毒治疗的潜在候选药物如何有助于控制病毒。 平衡和稳定性等概念被证明对于定性地确定最佳药物调度方案至关重要,这取决于抑制病毒感染和复制速率的效果。 重要的生物事件,如感染的反弹(当抗病毒药物错误地中断时)也可以通过靶细胞模型的动力学研究来解释。 在这项工作中,对受控作用下靶细胞模型的动力学行为进行了全面表征,并基于此表征计算出产生最少死亡细胞(无病毒载量反弹)的最佳固定剂量抗病毒方案。 通过考虑真实患者数据进行的多个仿真结果展示了模型表征和控制策略的潜在好处。
Subjects: Optimization and Control (math.OC) ; Populations and Evolution (q-bio.PE)
Cite as: arXiv:2106.09528 [math.OC]
  (or arXiv:2106.09528v1 [math.OC] for this version)
  https://doi.org/10.48550/arXiv.2106.09528
arXiv-issued DOI via DataCite

Submission history

From: Alejandro Hernan Gonzalez [view email]
[v1] Thu, 17 Jun 2021 14:18:51 UTC (2,340 KB)
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