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模拟治疗依从性挑战对丙型肝炎病毒传播动力学的影响。

Modeling the effects of treatment adherence challenges on the transmission dynamics of hepatitis C virus.

作者信息

Mupedza Tinashe Victor, Mhlanga Laurette, Mamutse Dennis, Helikumi Mlyashimbi, Lolika Paride Oresto, Murambiwa Shingirai Tangakugara, Mhlanga Adquate

机构信息

Department of Mathematics, University of Zimbabwe, Harare, Zimbabwe.

Department of Preventive Medicine and Institute for Global Health, Northwestern University, Chicago, Illinois, United States of America.

出版信息

PLoS One. 2025 Aug 8;20(8):e0329543. doi: 10.1371/journal.pone.0329543. eCollection 2025.

DOI:10.1371/journal.pone.0329543
PMID:40779594
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12334037/
Abstract

Infectious disease modeling is crucial for predicting disease progression over time and helps guide decision makers in public health policy. Hepatitis C virus (HCV) prevalence is still increasing in Zimbabwe, a low-middle-income country (LMIC), despite the availability of effective treatments, and the reasons for this increase are not well understood. Our study employed a mathematical model to explain the impact of poor treatment adherence on HCV transmission dynamics in Zimbabwe. We computed the basic reproduction number ([Formula: see text]), a vital metric of disease spread. Equilibrium states of the model were determined, and their stability was investigated. The study demonstrated that an adherence level exceeding 52% causes the reproduction number to drop below 1, curtailing further spread. Our HCV model indicates that variations in re-susceptibility minimally impact outcomes, suggesting that re-susceptibility can often be excluded in such analyses. Our model unraveled the synergistic impact of simultaneously enhancing the recovery rate of acutely infected individuals and treatment adherence on reducing [Formula: see text]. The study underlines the pressing need for stronger health interventions, including patient education, financial assistance, and rigorous monitoring, to improve treatment adherence. These interventions are paramount in curbing HCV proliferation, particularly in LMICs like Zimbabwe, and can serve as a template for similar settings globally.

摘要

传染病建模对于预测疾病随时间的进展至关重要,并有助于指导公共卫生政策的决策者。尽管有有效的治疗方法,但在津巴布韦这个低收入和中等收入国家(LMIC),丙型肝炎病毒(HCV)的流行率仍在上升,而这种上升的原因尚不清楚。我们的研究采用了一个数学模型来解释治疗依从性差对津巴布韦HCV传播动态的影响。我们计算了基本再生数([公式:见正文]),这是疾病传播的一个关键指标。确定了模型的平衡状态,并研究了它们的稳定性。研究表明,超过52%的依从水平会使再生数降至1以下,从而遏制进一步传播。我们的HCV模型表明,重新易感性的变化对结果的影响最小,这表明在这类分析中通常可以排除重新易感性。我们的模型揭示了同时提高急性感染个体的康复率和治疗依从性对降低[公式:见正文]的协同影响。该研究强调迫切需要加强卫生干预措施,包括患者教育、财政援助和严格监测,以提高治疗依从性。这些干预措施对于遏制HCV的扩散至关重要,特别是在像津巴布韦这样的低收入和中等收入国家,并且可以作为全球类似情况的模板。

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Adherence in Hepatitis C Virus Treatment: What We Know.丙型肝炎病毒治疗中的依从性:我们所了解的情况。
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