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Sensitivity analysis of chronic hepatitis C virus infection with immune response and cell proliferation
International Journal of Biomathematics ( IF 2.4 ) Pub Date : 2020-01-31 , DOI: 10.1142/s1793524520500175
Khondoker Nazmoon Nabi 1 , Chandra N. Podder 2
Affiliation  

A new mathematical model of chronic hepatitis C virus (HCV) infection incorporating humoral and cell-mediated immune responses, distinct cell proliferation rates for both uninfected and infected hepatocytes, and antiviral treatment all at once, is formulated and analyzed meticulously. Analysis of the model elucidates the existence of multiple equilibrium states. Moreover, the model has a locally asymptotically stable disease-free equilibrium (DFE) whenever the basic reproduction number is less than unity. Local sensitivity analysis (LSA) result exhibits that the most influential (negatively sensitive) parameters on the epidemic threshold are the drug efficacy of blocking virus production and the drug efficacy of removing infection. However, LSA does not accurately assess uncertainty and sensitivity in the system and may mislead us since by default this technique holds all other parameters fixed at baseline values. To overcome this pitfall, one of the most robust and efficient global sensitivity analysis (GSA) methods, which is Latin hypercube sampling-partial rank correlation coefficient technique, elucidates that the proliferation rate of infected hepatocytes and the drug efficacy of killing infected hepatocytes are highly sensitive parameters that affect the transmission dynamics of HCV in any population. Our study suggests that cell proliferation of the infected hepatocytes can be very crucial in controlling disease outbreak. Thus, a future HCV drug that boosts cell-mediated immune response is expected to be quite effective in controlling disease outbreak.

中文翻译:

慢性丙型肝炎病毒感染与免疫反应和细胞增殖的敏感性分析

一种新的慢性丙型肝炎病毒 (HCV) 感染数学模型结合了体液和细胞介导的免疫反应、未感染和感染肝细胞的不同细胞增殖率以及同时进行的抗病毒治疗,并进行了细致的分析。对模型的分析阐明了多个平衡状态的存在。此外,只要基本再生数小于 1,该模型就具有局部渐近稳定的无病平衡 (DFE)。局部敏感性分析(LSA)结果表明,对流行阈值影响最大(负敏感)的参数是阻断病毒产生的药效和清除感染的药效。然而,LSA 不能准确评估系统中的不确定性和灵敏度,并且可能会误导我们,因为默认情况下,该技术将所有其他参数固定在基线值。为了克服这一缺陷,最稳健和最有效的全局敏感性分析(GSA)方法之一,即拉丁超立方抽样-偏秩相关系数技术,阐明了受感染肝细胞的增殖率和杀死受感染肝细胞的药物功效高度影响任何人群中 HCV 传播动态的敏感参数。我们的研究表明,受感染肝细胞的细胞增殖对于控制疾病爆发非常重要。因此,未来一种增强细胞介导的免疫反应的 HCV 药物有望在控制疾病爆发方面非常有效。
更新日期:2020-01-31
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