HCV transmission model with protection awareness in an SEACTR community

IF 8.8 3区 医学 Q1 Medicine
Liangwei Wang , Fengying Wei , Zhen Jin , Xuerong Mao , Shaojian Cai , Guangmin Chen , Kuicheng Zheng , Jianfeng Xie
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Abstract

Background

Hepatitis C virus (HCV) is a bloodborne virus that causes both acute and chronic hepatitis with the severity from a mild illness to liver cirrhosis and cancer. As one of the major infectious diseases in China, the monthly surveillance data from the Fujian Provincial Center for Disease Control and Prevention shows the increasing tendency from 2004 to 2011, the stable tendency from 2012 to 2016, and the declining tendency from 2017 to 2022. The 2004–2022 HCV infection tendency of Fujian Province is affected by nation-wide main control measures of Chinese government, because no control measures for HCV are modified from 2020 to 2022 during the prevalence of COVID-19 in Fujian Province.

Methods

The SEACTR (the susceptible, the exposed, the acutely infected, the chronically infected, the treated, the recovered) models with protection awareness are proposed. The next generation matrix method is used to compute basic reproduction number of toy model and dynamic analysis method is used to produce stochastic reproduction number of modified model. The least squares method and toy model are used to perform the optimal fitting against the monthly surveillance data. The positive preserving truncated Euler-Maruyama method is applied in modified model for the positivity of numerical simulations.

Results

The optimal fitting is performed using the monthly surveillance data provided by the Fujian Provincial Center for Disease Control and Prevention from 2004 to 2022. The sensitivities of protection efficiency and conversion rate to basic reproduction number and stochastic reproduction number are analyzed. The reproduction numbers and HCV infection scale with measures (single-measure, double-measure, triple-measure, and none-measure) are compared using toy model and modified model. The impacts of protection efficiency and conversion rate on exposed population, acutely infected population, chronically infected population, and treated population are analyzed. The tendency predictions for infected population and treated population in Fujian Province from 2023 to 2035 are conducted.

Conclusions

The HCV infection scale mainly depends on both protection efficiency and conversion rate, in which protection efficiency is the most important contributor. The reproduction numbers show the declining tendencies by phases, which indicate that the prevention and control of HCV in Fujian Province has achieved a remarkable achievement. The 2023–2035 tendency predictions of HCV infection scale in Fujian Province grow slowly due to approximately 19–109 monthly infections. The overall HCV growth tendency of Fujian Province is consistent with the nation-wide elimination objective.
SEACTR社区中具有保护意识的HCV传播模型
丙型肝炎病毒(HCV)是一种血源性病毒,可引起急性和慢性肝炎,严重程度从轻微到肝硬化和癌症。作为中国主要传染病之一,福建省疾病预防控制中心月度监测数据显示,2004 - 2011年呈上升趋势,2012 - 2016年呈稳定趋势,2017 - 2022年呈下降趋势。福建省2004-2022年HCV感染趋势受中国政府在全国范围内的主要控制措施的影响,因为福建省在2020 - 2022年COVID-19流行期间没有修改HCV控制措施。方法提出具有保护意识的SEACTR(易感者、暴露者、急性感染者、慢性感染者、治疗者、康复者)模型。采用下一代矩阵法计算玩具模型的基本再现数,采用动态分析法生成修正模型的随机再现数。采用最小二乘法和玩具模型对月度监测数据进行最优拟合。针对数值模拟的正性,在修正模型中应用了保正截断欧拉-丸山法。结果利用福建省疾病预防控制中心2004 - 2022年每月监测数据进行了最优拟合。分析了保护效率和转换率对基本繁殖数和随机繁殖数的敏感性。采用玩具模型和修正模型比较不同测量方法(单测量、双测量、三测量和无测量)的繁殖数和HCV感染量表。分析了防护效率和转化率对暴露人群、急性感染人群、慢性感染人群和治疗人群的影响。对福建省2023 ~ 2035年感染人群和治疗人群趋势进行预测。结论HCV感染规模主要取决于防护效率和转化率,其中防护效率是最重要的因素。繁殖数呈阶段性下降趋势,说明福建省HCV防控工作取得了显著成效。福建省2023-2035年HCV感染规模趋势预测增长缓慢,每月感染数约为19-109例。福建省HCV总体增长趋势与全国消除目标一致。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Infectious Disease Modelling
Infectious Disease Modelling Mathematics-Applied Mathematics
CiteScore
17.00
自引率
3.40%
发文量
73
审稿时长
17 weeks
期刊介绍: Infectious Disease Modelling is an open access journal that undergoes peer-review. Its main objective is to facilitate research that combines mathematical modelling, retrieval and analysis of infection disease data, and public health decision support. The journal actively encourages original research that improves this interface, as well as review articles that highlight innovative methodologies relevant to data collection, informatics, and policy making in the field of public health.
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