SCH: INT: Collaborative Research: Smart Intervention Strategies for Hepatitis C Elimination

慢性丙型肝炎病毒(丙型肝炎病毒)感染是一个全球性的健康问题，导致的死亡人数超过其他常见传染病，包括艾滋病毒/艾滋病、结核病和疟疾。由于感染潜伏期长，几乎没有症状，大多数感染者仍然不知道自己的健康状况。世界卫生组织(WHO)和美国国家医学研究院最近制定了到2030年消灭丙型肝炎病毒的雄心勃勃的目标；然而，仍然存在一些挑战。疾病的真实流行情况尚不清楚，也不存在有效的筛查政策，治疗虽然有效，但非常昂贵。该项目有以下三个主要目标：1)估计国家和国家一级丙型肝炎的真实疾病负担；2)确定有效的筛查和治疗战略，以确定丙型肝炎感染；以及3)设计实用的决策支持工具，供利益攸关方用来管理疾病。从社会的角度来看，这项研究将有助于决策者减轻丙型肝炎的疾病负担。该小组与疾病控制和预防中心(CDC)、州和县卫生部门以及世界卫生组织(WHO)进行了接触，他们是这项研究的潜在利益相关者。该项目还将为学生和研究员提供学习和研究经验，将系统动态建模应用于医疗保健。本研究将应用流行病学和运筹学的方法，为分配有限的资源以有效干预政策以遏制持续的丙型肝炎病毒流行提供决策支持。研究团队将通过以下方式解决与丙型肝炎管理和控制相关的几个重要研究问题：1)开展调查以估计高危人群中丙型肝炎的流行率；2)通过验证的基于试剂的模拟模型估计州和国家层面的真实丙型肝炎流行率；3)研究动态分配适应性干预策略的基于优化的方法。这项研究有望为实际决策提供支持。这项研究将促进传染病的数学和计算建模方面的知识，并指导分配有限的资源，以消除丙型肝炎病毒这一全球健康威胁。

Chronic hepatitis C virus (HCV) infection is a global health problem that causes more deaths than other common infectious conditions, including HIV/AIDS, tuberculosis and malaria. Because the infection has a long latency period with few symptons, the majority of those infected remain unaware of their health status. The World Health Organization (WHO) and the National Academy of Medicine recently set an ambitious target to eliminate HCV by 2030; however, several challenges exist. The true prevalence of disease is unknown and effective screening policies do not exist, and treatment, while effective, is very expensive. This project has the following three overarching aims: 1) estimating the true disease burden of hepatitis C at the State and National level; 2) identifying effective screening and treatment strategies for identifying hepatitis C infection; and 3) designing practical decision support tools for use by stakeholders to manage the disease. From a societal perspective, the research will assist decision makers in reducing HCV disease burden. The team is engaged with the Centers for Disease Control and Prevention (CDC), State and County health departments, and the World Health Organization (WHO), who are the potential stakeholders of the research. This project will also provide learning and research experience to students and fellows on the application of systems dynamic modeling to healthcare.This research will apply methodologies from epidemiology and operations research to provide decision support for the allocation of limited resources to effective intervention policies to contain the ongoing epidemic of HCV. The research team will address several important research questions related to hepatitis C management and control by 1) conducting surveys to estimate HCV prevalence in at-risk populations; 2) estimating the true HCV prevalence at the State and National level via a validated agent-based simulation model; 3) researching optimization-based approaches to dynamic allocation of adaptive intervention strategies. The research is expected to lead to practical decision support. The research will advance knowledge in mathematical and computational modeling of infectious diseases and guide the allocation of limited resources towards eliminating HCV as a global health threat.