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

慢性丙型肝炎病毒（HCV）感染是一个全球性健康问题，造成的死亡人数超过艾滋病毒/艾滋病、结核病和疟疾等其他常见传染病。由于感染潜伏期长，症状少，大多数感染者不知道自己的健康状况。世界卫生组织（世卫组织）和美国国家医学院最近制定了到2030年消除丙型肝炎病毒的宏伟目标；然而，存在一些挑战。疾病的真正流行情况尚不清楚，没有有效的筛查政策，治疗虽然有效，但非常昂贵。该项目有以下三个总体目标：1)在州和国家层面估计丙型肝炎的真实疾病负担；2)确定有效的筛查和治疗策略，以识别丙型肝炎感染；3)设计实用的决策支持工具，供利益相关者使用以管理疾病。从社会的角度来看，这项研究将有助于决策者减轻HCV疾病负担。该团队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）合作，他们是这项研究的潜在利益相关者。该项目也将为学生和研究员提供系统动态建模在医疗保健中的应用的学习和研究经验。本研究将应用流行病学和运筹学的方法，为有限资源的分配提供决策支持，以制定有效的干预政策，遏制丙型肝炎病毒的持续流行。研究小组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，估计高危人群中丙型肝炎的流行情况；2)通过验证的基于agent的模拟模型估算州和国家层面的HCV真实患病率；3)研究基于优化的自适应干预策略动态分配方法。该研究有望为实际决策提供支持。这项研究将促进传染病数学和计算建模方面的知识，并指导有限资源的分配，以消除作为全球健康威胁的丙型肝炎病毒。

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.