CAREER: Optimal Management of Chronic Diseases Caused by Infections

该学院早期职业发展（Career）项目奖的研究目标是研究在资源有限的情况下，以人群为基础的最佳疾病监测政策和慢性传染病治疗优先策略的设计。该项目特别侧重于丙型肝炎病毒（HCV）的最佳控制，这种流行病影响了美国近2%的人口，但也适用于许多其他慢性疾病。未经治疗的丙型肝炎病毒感染是肝硬化和肝细胞癌的主要原因，而肝硬化和肝细胞癌是美国癌症相关死亡人数增长最快的原因。如果成功，该研究的结果可能在实践中改善肝细胞癌监测和HCV治疗优先级决策，并可以在指南制定方面为国家一级的预防和治疗决策提供信息。在这个项目中，一名博士生将与几位医学研究人员合作，接受严格的培训，将系统建模方法应用于医疗保健领域。此外，一些高中生和本科生将参与这些研究活动。该项目的成果将通过论文出版物和会议报告向工程界和医学界广泛传播。许多卫生政策问题，如慢性传染病的最佳疾病监测和治疗优先级决定，无法通过传统的医学研究方法，如随机对照试验和观察性研究来回答。这项研究将使用数据驱动的数学模型来支持一些此类复杂的卫生政策决策。由此产生的随机优化模型是非标准的、大规模的、具有计算挑战性的。因此，这项研究将创造新的方法或扩展现有的方法，以解释疾病流行病学的关键特征，资源限制和管理策略。该研究的一个重要组成部分是使用国内一些最大的数据集对模型进行仔细的参数化和校准。虽然本项目主要关注肝细胞癌和HCV，但本研究的主题可以推广并应用于其他一些疾病管理问题。

The research objective of this Faculty Early Career Development (CAREER) Program award is to study the design of optimal population-based disease surveillance policies and treatment prioritization strategies for chronic infectious diseases in resource-limited settings. The project particularly focuses on the optimal control of hepatitis C Virus (HCV), an epidemic affecting nearly 2 percent of the entire US population, but also applies to many other chronic diseases. Untreated HCV infection is the leading cause of cirrhosis and hepatocellular carcinoma, the fastest-growing cause of cancer-related deaths in the US. If successful, results from this research may improve hepatocellular carcinoma surveillance and HCV treatment prioritization decisions in practice and can inform prevention and treatment decisions at the national level in terms of guideline development. Under this project, in collaboration with several medical researchers, a PhD student will be rigorously trained to apply systems modeling methodologies to the healthcare area. In addition, several high school and undergraduate students will be engaged into these research activities. The results of the project will be widely disseminated to both engineering and medical communities through paper publications and conference presentations.Many health policy questions, such as optimal disease surveillance and treatment prioritization decisions for chronic infectious diseases, cannot be answered through the traditional research methods in medicine, such as randomized control trials and observational studies. This study will use data-driven mathematical models to underpin some of such complex health policy decisions. The resulting stochastic optimization models are non-standard, large scale, and computationally challenging. Therefore, this study will either create new approaches or extending the existing methodology to account for the critical features of the disease epidemiology, resource limitations, and management strategies. An important component of the research is careful parameterization and calibration of the models using some of the largest datasets in the nation. While this project mainly focuses on hepatocellular carcinoma and HCV, the themes of this study can be generalized and applied to several other disease management problems.