CAREER: Optimal Management of Chronic Diseases Caused by Infections

该学院早期职业发展（CAREER）计划奖的研究目标是研究在资源有限的环境中设计最佳的基于人群的疾病监测政策和慢性传染病治疗优先策略。该项目特别关注丙型肝炎病毒（HCV）的最佳控制，这是一种影响近2%美国人口的流行病，但也适用于许多其他慢性病。未经治疗的HCV感染是肝硬化和肝细胞癌的主要原因，肝细胞癌是美国癌症相关死亡增长最快的原因。 如果成功，这项研究的结果可能会改善肝细胞癌的监测和HCV治疗的优先级决定在实践中，并可以告知预防和治疗决策在国家一级的指导方针的发展。在该项目下，与几位医学研究人员合作，一名博士生将接受严格的培训，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广泛传播到工程界和医学界。许多卫生政策问题，如最佳疾病监测和慢性传染病治疗优先顺序的决定，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，计算上具有挑战性。因此，这项研究将创建新的方法或扩展现有的方法，以考虑疾病流行病学，资源限制和管理策略的关键特征。研究的一个重要组成部分是使用全国一些最大的数据集对模型进行仔细的参数化和校准。虽然这个项目主要集中在肝细胞癌和丙型肝炎病毒，这项研究的主题可以概括和应用到其他几个疾病的管理问题。

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.