CAREER: Optimal Management of Chronic Diseases Caused by Infections

职业：感染引起的慢性病的最佳管理

• 批准号: 1452999
• 负责人: Turgay Ayer
• 金额: $ 50万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1452999&HistoricalAwards=false
• 关键词: CAREER; Optimal; Management; Chronic; Diseases

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.

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针对慢性传染病设计最佳的基于人群的疾病监测政策和治疗优先策略。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增长最快的原因。 如果成功，这项研究的结果可能会改善实践中的肝细胞癌监测和丙型肝炎治疗优先决策，并可以为国家层面的指南制定提供预防和治疗决策信息。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，例如慢性传染病的最佳疾病监测和治疗优先决策，无法通过传统的医学研究方法（例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、大规模的且计算上具有挑战性。因此，本研究将创建新方法或扩展现有方法，以考虑疾病流行病学、资源限制和管理策略的关键特征。该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。虽然该项目主要关注肝细胞癌和丙型肝炎病毒，但本研究的主题可以推广并应用于其他几个疾病管理问题。