SCH: INT: Simulation and Decision-Analysis Algorithms for Integrated Modeling of Diseases: A healthy lives for all approach

该奖项将通过研究公共卫生投资在影响一系列相关慢性疾病方面的有效性，为促进国民健康和福利做出贡献。 人们普遍认为，社会条件，包括获得基本公共服务、幼儿发展和教育、经济和粮食安全以及环境条件，是个人健康的重要决定因素。被统称为健康的社会决定因素（SDOH）的社会流行病学研究表明，旨在改善SDOH的结构性干预措施可以预防疾病，从而改善福祉。 该项目探讨了慢性病的各种风险因素之间的重要相互作用，并开发了数学模型和决策支持方法，使经济分析的成本效益组合的结构性干预措施，作为整体公共卫生战略的一部分。 重点关注病毒性高负荷慢性病的复杂性（例如，艾滋病毒、人乳头瘤病毒、乙型肝炎病毒和丙型肝炎病毒感染），该项目旨在开发和验证一种多疾病模型，该模型能够捕捉传播和疾病进展的流行病学性质，并能够更好地预测结构性干预措施的总体有效性。 研究小组将利用与疾病控制和预防中心和世界卫生组织的主要工作人员正在进行的合作，他们是这项工作的潜在利益相关者。该项目让工程和计算机科学专业的学生参与多学科研究，旨在开发创新的决策分析模型，为国家和全球公共卫生决策提供信息。研究目标是：1）开发一个综合多疾病预测建模的计算框架; 2）开发新的方法，用于参数化受社会经济和人口因素影响的人群中相互作用的传染性和非传染性疾病的自然进展; 3）开发一个动态决策分析模型，用于评估结构性干预措施。计算框架结合了基于代理的模拟和房室分析，将病毒的传播和进展。 该项目采用了系统动力学、机器学习、图论和优化等技术，以扩展当前最先进的模拟和参数化方法。 关于疾病发病率、发病率和死亡率的数据将从国家监测和调查数据库中获得，用于模型验证。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

This Smart and Connected Health (SCH) award will contribute to the advancement of the national health and welfare by studying the effectiveness of public health investments in impacting a collection of related chronic diseases. It is generally accepted that social conditions, including access to essential public services, early childhood development and education, economic and food security and environmental conditions, are important determinants of individual health. Referred to collectively as Social Determinants of Health (SDOH), research in social epidemiology has shown that structural interventions aimed at improving SDOH can prevent diseases, thereby improving well-being. This project addresses the important interactions between a variety of risk factors for chronic diseases, and develops mathematical models and decision-support methods that enable economic analysis of cost-effective combinations of structural interventions as part of an overall public health strategy. Focusing on a complex of high-burden chronic diseases with viral origins (e.g., HIV, HPV, HBV and HCV infections), this project aims to develop and validate a multi-disease model that captures the syndemic nature of transmission and disease progression and enables better prediction of the overall effectiveness of structural interventions. The research team will exploit ongoing collaborations with key staff at the Centers for Disease Control and Prevention and the World Health Organization who are potential stakeholders in this work. The project engages both engineering and computer science students in multi-disciplinary research aimed at developing innovative decision-analytic models for informing national and global public health decisions. The research objectives are to: 1) develop a computational framework for integrated multi-disease prediction modeling; 2) develop new methodologies for parameterizing the natural progression of interacting communicable and non-communicable diseases in a population influenced by socioeconomic and demographic factors; and 3) develop a dynamic decision-analytic model for evaluation of structural interventions. The computational framework combines agent-based simulation and compartmental analysis to incorporate viral transmission and progression. This project employs techniques from system dynamics, machine learning, graph theory, and optimization to extend current state-of-the-art in simulation and parameterization methods. Data on disease incidence, morbidity and mortality will be obtained from national surveillance and survey databases for model validation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.