CAREER: Advancing Disease Modeling for Decision Making in Healthcare

该学院早期职业发展计划（CAREER）拨款将通过整合个人层面和人群层面的传染病模型来支持数据驱动的医疗决策，从而促进国家健康和福利的发展。该项目旨在建立一个统一的疾病建模理论，以纳入人类行为的变化，这些变化可能影响疾病传播，进展和恢复的流行趋势。该研究将深入了解如何使用这些模型来提高公共卫生服务的质量和成本效益。与临床医生和卫生政策制定者的伙伴关系将确保模型解决政策问题，并促进跨学科合作和数据共享。该项目的教育目标旨在推进临床合作者和学生的STEM教育，包括那些来自代表性不足和少数民族社区的学生。本研究状态空间聚合方法，以统一个人水平的模拟与疾病建模的房室模型。 我们的目标是优化这些模型在单一和重复的决策背景下，并整合这些方法的成本效益分析。这项工作的动机是需要易于处理的疾病模型，其中包括行为和群体异质性，这需要数据驱动的方法将个体聚集到可解释的群体中，同时尊重潜在疾病过程的动态。了解如何将模拟结果投影到低维空间，并弥合建模中个人和群体级别表示之间的差距，将允许这些方法更好地为政策提供信息。具体而言，这些模型的结构特性，平衡和过渡动力学将进行研究，其目标是使用它们来简化疾病动力学的优化和马尔可夫决策过程框架。这一领域的创新可以被整合到成本效益和概率敏感性分析中，从而对可行的政策空间进行更有效和全面的评估。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 Faculty Early Career Development Program (CAREER) grant will contribute to the advancement of national health and welfare by integrating individual-level and population-level models of infectious diseases to support data-driven healthcare decision making. This project aims to build a unifying theory of disease modeling to incorporate variation in human behavior that can affect epidemic trends in disease transmission, progression, and recovery. The research will provide insights into how these models can be used to improve both quality and cost-effectiveness of public health services. Partnership with clinicians and health policy makers will ensure that the models address policy concerns and advance interdisciplinary collaboration and data sharing. The educational goals of the project aim to advance STEM education among clinical collaborators and students, including those from underrepresented and minority communities.This research studies state space aggregation methods to unify individual-level simulations with compartmental models for disease modeling. The objectives are to optimize these models in single and repeated decision contexts, and to integrate these methods in cost-effectiveness analysis. This work is motivated by the need for tractable disease models that incorporate behavior and population heterogeneity, which require data-driven methods to aggregate individuals into interpretable groups while respecting the dynamics of the underlying disease process. Understanding how simulation outcomes can be projected to a lower-dimensional space and bridging the gap between individual and group-level representation in modeling will allow for these methods to better inform policy. Specifically, structural properties, equilibria, and transition dynamics of these models will be studied with the goal of using them to simplify optimization and Markov decision process frameworks on disease dynamics. Innovations in this area can then be integrated into cost-effectiveness and probabilistic sensitivity analysis to provide more efficient and thorough evaluation of the feasible policy space.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.