CAREER: Advancing Disease Modeling for Decision Making in Healthcare

这项学院早期职业发展计划(Career)补助金将通过整合个人和人口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进步。该项目旨在建立统一的疾病建模理论，以纳入可能影响疾病传播、进展和恢复的流行趋势的人类行为变量。这项研究将为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提供见解。与临床医生和卫生政策制定者的伙伴关系将确保这些模型解决政策问题，并促进跨学科合作和数据共享。该项目的教育目标是在临床合作者和学生中推进STEM教育，包括来自代表性不足和少数族裔社区的学生。本研究研究状态空间聚合方法，以统一个体级别的模拟和用于疾病建模的分区模型。其目标是在单一和重复的决策背景下优化这些模型，并将这些方法整合到成本效益分析中。这项工作的动机是需要包含行为和群体异质性的易处理疾病模型，这需要数据驱动的方法将个人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射到较低维度的空间，并在建模中弥合个人和群体级别表示之间的差距，将使这些方法能够更好地为政策提供信息。具体地说，将研究这些模型的结构属性、平衡点和过渡动力学，目的是利用它们来简化疾病动力学的最优化和马尔可夫决策过程框架。然后，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，以提供对可行政策空间的更有效和更彻底的评估。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.