RFA-CK-22-008, Building Next-generation Mathematical Biology Modeling Workforce for HAI Control

数学模型是表征、预测和制定干预措施的重要工具， 医疗相关感染（HAI）。然而，目前的HAI建模工作缺乏对各种 异质性和不确定性的来源（例如，g，个体、空间和时间异质性），以及 不能捕捉到从病原体基因组学到内部和之间相互作用的跨尺度现象 医疗保健设施。通过这个项目，我们的目标是开发，传播和指导新的跨尺度 包括各种建模技术的建模套件。建模套件有效地结合了 来自多个数据源的各种异质性，整合主机内和主机间的动态 整合到一个统一的建模框架中，了解和跟踪跨尺度HAI传播途径， 在资源有限的情况下，为保健设施设计优化的干预战略。很长的- 这种跨学科研究教育整合的长期目标是显着提高我们的 建模能力，以应对当今与HAI相关的复杂挑战和不确定性。我们将 解决以下三个CDC主要研究主题领域：1）流行病学模拟 研究; 2）医疗机构内部和之间的患者连通性; 3）系统方法。 此外，还将包括抗菌素耐药性（AR）和卫生经济学。这项工作将 开发并进一步促进我们与我们的长期合作伙伴Lanzas博士（NC）的协同工作 州立大学）基于HAI和AR的多尺度建模的CDC MInD项目。我们建议 以下三个研究项目为博士前研究员呼应的研究主题：1）系统 审查和描述医疗保健内部和整个医疗保健之间的异质性和不确定性的各种来源 设施; 2）HAI和AR的宿主内和宿主间动态的跨尺度建模;以及3） 集成多个数据源，用于医疗保健研究中的决策支持和优化。我们将 创建一个详细的计划，选择来自不同背景的博士前研究员，以增加多样性， 工作人员，与数学建模，临床科学，传染病专家团队合作 流行病学和卫生经济学，并确保他们在指导的研究项目中取得成功。 这三个项目的完成将充分准备和改造我们的下一代建模人员 随着对HAI和AR挑战的深入了解，对各种 复杂HAI系统中的异质性和不确定性，并提供广泛的创新 和有效的数学建模技术，以提高人力资源和健康研究的能力。 开发的建模框架将作为开放源码、开放获取工具有效传播， 并与更广泛的临床和公共卫生科学研究人员，医院临床医生和 流行病学家和公共卫生决策者。

Mathematical models are essential tools to characterize, predict, and develop interventions of healthcare associated infections (HAIs). However, current HAI modeling efforts lack inclusion of various sources of heterogeneity and uncertainty (e..g, individual, spatial, and temporal heterogeneities), and do not capture cross-scale phenomena from pathogen genomics to interactions within and across healthcare facilities. Through this project, we aim to develop, disseminate, and instruct novel cross-scale modeling suites including various modeling techniques. The modeling suites effectively incorporate various heterogeneities from multiple data sources, integrate both within- and between-host dynamics into one unified modeling framework, understand and track cross-scale HAI transmission pathways, and design optimized intervention strategies for healthcare facilities under resource constraints. The long- term goal of this transdisciplinary research-education integration is to significantly increase our modeling capacity to address today’s complex challenges and uncertainties associated with HAI. We will address the following three CDC major research thematic areas: 1) simulation of epidemiological studies; 2) connectedness of patients within and among healthcare facilities; and 3) system approaches. In addition, antimicrobial resistance (AR) and health economics will also be included. This work will be developed on and further foster our synergistic work with our long-term collaborator Dr. Lanzas (NC State University) based on the CDC MInD project of multi-scale modeling of HAI and AR. We propose the following three research projects for predoctoral fellows to echo the research themes: 1) systematic review and characterize various sources of heterogeneity and uncertainty within and across healthcare facilities; 2) cross-scale modeling of within- and between-host dynamics of HAI and AR; and 3) integrating multiple data sources for decision support and optimization in healthcare research. We will create a detailed plan to select predoctoral fellows from various backgrounds to increase diversity in the workforce, work with a team of experts from mathematical modeling, clinical science, infectious disease epidemiology, and health economics, and ensure their success in the mentored research projects. Completion of these three projects will adequately prepare and transform our next-generation modelers with a deeper understanding of HAI and AR challenges, extensive knowledge about various heterogeneities and uncertainties in the complex HAI system, and provide a wide range of innovative and effective mathematical modeling techniques to increase the capacity of HAI and health research. The developed modeling frameworks will be effectively disseminated as open source, open access tools, and shared with broader clinical and public health science researchers, hospital clinicians and epidemiologists, and public health decision makers.