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

数学模型是描述、预测和制定干预措施的基本工具 医疗保健相关感染(HAI)。然而，当前的HAI建模工作缺乏包括各种 异质性和不确定性的来源(例如，个体、空间和时间的异质性)，以及 不要捕捉从病原体基因组学到内部和跨领域的相互作用的跨尺度现象 医疗设施。通过这个项目，我们的目标是开发、传播和指导新的跨尺度 建模套件包括各种建模技术。建模套件有效地结合了 来自多个数据源的各种异构性，集成了主机内部和主机之间的动态 整合到一个统一的建模框架中，了解和跟踪跨尺度HAI传播路径，以及 在资源受限的情况下，设计医疗机构的优化干预策略。长的- 这种跨学科研究-教育整合的学期目标是显著增加我们的 建模能力，以应对当今与HAI相关的复杂挑战和不确定性。我们会 处理以下三个疾控中心主要研究主题领域：1)流行病学模拟 研究；2)患者在医疗机构内部和之间的连接性；以及3)系统方法。 此外，抗菌素耐药性(AR)和卫生经济学也将包括在内。这项工作将是 在我们与长期合作伙伴Lanzas博士(北卡罗来纳州)合作的基础上发展并进一步促进我们的协同工作 州立大学)基于CDC Mind项目的HAI和AR的多尺度建模。我们建议 以下三个研究项目为博士后研究员呼应研究主题：1)系统化 回顾和描述医疗保健内部和跨医疗保健领域的异质性和不确定性的各种来源 设施；2)HAI和AR的主机内和主机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。我们会 制定详细的计划，挑选来自不同背景的博士前研究员，以增加 工作人员与来自数学建模、临床科学、传染病等领域的专家团队合作 流行病学和卫生经济学，并确保他们在指导研究项目中取得成功。 这三个项目的完成将为我们的下一代模型师做好充分的准备和改造 随着对HAI和AR挑战的更深入了解，广泛的各种知识 在复杂的HAI系统中的异构性和不确定性，并提供了广泛的创新 以及有效的数学建模技术，以增加HAI和健康研究的能力。 所开发的建模框架将作为开放源码、开放获取工具、 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.