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基本工具

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.