Charlotte Public Health Sciences Team Receives CDC Grant to Develop Next-Generation Mathematical Models of Infectious Diseases
A UNC Charlotte team, led by Dr. Shi Chen, has received nearly $750,000 in a highly competitive three-year U01 (cooperative agreement) grant from the US Centers for Disease Control and Prevention (CDC) to develop novel mathematical models of infectious diseases and train the next-generation healthcare research workforce.
Researchers from the Department of Public Health Sciences and the Center of Computational Intelligence to Predict Health and Environmental Risks (CIPHER) will develop a suite of mathematical and computational models to address current issues in infectious disease modeling, especially for healthcare-associated infections and antimicrobial resistance.
“Mathematical models have been the vital key to understand, characterize, and mitigate infectious diseases especially within and across healthcare facilities. Our synergistic team of mathematical modeler, biostatistician, epidemiologist, and clinician will bring in complementary expertises in disease transmission characterization, infection and antimicrobial resistance surveillance, early warning of potential outbreaks, healthcare facility operation optimization, and health economics modeling” said Principal Investigator Dr. Shi Chen, an Associate Professor of Public Health Sciences whose focus is on health informatics and analytics. The research team also includes co-investigators Drs. Michael Dulin, Rajib Paul, Monika Sawhney, Daniel Janies, and Cristina Lanzas. Dr. Dulin is a Professor of Public Health Sciences and Director of the Academy for Population Health Innovation (APHI), a partnership with Mecklenburg County Public Health. Drs. Paul and Sawhney are Associate Professors of Public Health Sciences. Dr. Janies, CIPHER Director, is a professor in the Department of Bioinformatics and Genomics. Dr. Lanzas is a Professor and Associate Chair of Pathobiology and Population Health at NC State University.
The UNC Charlotte team will closely collaborate with the CDC to develop multiple modeling tools, including:
A quantitative characterization of various sources of heterogeneity in infectious disease transmission, including individual, spatial, and temporal heterogeneities, based on comprehensive, systematic literature reviews.
A novel, universally designed cross-scale modeling framework to model antimicrobial resistance and infectious disease transmission within and across healthcare facilities.
A hybrid of machine learning and mechanistic methods to optimize healthcare facility operation during health emergencies.
Throughout the project, the team will also provide immersive mentored research experience for three predoctoral fellows. The fellows will work closely with the team, as well as external collaborators from major healthcare facilities, local, state, and federal public health agencies (including the CDC) to develop novel infectious disease modeling methodology and improve healthcare practice in broader settings.