Building mathematical modeling workforce capacity to support infectious disease and healthcare research

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Project Summary In this project, we will train predoctoral students to design and conduct infectious disease modeling analyses. The specific aims are: (1) To develop a transparent sequential learning algorithm for spatio-temporal disease surveillance and early detection of disease clusters. This machine-learning-based surveillance algorithm recursively updates its learned objectives using up-to-date data in a real-time fashion, while accommodating seasonality, latent spatio-temporal correlation, and other complex data structure. It does not impose any parametric forms on the data distribution, spatio-temporal data variation, and spatio-temporal data correlation. (2) To develop a competing risks modeling framework for transmission dynamics of antimicrobial-resistant and antimicrobial-susceptible pathogens at the individual level in healthcare centers and at the population level in communities. This framework couples individual exposure data in healthcare centers with aggregated data in communities at large to assess transmissibility, susceptibility and health disparity determinants, and the relative contributions of healthcare-associated and community-associated infections, while accounting for environmental contamination and superspreaders. (3) To develop an agent-based model to assess 1) effectiveness of strategies combining early detection, antimicrobial intervention and patient management on containing both antimicrobial- sensitive and antimicrobial-resistant pathogens; and 2) optimal control strategies for vaccine- preventable infectious diseases. This agent-based model will be developed under the MInD- Healthcare Framework to increase its reproducibility and generalizability. We will systematically evaluate effectiveness of control strategies determined by surveillance, antimicrobial treatment and patient management under several transmission settings and mutation parameters. This project will produce novel statistical methods for surveillance, inference and agent-based modeling. Findings may potentially influence surveillance practice and intervention policies for emerging and endemic pathogens and their drug-resistant mutants. This project will fully prepare trainees for independent and collaborative research in both methodology and practice related to healthcare-associated infections and disease transmission in broader settings.