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

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.