North Carolina State University
Host behavior and pathogen-specific life history generate complex patterns of pathogen exposure that drive transmission heterogeneity. Our overreaching hypothesis is that variation in pathogen exposure leads to important heterogeneities in infection traits and transmission that can be exploited for control purposes. The project will integrate data generated in a natural infection model system (Escherichia coli - cattle) through experimental challenge studies, field transmission studies, and animal movement monitoring systems with mathematical models. The specific aims are to (1) Characterize sources of heterogeneity for enteric disease transmission in cattle production systems: Data will be collected on the variation in pathogen dynamics in the host-associated environments, the contact structure among hosts, between host and specific environments, and the dose dependency of infection traits. (2) Develop a modeling framework to integrate and investigate the sources of heterogeneity for enteric pathogens for disease transmission. Individual-based and meal-field models will be developed to investigate the effect of the contact structure and variation of the exposure on population transmission patterns. Models will be parameterized and validated using data collected in longitudinal field studies. (3) Assessment of the implications of exposure heterogeneity in enteric disease control. The developed models will be used to investigate and identify intervention strategies that could lead to reduction in transmission. The proposed research will advance the understanding of the transmission of enteric pathogens by developing and analyzing models that are well-grounded in the processes associated with transmission and tightly connected to collected unique high-resolution, longitudinal data on host-host and host-environment contacts, extra-host dynamics of pathogens, and pathogen transfer. Overall this project will generate a broader understanding of the interaction between sources of heterogeneity on disease transmission and how to incorporate these sources of heterogeneity in different mathematical and computational models that is relevant to investigate ecological factors influencing disease transmission and generalizable to other host-pathogen systems.