The effects of heterogeneity in individual infectiousness on disease modeling predictions

Abstract

Individual variation in infectiousness is generated by heterogeneities in the host, the pathogen, and the environment. However, many models of disease transmission, especially those designed for wildlife and livestock populations, do not typically allow for such variation in individual infectiousness. The objective of my research is to explore the effects of heterogeneity in individual infectiousness on disease modeling predictions within and across populations. My dissertation research explores three different types of heterogeneity that can alter individual infectiousness: (i) host heterogeneity resulting from individual differences in susceptibility, infectiousness, and behavioral contact rates, (ii) contact heterogeneity that arises within a population from underlying social systems and interactions; and (iii) spatial heterogeneity that arises from variation in host density as a function of resource quality and variable individual movement rates across a landscape. An improved understanding of the factors that lead to variability in individual infectiousness and the conditions that necessitate the inclusion of such variability in future disease models will be critical to address the growing global threats of zoonoses and emerging infectious diseases.