Data Science

Modeling the impact of host heterogeneity on the risk and dynamics of a West Nile virus epidemic

Paul Klockenkemper, a Computational and Data Science doctoral student at MTSU, presents an update of his research on modeling host heterogeneity in the West Nile virus

West Nile Virus (WNV) is a mosquito-borne arbovirus with significant ecological and public health implications. Its transmission cycle involves avian hosts and mosquito vectors. Many factors, including host diversity and mosquito population dynamics, are known to shape epidemic patterns. In this study, we extend previous work by incorporating multiple host types, horizontal transmission among hosts, and mosquito feeding preference into a WNV model. Using a system of ordinary differential equations, we analyze the impact of variable host competence, host abundance, host community structure, and mosquito biting preference on epidemic metrics. We derive an expression for the basic reproduction number in multi-host systems and qualify the impact of low-competence hosts on its value. Numerical simulations explore the impact of structural variations in the model on epidemic metrics and assess the potential for lower-competence hosts and mosquito feeding preference to dilute the epidemic. Our findings elucidate the influence of host heterogeneity, horizontal transmission, and mosquito biting preference on the severity and dynamics of a WNV epidemic.

Watch the webinar here.