Abstract

West Nile virus (WNV) is prevalent across the United States, but its transmission patterns and spatio-temporal intensity vary significantly, particularly in the Eastern United States. For instance, Chicago has long been a hotspot for WNV cases due to its high cumulative incidence of infection, with the number of cases varying considerably from year to year. The abilities of host species to maintain and disseminate WNV, along with eco-epidemiological factors that influence vector-host contact rates underlie WNV transmission potential. There is growing evidence that several vectors exhibit strong feeding preferences towards different host communities. In our research study, we construct a process based weather driven ordinary differential equation (ODE) model to understand the impact of one vector species (Culex pipiens), its preferred avian and non-preferred human hosts on the basic reproduction number (R0). In developing this WNV transmission model, we account for the feeding index, which is defined as the relative preference of the vectors for taking blood meals from a competent avian host versus a non-competent mammalian host. We also include continuous introduction of infected agents into the model during the simulations as the introduction of WNV is not a single event phenomenon. We derive an analytic form of R0 to predict the conditions under which there will be an outbreak of WNV and the relationship between the feeding index and the efficacy of adulticide is highly nonlinear. In our mechanistic model, we also demonstrate that adulticide treatments produced significant reductions in the Culex pipiens population. Sensitivity analysis demonstrates that feeding index and rate of introduction of infected agents are two important factors beside the efficacy of adulticide. We validate our model by comparing simulations to surveillance data collected for the Culex pipiens complex in Cook County, Illinois, USA. Our results reveal that the interaction between the feeding index and mosquito abatement strategy is intricate, especially considering the fluctuating temperature conditions. This induces heterogeneous transmission patterns that need to be incorporated when modelling multi-host, multi-vector transmission models.

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