Site-specific prediction of West Nile virus mosquito abundance in Greater Toronto Area using generalized linear mixed models

Mosquito surveillance programs provide a primary means of understanding mosquito vector population dynamics for the risk assessment of human exposure to West Nile virus (WNv). The lack of spatial coverage and missing observations in mosquito surveillance data often challenge our efforts to predict this vector-borne disease and implement control measures. We developed a WNv mosquito abundance prediction model in which local meteorological and environmental data were synthesized with entomological data in a generalized linear mixed modeling framework. The discrete nature of mosquito surveillance data is accommodated by a Poisson distributional assumption, and the site-specific random effects of the generalized linear mixed model (GLMM) capture any fluctuation unexplained by a general trend. The proposed Poisson GLMMs efficiently account for the nested structure of mosquito surveillance data and incorporate the temporal correlation between observations obtained at each trap by a first-order autoregressive model. In the case study, Bayesian inference of the proposed models is illustrated using a subset of mosquito surveillance data in the Greater Toronto Area. The relevance of the proposed GLMM tailored to WNv mosquito surveillance data is highlighted by the comparison of model performance in the presence of inevitable but quantifiable uncertainties.