Spatiotemporal modeling of ecological and sociological predictors of West Nile virus in Suffolk County, NY, mosquitoes

Abstract

Suffolk County, New York, is a locus for West Nile virus (WNV) infection in the American northeast that includes the majority of Long Island to the east of New York City. The county has a system of light and gravid traps used for mosquito collection and disease monitoring. In order to identify predictors of WNV incidence in mosquitoes and predict future occurrence of WNV, we have developed a spatiotemporal Bayesian model, beginning with over 40 ecological, meteorological, and built-environment covariates. A mixed-effects model including spatially and temporally correlated errors was fit to WNV surveillance data from 2008 to 2014 using the R package “R-INLA,” which allows for Bayesian modeling using the stochastic partial differential equation (SPDE) approach. The integrated nested Laplace approximation (INLA) SPDE allows for simultaneous fitting of a temporal parameter and a spatial covariance, while incorporating a variety of likelihood functions and running in R statistical software on a home computer. We found that land cover classified as open water and woody wetlands had a negative association with WNV incidence in mosquitoes, and the count of septic systems was associated with an increase in WNV. Mean temperature at two-week lag was associated with a strong positive impact, while mean precipitation at no lag and one-week lag was associated with positive and negative impacts on WNV, respectively. Incorporation of spatiotemporal factors resulted in a marked increase in model goodness-of-fit. The predictive power of the model was evaluated on 2015 surveillance results, where the best model achieved a sensitivity of 80.9% and a specificity of 77.0%. The spatial covariate was mapped across the county, identifying a gradient of WNV prevalence increasing from east to west. The Bayesian spatiotemporal model improves upon previous approaches, and we recommend the INLA SPDE methodology as an efficient way to develop robust models from surveillance data to develop and enhance monitoring and control programs. Our study confirms previously found associations between weather conditions and WNV and suggests that wetland cover has a mitigating effect on WNV infection in mosquitoes, while high septic system density is associated with an increase in WNV infection.

Dynamic spatiotemporal modeling of the infected rate of visceral leishmaniasis in human in an endemic area of Amhara regional state, Ethiopia

“… was 0.276 and was 0.0176. We designated that more variation is explained by the spatial term rather than by the error, This result is in line with the study by [20], [29], [30]. Additionally, the high value of the AR(1) temporal correlation coefficient confirms the short-term persistence of Infected rate of Visceral leishmaniasis.…”

The Role of Temperature in Transmission of Zoonotic Arboviruses

“…A wide variety of models have been used to model temperature effects on arboviruses, including Machine Learning techniques [148,149,150], a real-time Bayesian Ensemble Adjustment Kalman Filter method [151], spatiotemporal Bayesian models [152,153], generalized linear models [150,154,155,156,157], case-crossover approaches [158], seasonal autoregressive models [159,160], R 0 models [3], and Susceptible-Infectious-Recovered (SIR) and Susceptible-Exposed-Infectious-Recovered (SEIR) models [3,124,151,161,162,163,164,165,166,167]. West Nile virus is by far the most studied enzootic arbovirus (Table 3).…”

“…Minimum summer temperature was found to predict human and mosquito cases in New York and Connecticut [149]. Increased temperatures at a two-week lag interval were associated with WNV in Suffolk County, NY [152] and Nassau County, NY [153]. August and September temperatures were associated with increased WNV incidence in Russia [186].…”

Narrow oviposition preference of an insect herbivore risks survival under conditions of severe drought

“…This method is increasingly being used in ecological studies as an efficient way to model species occurrences and population dynamics while accounting for spatial and temporal dependencies in the data (e.g. : Myer, Campbell, & Johnston, 2017;Schulz et al, 2019;Ward et al, 2015). In brief, spatial dependency of observations are accounted for using a latent Gaussian random field, which we constructed using a two-dimensional irregular grid (mesh) based on the geographic coordinates of cell centroids.…”