Dataset Information

Spatiotemporal Bayesian modeling of West Nile virus: Identifying risk of infection in mosquitoes with local-scale predictors.

ABSTRACT: Monitoring and control of West Nile virus (WNV) presents a challenge to state and local vector control managers. Models of mosquito presence and viral incidence have revealed that variations in mosquito autecology and land use patterns introduce unique dynamics of disease at the scale of a county or city, and that effective prediction requires locally parameterized models. We applied Bayesian spatiotemporal modeling to West Nile surveillance data from 49 mosquito trap sites in Nassau County, New York, from 2001 to 2015 and evaluated environmental and sociological predictors of West Nile virus incidence in Culex pipiens-restuans. A Bayesian spike-and-slab variable selection algorithm was used to help select influential independent variables. This method can be used to identify locally-important predictors. The best model predicted West Nile positives well, with an Area Under Curve (AUC) of 0.83 on holdout data. The temporal trend was nonlinear and increased throughout the year. The spatial component identified increased West Nile incidence odds in the northwestern portion of the county, with lower odds in wetlands on the south shore of Long Island. High Normalized Difference Vegetation Index (NDVI) areas, wetlands, and areas of high urban development had negative associations with WNV incidence. In this study we demonstrate a method for improving spatiotemporal models of West Nile virus incidence for decision making at the county and community scale, which empowers disease and vector control organizations to prioritize and evaluate prevention efforts.

SUBMITTER: Myer MH

PROVIDER: S-EPMC7676626 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Spatiotemporal Bayesian modeling of West Nile virus: Identifying risk of infection in mosquitoes with local-scale predictors.

Myer Mark H MH Johnston John M JM

The Science of the total environment 20181002 Pt 2

Monitoring and control of West Nile virus (WNV) presents a challenge to state and local vector control managers. Models of mosquito presence and viral incidence have revealed that variations in mosquito autecology and land use patterns introduce unique dynamics of disease at the scale of a county or city, and that effective prediction requires locally parameterized models. We applied Bayesian spatiotemporal modeling to West Nile surveillance data from 49 mosquito trap sites in Nassau County, New ...[more]

PMID: 30373059

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Project description: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.

Project description:ImportanceWest Nile virus (WNV) is the leading cause of domestically acquired arboviral disease.ObjectiveTo develop real-time WNV forecasts of infected mosquitoes and human cases.Design, setting, and participantsReal-time forecasts of WNV in 4 geographically dispersed locations in the United States were generated using a WNV model-inference forecasting system previously validated with retrospective data. Analysis was performed to evaluate how observational reporting delays of mosquito WNV assay results and human medical records were associated with real-time forecast accuracy.ExposuresMosquitoes positive for WNV and human cases.Main outcomes and measuresDelays in reporting mosquito WNV assay results and human medical records and the association of these delays with real-time WNV forecast accuracy.ResultsSubstantial delays in data reporting exist for both infected mosquitoes and human WNV cases. For human cases, confirmed data (n = 37) lagged behind the onset of illness by a mean (SD) of 5.5 (2.3) weeks (range, 2-14 weeks). These human case reporting lags reduced mean forecast accuracy for the total number of human cases over the season in 110 simulated outbreaks for 2 forecasting systems by 26% and 14%, from 2 weeks before to 3 weeks after the predicted peak of infected mosquitoes. This period is the time span during which 47% of human cases are reported. Of 7064 mosquito pools, 500 (7%) tested positive; the reporting lag for these data associated with viral testing at a state laboratory was a mean (SD) of 6.6 (2.6) days (range, 4-11 days). This reporting lag was associated with decreased mean forecast accuracy for the 3 mosquito infection indicators, timing, magnitude, and season, by approximately 5% for both forecasting systems.Conclusions and relevanceDelays in reporting human WNV disease and infected mosquito information are associated with difficulties in outbreak surveillance and decreased real-time forecast accuracy. Infected mosquito lags were short enough that skillful forecasts could still be generated for mosquito infection indicators, but the human WNV case lags were too great to support accurate forecasting in real time. Forecasting WNV is potentially an important evidence-based decision support tool for public health officials and mosquito abatement districts; however, to operationalize real-time forecasting, more resources are needed to reduce human case reporting lags between illness onset and case confirmation.

Project description:BackgroundWest Nile virus (WNV), primarily vectored by mosquitoes of the genus Culex, is the most important mosquito-borne pathogen in North America, having infected thousands of humans and countless wildlife since its arrival in the USA in 1999. In locations with dedicated mosquito control programs, surveillance methods often rely on frequent testing of mosquitoes collected in a network of gravid traps (GTs) and CO2-baited light traps (LTs). Traps specifically targeting oviposition-seeking (e.g. GTs) and host-seeking (e.g. LTs) mosquitoes are vulnerable to trap bias, and captured specimens are often damaged, making morphological identification difficult.MethodsThis study leverages an alternative mosquito collection method, the human landing catch (HLC), as a means to compare sampling of potential WNV vectors to traditional trapping methods. Human collectors exposed one limb for 15 min at crepuscular periods (5:00-8:30 am and 6:00-9:30 pm daily, the time when Culex species are most actively host-seeking) at each of 55 study sites in suburban Chicago, Illinois, for two summers (2018 and 2019).ResultsA total of 223 human-seeking mosquitoes were caught by HLC, of which 46 (20.6%) were mosquitoes of genus Culex. Of these 46 collected Culex specimens, 34 (73.9%) were Cx. salinarius, a potential WNV vector species not thought to be highly abundant in upper Midwest USA. Per trapping effort, GTs and LTs collected > 7.5-fold the number of individual Culex specimens than HLC efforts.ConclusionsThe less commonly used HLC method provides important insight into the complement of human-biting mosquitoes in a region with consistent WNV epidemics. This study underscores the value of the HLC collection method as a complementary tool for surveillance to aid in WNV vector species characterization. However, given the added risk to the collector, novel mitigation methods or alternative approaches must be explored to incorporate HLC collections safely and strategically into control programs.

Project description:BackgroundModels of the effects of environmental factors on West Nile virus disease risk have yielded conflicting outcomes. The role of precipitation has been especially difficult to discern from existing studies, due in part to habitat and behavior characteristics of specific vector species and because of differences in the temporal and spatial scales of the published studies. We used spatial and statistical modeling techniques to analyze and forecast fine scale spatial (2000 m grid) and temporal (weekly) patterns of West Nile virus mosquito infection relative to changing weather conditions in the urban landscape of the greater Chicago, Illinois, region for the years from 2004 to 2008.ResultsIncreased air temperature was the strongest temporal predictor of increased infection in Culex pipiens and Culex restuans mosquitoes, with cumulative high temperature differences being a key factor distinguishing years with higher mosquito infection and higher human illness rates from those with lower rates. Drier conditions in the spring followed by wetter conditions just prior to an increase in infection were factors in some but not all years. Overall, 80% of the weekly variation in mosquito infection was explained by prior weather conditions. Spatially, lower precipitation was the most important variable predicting stronger mosquito infection; precipitation and temperature alone could explain the pattern of spatial variability better than could other environmental variables (79% explained in the best model). Variables related to impervious surfaces and elevation differences were of modest importance in the spatial model.ConclusionFinely grained temporal and spatial patterns of precipitation and air temperature have a consistent and significant impact on the timing and location of increased mosquito infection in the northeastern Illinois study area. The use of local weather data at multiple monitoring locations and the integration of mosquito infection data from numerous sources across several years are important to the strength of the models presented. The other spatial environmental factors that tended to be important, including impervious surfaces and elevation measures, would mediate the effect of rainfall on soils and in urban catch basins. Changes in weather patterns with global climate change make it especially important to improve our ability to predict how inter-related local weather and environmental factors affect vectors and vector-borne disease risk.Local impact of temperature and precipitation on West Nile virus infection in Culex species mosquitoes in northeast Illinois, USA.

Dataset Information

Spatiotemporal Bayesian modeling of West Nile virus: Identifying risk of infection in mosquitoes with local-scale predictors.

Publications

Spatiotemporal Bayesian modeling of West Nile virus: Identifying risk of infection in mosquitoes with local-scale predictors.

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