Avian GIS models signal human risk for West Nile virus in Mississippi

Abstract

BackgroundWest Nile virus (WNV) poses a significant health risk for residents of Mississippi. Physicians and state health officials are interested in new and efficient methods for monitoring disease spread and predicting future outbreaks. Geographic Information Systems (GIS) models have the potential to support these efforts. Environmental conditions favorable for mosquito habitat were modeled using GIS to derive WNV risk maps for Mississippi. Variables important to WNV dissemination were selected and classified as static and dynamic. The static variables included road density, stream density, slope, and vegetation. The dynamic variable represented seasonal water budget and was calculated using precipitation and evaporation estimates. Significance tests provided deterministic evidence of variable importance to the models.ResultsSeveral models were developed to estimate WNV risk including a landscape-base model and seasonal climatic sub-models. P-values from t-tests guided variable importance ranking. Variables were ranked and weights assigned as follows: road density (0.4), stream density (0.3), slope (0.2) and vegetation (0.1). This landscape-base model was modified by climatic conditions to assess the importance of climate to WNV risk. Human case data at the zip code level were used to validate modeling results. All models were summarized by zip codes for interpretation and model validation. For all models, estimated risk was higher for zip codes with at least one human case than for zip codes where no human cases were recorded. Overall median measure of risk by zip code indicated that 67% of human cases occurred in the high-risk category.ConclusionModeling results indicated that dead bird occurrences are correlated with human WNV risk and can facilitate the assessment of environmental variables that contribute to that risk. Each variable's importance in GIS-based risk predictions was assigned deterministically. Our models indicated non-uniform distribution of risk across the state and showed elevated risk in urban and as well as rural areas. Model limitations include resolution of human data, zip code aggregation issues, and quality/availability of vegetation and stream density layers. Our approach verified that WNV risk can be modeled at the state level and can be modified for risk predictions of other vector-borne diseases in varied ecological regions.

Environmental Factors and Risk Areas of West Nile Virus in Southern California, 2007–2009

“…Greenness level, distance to a WNV-positive dead bird species, geological factors, and socioeconomic conditions (e.g., age of population and age of housing) are shown to be the risk factors for human WNV infections in the greater Chicago area [46]. Road density, stream density, slope, and vegetation are closely related to avian WNV epidemics in the state of Mississippi [10]. In addition, a simulation model has been developed to describe the population dynamics of mosquitoes and American crows, two major factors of WNV dissemination in North America [3].…”

“…Winters et al [53] employed regression analysis to develop spatial models for predicting high-risk areas of exposure to WNV in western and eastern Colorado based on the human WNV cases from 2002 to 2006. Studies, at various scales, include the consideration of static and dynamic variables on environmental and socioeconomic conditions in relation to WNV epidemics in mammals, including humans and insects [3,10,29,46]. Greenness level, distance to a WNV-positive dead bird species, geological factors, and socioeconomic conditions (e.g., age of population and age of housing) are shown to be the risk factors for human WNV infections in the greater Chicago area [46].…”

“…Its dissemination varies seasonally in response to the characteristics of environmental and socioeconomic conditions and climate [2,32,41,42]. Nonhuman WNV surveillance can be used as an early warning for WNV activity [10,35]. The abrupt appearance of WNV in North America indicates arboviruses may continue to emerge in new regions [20].…”

A comparison of least squares regression and geographically weighted regression modeling of West Nile virus risk based on environmental parameters

“…Our alternative strategy was to assess WNV infected dead bird counts as a surrogate measure of human risk because “infection rates” in dead birds can be more precise because of the genetic markers tested in dead birds may be more reliable than case data and/or surveillance data. Additionally, others have also used mosquito habitat suitability as a surrogate for estimating WNV risk for human infection (Cooke, Grala & Wallis, 2006). For our study, we followed a similar approach and used a model of mosquito habitat suitability condition as a predictor of the spatial distributions of infected birds, which in turn can be used to estimate WNV disease risk among human populations.…”

“…Subsequently, the disease may be transmitted by infected mosquitoes to humans or other mammals that act as incidental hosts. Dead birds found to be infected with WNV are often the primary indicators for presence of the disease in a geographic region and have proven to be useful for disease prediction modeling and identifying areas for human infection risk (Cooke, Grala & Wallis, 2006; Ruiz et al, 2004; Valiakos et al, 2014). This relationship allows an assumption of a positive correlation between infected dead birds and WNV risk.…”