A risk index model for predicting eastern equine encephalitis virus transmission to horses in Florida

Abstract

A GIS-based risk index model was developed to quantify EEEV transmission risk to horses in the State of Florida. EEEV is a highly pathogenic arbovirus that is endemic along the east coast of the United States, and it is generally fatal to both horses and humans. The model evaluates EEEV transmission risk at individual raster cells in map on a continuous scale of 0 to 1. The risk index is derived based on local habitat features and the composition and configuration of surrounding land cover types associated with EEEV transmission. The model was verified and validated using the locations of documented horse cases of EEEV. These results of the verification and validation indicate that the model is able to predict locations of EEEV transmission to horses broadly across the state. The model is relatively robust to regional variation in EEEV transmission and habitat conditions in Florida, and it accurately predicted nearly all verification and validation cases in the Panhandle, North, and Central regions of the state. The model performed less accurately in the South, where relatively few cases are documented. Despite these differences, the model provides a useful way to assess EEEV risk both from a regional perspective and at more localized scales. The resulting predictive maps are designed to guide EEEV surveillance and prevention efforts by county mosquito control districts.

Mapping eastern equine encephalitis virus risk for white-tailed deer in Michigan

“…Risk index models use similar techniques to measure risk on a scale from 0.0 (no measurable risk) to 1.0 (highest possible risk). The model developed here follows the same basic structure as a risk index model used to predict EEEV risk to horses in Florida (Vander Kelen et al, 2014), but the inputs (termed risk variables) are adapted to reflect habitat preferences of white-tailed deer and the relevant vector mosquito species in Michigan. The model quantifies EEEV risk to white-tailed deer at individual locations in a map (represented as raster cells) based on the habitat type at that location and the composition and configuration of surrounding land cover types.…”

“…The habitat type used most often was upland deciduous forest (27.12%). As per Vander Kelen et al (2014), this cover type was assigned the highest risk value of 1.0. Risk values for remaining cover types were calculated by dividing the reported percentage for that type by that of the highest class to create a ratio of relative risk.…”

“…The same formulation for RV2 used by Vander Kelen et al (2014) is used here. RV2 is calculated based on the distance from the focal raster cell (the raster cell being evaluated) to the nearest wetland in meters, d w , where: $$RV2=true\{\begin{array}{c}\hfill 1if{d}_w\le 400\hfill \\ \hfill 1-\left(\frac{d_w-400}{1500-400}\right)\hfill \\ \hfill 0if{d}_w\ge 1500\hfill \end{array}if400<d_w<1500true\}.$$…”

“…If a cell is more than 1500 m away, it is assigned 0.0. The distance parameters were modelled after Vander Kelen et al (2014).…”

Epidemiological surveillance methods for vector-borne diseases

“…Eisen and Eisen (47) give several examples of the use of spatial risk models, both to identify risk factors for vector and/or disease occurrence and to generate risk maps, which may be useful in predicting disease occurrence. Other examples of the use of GIS-based models include risk index models for predicting eastern equine encephalitis virus transmission to horses (48) and human WNV infection (49), generalised linear models using remotely sensed environmental variables to predict BTV seropositivity in cattle in northern Australia (50) and WNV vector abundance in Greater Toronto (51), and near real-time monitoring of sea surface temperatures and the normalised difference vegetation index (NDVI) to predict RVF outbreaks in Africa (52). Spatial analysis of syndromic surveillance data for acute encephalitis syndrome in Nepal was used to conclude that this approach could be useful to provide an early warning signal for JE surveillance (53).…”