User talk:AbhishekKala

West Nile Virus - spatially explicit prediction modeling

West Nile Virus (WNV) is a vector-borne disease that was first detected in the United States in 1999 (Nash et al., 2001). Within a few years the virus had spread across the North American continent (Hayes et al., 2005). WNV has had important environmental and human impacts, including a decline in numerous bird species (CDC) and increased morbidity and mortality among humans. This has also resulted in increased economic burdens due to initial acute health care needs of infected individuals and subsequent long-term costs associates with infection, estimated at approximately $56 million per year between 1999 and 2012 (Barrett, 2014). Because that study indicated how difficult predicting and planning for WNV outbreaks was, we became interested in developing a spatially explicit model using environmental factors in an attempt to improve WNV risk predictions.

There are two important considerations that should typically be examined when developing spatially explicit environmental disease risk models (Miller, 2012). The first should be an examination of potential spatial autocorrelation (the degree to which a set of spatial features and their associated data values tend to be clustered together in space). This involves accounting for whether environmental factors and the corresponding disease rates in geographically proximate areas are more or less clustered together than they are in geographically distant areas. Second, data non-stationarity (changing means, variances and covariances in data across space) should be investigated and controlled when necessary (Fotheringham, 2009a; Miller, 2012). Geographically weighted regression (GWR) can be used for these two considerations and can often produce improved models that enable better spatial inference and prediction. Recent studies have applied GWR modeling to drug-resistant tuberculosis versus risk factors (Liu et al., 2011); environmental factors versus typhoid fever (Dewan et al., 2013); local climate and population distribution versus hand, foot, and mouth disease (Hu et al., 2012); and environmental factors and tick-borne disease (Atkinson et al., 2012; Atkinson et al., 2014; Wimberly, Baer & Yabsley, 2008; Wimberly et al., 2008), all showing that predictor variables varied spatially across large geographic regions, implying that the results for such studies may be improved using GWR.

The spatially explicit model that is discussed in this paper uses GWR to account for spatial heterogeneity for two reasons: (a) WNV disease risk observed across space may be related to similar environmental variables that increase vector habitat suitability and (b) environmental variables that influence WNV risk are not typically uniformly distributed across geographic space. Although many epidemiological models of WNV risk have been developed, it appears that there has been little research to explicitly examine techniques that account for spatial heterogeneity. Most models assume that the impact of various environmental factors are constant across the study region, which is unrealistic as larger areas display substantial variations in distribution of environmental, socio-economic, and demographic conditions (Goovaerts, 2008).

Due to the unavailability of reliable and complete data, developing models of WNV risk pose additional challenges. Human case data is lacking due to issues of under-reporting and limited surveillance. 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. Further, because the environmental variables considered in this study are known to vary across space, we account for spatial autocorrelation and non-stationarity using GWR following the approach of (DeGroote et al., 2008) in order to improve the predictability of a model.