Enhanced spatial models for predicting the geographic distributions of tick-borne pathogens

Abstract

Background: Disease maps are used increasingly in the health sciences, with applications ranging from the diagnosis of individual cases to regional and global assessments of public health. However, data on the distributions of emerging infectious diseases are often available from only a limited number of samples. We compared several spatial modelling approaches for predicting the geographic distributions of two tick-borne pathogens: Ehrlichia chaffeensis, the causative agent of human monocytotropic ehrlichiosis, and Anaplasma phagocytophilum, the causative agent of human granulocytotropic anaplasmosis. These approaches extended environmental modelling based on logistic regression by incorporating both spatial autocorrelation (the tendency for pathogen distributions to be clustered in space) and spatial heterogeneity (the potential for environmental relationships to vary spatially).

The role of human awareness on the spatial patterns of the tick-borne disease human monocytic ehrlichiosis in Missouri.

“…In 2007 and2008, 202 and198 cases, respectively, were reported in Missouri (CDC 2009;CDC 2010). GIS risk analysis by Wimberly et al (2008b) and Yabsley et al (2005) has illustrate that HME is highly endemic to Missouri. Mapping actual cases in Missouri and comparing these results to the models show that these risk models overestimate the occurrence ofHME.…”

“…Soil type, forest cover, and temperature are a few factors important to the occurrence oftick-borne diseases (Guerra et al 2002;Yabsley et al 2005;Wimberley et al 2008a;2008b;Stein et al 2008). …”

“…Many ofthese involve the application of Geographic Information Systems (GIS) for spatial analysis and mapping. For tick-borne diseases, studies have been done on tick habitat suitability (Guerra et al 2002), vector mapping and prediction (Stein et al 2008;Keefe et al 2009), and risk mapping (Guerra et al 2002;Yabsley et al 2005;Wimberlyet al 2008a;Wimberly et al 2008b;), and are imperative for identifying key regions and locations where people may become exposed to ticks. These models are irrefutably important in identifying regions that contain high densities of ticks, and therefore where people are likely to come into contact with them.…”

“…The two key research articles mentioned (Yabsley et al 2005;Wimberly et al 2008b) found that ecological variables such as deciduous forest mix and other forest coverage, wooded wetlands, soil type, humidity, precipitation, among others, were important statistically significant predictor variables in creating risk maps ofHME in South-central U.S ( Figure 3). …”

“…This research project is a way to enhance some of the GIS-based research of HME spatial distribution, primarily the studies conducted by Yabsley et al (2005) and Wimberly et al (2008b). While GIS analysis must continually be incorporated in investigating vector-borne diseases and HME alike, other methods that include residential awareness and susceptibility must also be taken into account.…”