Enhanced spatial models for predicting the geographic distributions of tick-borne pathogens |
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Authors: | Michael C Wimberly Adam D Baer Michael J Yabsley |
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Institution: | (1) Geographic Information Science Center of Excellence, South Dakota State University, Brookings, SD, USA;(2) Southeastern Cooperative Wildlife Disease Study, University of Georgia, Athens, GA, USA;(3) Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA, USA |
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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). |
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