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

Michael C Wimberly,Adam D Baer,Michael J Yabsley

doi:10.1186/1476-072x-7-15

Abstract

BackgroundDisease 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).ResultsIncorporating either spatial autocorrelation or spatial heterogeneity resulted in substantial improvements over the standard logistic regression model. For E. chaffeensis, which was common within the boundaries of its geographic range and had a highly clustered distribution, the model based only on spatial autocorrelation was most accurate. For A. phagocytophilum, which has a more complex zoonotic cycle and a comparatively weak spatial pattern, the model that incorporated both spatial autocorrelation and spatially heterogeneous relationships with environmental variables was most accurate.ConclusionSpatial autocorrelation can improve the accuracy of predictive disease risk models by incorporating spatial patterns as a proxy for unmeasured environmental variables and spatial processes. Spatial heterogeneity can also improve prediction accuracy by accounting for unique ecological conditions in different regions that affect the relative importance of environmental drivers on disease risk.

Highlights

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
This surveillance approach was based on immunofluorescent antibody (IFA) tests performed on serum samples from white-tailed deer, and its efficacy has been confirmed by comparisons with polymerase chain reaction assays and culture isolations
We found that spatial interpolation of E. chaffeensis based on indicator kriging was more accurate than environmental predictions based on logistic regression models [13]

Summary

Introduction

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. Disease risk is defined as the probability of an individual contracting a disease within a specific time period [5], direct measurements of risk can be difficult to obtain, and disease maps are often based on presumed correlates of risk such as vector abundance, pathogen prevalence in a sentinel species, or disease frequency in human populations. Another challenge in developing disease maps is that the underlying data may be available at a limited number of isolated locations. It is often necessary to interpolate between isolated sample locations to generate a continuous surface of disease risk predictions

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