Show simple item record

dc.contributor.authorWimberly, Michael C
dc.contributor.authorBaer, Adam D
dc.contributor.authorYabsley, Michael J
dc.date.accessioned2013-06-12T14:38:54Z
dc.date.available2013-06-12T14:38:54Z
dc.date.issued2008-04-15
dc.identifier.citationInternational Journal of Health Geographics. 2008 Apr 15;7(1):15
dc.identifier.urihttp://dx.doi.org/10.1186/1476-072X-7-15
dc.identifier.urihttp://hdl.handle.net/10724/19520
dc.description.abstractAbstract 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). Results Incorporating 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. Conclusion Spatial 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.
dc.titleEnhanced spatial models for predicting the geographic distributions of tick-borne pathogens
dc.typeJournal Article
dc.date.updated2013-06-07T18:49:50Z
dc.description.versionPeer Reviewed
dc.language.rfc3066en
dc.rights.holderMichael C Wimberly et al.; licensee BioMed Central Ltd.


Files in this item

Thumbnail
Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record