Table of Contents
ISRN Biomathematics
Volume 2012 (2012), Article ID 132342, 12 pages
Research Article

Spatially Explicit Nonlinear Models for Explaining the Occurrence of Infectious Zoonotic Diseases

1BlueCross BlueShield of Tennessee, Department of Medical Informatics, 1 Cameron Hill Circle, Building 2.1, Chattanooga, TN 37402, USA
2Forestry and Natural Resources Department, Clemson University, Georgetown, SC, USA

Received 2 August 2012; Accepted 24 September 2012

Academic Editors: H. Ishikawa, M. Jose, Y. Pan, and W. Raffelsberger

Copyright © 2012 Stephen Jones et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Zoonotic diseases can be transmitted via an arthropod vector, and disease risk maps are often created based on underlying associative factors within the surrounding landscape of known occurrences. A limitation however is the ability to map disease risk at a meaningful geographic scale, and traditional regression modeling approaches may not always be appropriate. Our objective was to determine if nonlinear modeling could improve explanatory power in describing the occurrence of 2 tick-borne diseases (Lyme disease (LD) and Rocky Mountain spotted fever (RMSF)) known to occur in Tennessee. Medically diagnosed cases of LD (ICD-9: 088.81) and RMSF (ICD-9: 082.0) were extracted from a managed care organization data warehouse for the 2000–2009 time period. Four separate modeling techniques were constructed (logistic regression, classification and regression tree (CART), gradient boosted tree (GBT), and neural network (NNET)) and compared for accuracy. Results suggest that areas higher in disease prevalence were not necessarily the same areas having high predicted disease risk. GBT best explained LD occurrence (misclassification rate: 0.232; ROC: 0.789). RMSF prevalence was best explained with an NNET algorithm (misclassification rate: 0.288; ROC: 0.696). Covariates explaining disease risk included forested wetlands, urbanization, and median income. Nonlinear modeling may provide better results than traditional regression-based approaches.