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Generalized linear spatial models in epidemiology: A case study of zoonotic cutaneous leishmaniasis in Tunisia

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  • K. Ben-Ahmed
  • A. Bouratbine
  • M. -A. El-Aroui

Abstract

Generalized linear spatial models (GLSM) are used here to study spatial characters of zoonotic cutaneous leishmaniasis (ZCL) in Tunisia. The response variable stands for the number of affected by district during the period 2001-2002. The model covariates are: climates (temperature and rainfall), humidity and surrounding vegetation status. As the environmental and weather data are not available for all the studied districts, Kriging based on linear interpolation was used to estimate the missing data. To account for unexplained spatial variation in the model, we include a stationary Gaussian process S with a powered exponential spatial correlation function. Moran coefficient, DIC criterion and residuals variograms are used to show the high goodness-of-fit of the GLSM. When compared with the statistical tools used in the previous ZCL studies, the optimal GLSM found here yields a better assessment of the impact of the risk factors, a better prediction of ZCL evolution and a better comprehension of the disease transmission. The statistical results show the progressive increase in the number of affected in zones with high temperature, low rainfall and high surrounding vegetation index. Relative humidity does not seem to affect the distribution of the disease in Tunisia. The results of the statistical analyses stress the important risk of misleading epidemiological conclusions when non-spatial models are used to analyse spatially structured data.

Suggested Citation

  • K. Ben-Ahmed & A. Bouratbine & M. -A. El-Aroui, 2010. "Generalized linear spatial models in epidemiology: A case study of zoonotic cutaneous leishmaniasis in Tunisia," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(1), pages 159-170.
    • Handle: RePEc:taf:japsta:v:37:y:2010:i:1:p:159-170
    DOI: 10.1080/02664760802684169
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    References listed on IDEAS

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    2. Ole F. Christensen & Rasmus Waagepetersen, 2002. "Bayesian Prediction of Spatial Count Data Using Generalized Linear Mixed Models," Biometrics, The International Biometric Society, vol. 58(2), pages 280-286, June.
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