Bayesian geostatistical prediction of the intensity of infection with Schistosoma mansoni in East Africa.
A Bayesian geostatistical model was developed to predict the intensity of infection with Schistosoma mansoni in East Africa. Epidemiological data from purpose-designed and standardized surveys were available for 31,458 schoolchildren (90% aged between 6 and 16 years) from 459 locations across the region and used in combination with remote sensing environmental data to identify factors associated with spatial variation in infection patterns. The geostatistical model explicitly takes into account the highly aggregated distribution of parasite distributions by fitting a negative binomial distribution to the data and accounts for spatial correlation. Results identify the role of environmental risk factors in explaining geographical heterogeneity in infection intensity and show how these factors can be used to develop a predictive map. Such a map has important implications for schisosomiasis control programmes in the region.
Clements
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Department of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK.
Moyeed
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Brooker
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D000293:Adolescent; D000328:Adult; D000349:Africa / Q000453:epidemiology; D000818:Animals; D001499:Bayes Theorem; D002648:Child; D002675:Child, Preschool; D005260:Female; D006801:Humans; D008297:Male; D008390:Markov Chains; D008954:Models, Biological; D011237:Predictive Value of Tests; D012550:Schistosoma mansoni / Q000302:isolation & purification; D012555:Schistosomiasis mansoni / Q000453:epidemiology* / Q000469:parasitology