Geostatistical model-based predictions of helminthiases risk to assist control interventions

A reduction in the burden caused by helminthic infections has been incorporated into the Millennium Development Goals (MDG). A good knowledge and understanding of the geographical distribution of the infections and reliable high resolution risk estimates are needed for cost-effective planning, targeting and monitoring of control interventions.
The work presented in this PhD thesis develops and implements the Bayesian geostatistical methodology for modelling helminthiases risk with a particular emphasis on Bayesian variable selection, the modelling of large spatial dataset and the spatio-temporal aspects of the distribution. Our applications focus on soil-transmitted helminth infections in Latin America where limited information on the geographical distribution and infection risk have hampered adequate control measures, as well as on schistosomiasis in Côte d’Ivoire, a country where implementation of interventions suffered from a decade of political instabilities.
This thesis provides important baseline information for control programmes and a benchmark upon which further estimates could be compared, as soon as new data become available and interventions are progressing.