Risk Mapping and Prediction of Neglected Tropical Diseases

Risk Mapping and Prediction of Neglected Tropical Diseases

Neglected tropical diseases are pervasive in rural communities of resource-constrained countries and they are intimately linked with the lack of clean water and adequate sanitation, and hence poverty-promoting. An important epidemiological feature of the neglected tropical diseases is their focal distribution. In order to target limited resources for control in a cost-effective manner, our research aims to develop new approaches for prediction and mapping of high-risk areas to guide spatial targeting of control interventions.

For the mountainous region of Man in western Côte d’Ivoire, we were able to predict high-risk areas for Schistosoma mansoni, using a diversity of data sources (i.e. demographic and socio- economic data derived from questionnaire surveys, and environmental features obtained from remotely-sensed satellite sensors) and Bayesian variogram models. A digital elevation model was developed to characterise rivers according to the stream order and to delineate catchment areas. The data were incorporated into a geographical information system (GIS) and spatially-explicit analysis found highly significant associations between the prevalence of S. mansoni at the unit of a school and stream order and catchment area. Hence, these two hydrological features were good predictors for mapping the high-risk areas of S. mansoni. In a next step, risk maps for hookworm infections were generated, using the same data sources. Bivariate analysis showed that sex, age, socio-economic status, elevation, rainfall and land cover were significantly associated with the spatial distribution of hookworm infection, but spatial correlation depended only marginally on the location. Next, we predicted high-risk areas for S. mansoni-hookworm co-infections. Significant risk factors for a co-infection were age (children aged 11-16 years were at a higher risk than their younger counterparts, sex (boys were at a higher risk than girls), altitude (schools located below 400 m above sea level were at higher risk than schools at higher altitudes) and socio-economic status (poorer households were at higher risk than better-off households). Our integrated risk profiling approach holds promise for prediction and mapping of other neglected tropical diseases. In a next step, we aim to predict infection intensities of single and multiple species parasitic infections, including malaria.

http://www.sti.ch/research/public-health-and-epidemiology/ecosystem-health-sciences/risk-mapping-and-prediction-of-neglected-tropical-diseases.html