Objectives
- To determine the prevalence patterns in microenvironments within Schistosomiasis endemic areas for a more effective control programme.
- To build geographical models of infection, pathology and innate immune response interrelationships.
Description of work
In collaboration with the regional Centres for Remote Sensing and Geographical Information Systems a workshop will be organised to teach GPS/GIS theory, concepts, use of spatial data, and choice of software. GPS handset use will be taught as well as ArcGIS program for data analysis and generation of choropleth maps.
Villages will be surveyed by researchers trained and equipped with GPS units. Villages will be surveyed and home coordinates recorded and linked to subject identification number. The distance to water bodies, altitude, distance to a health centre, and the school position attended by children will be collected. From the population aged 5-40, stool, urine and blood examination of parasitological infections as in WP1 will be linked to the GPS data on desktop/laptop computers. This will allow coordinates to be converted into point files that can be made into grade maps. These point files can be overlaid upon shape files (e.g. vegetation, drainage, roads) for the study areas. The results of ultrasound will be entered in a similar way. The baseline distribution maps of schistosome, malaria and intestinal helminth infections will be generated using ArcGIS. Although we expect a relatively small number of subjects with pathology, attempts will be made to also generate maps of pathology (‘light’ and ‘severe’). Data on parameters such as anaemia and body mass index are other clinical scores that can be studied.
For immunology, data will be selected (e.g. IFN-γ or TLR-2 expression, if tabular data indicate that these have predictive values for immunoregulation, or pathology) to be added to geographic data and analysed spatially. Multivariate statistical analysis will be applied to the data to understand determinants of infection and disease with a link to environmental settings.
At one level, the maps will reveal the spatial aggregation of infection and pathology within microenvironments, which will be utilised by local control teams to intensify treatment and surveillance. At another level, the geographical model created will help discover new processes and relationships of scientific interest.
Demographic data, geographical location, water contact behaviour and the various factors of the physical geography will be put in a logistic regression model to determine the best models for dynamics of disease and relationship with the environment and the immune system.
If the work progresses rapidly and time allows, we may consider the purchase of digital high-resolution images (Quick Bird: Resolution 0.6m) to overlay our dataset on available physical maps of the area.