Development of a Classification Scheme for Managing Human African Trypanosomiasis using Geospatial Techniques

Abstract

Distinctive environ-climatic variables have been associated with Trypanosoma brucei gambiense spatial characteristics, signifying the importance of physical landscape in HAT propagation/risk. Nevertheless, techniques projected to classify human African trypanosomiasis (HAT) vector habitats tend to be generalised, time wasting and costly.  Despite control efforts, HAT has become resurgent in some locations. No model to acquire detailed and comprehensive HAT spatial or epidemiological data exists for the study area, meaning many of those most in need, especially those  residing in remotest parts of the region, may not be benefitting from good health care due to lack of information about them. This paper proposes a geospatial technique to explore vector habitat mapping. The goal was to develop a surveillance methodology that will facilitate quick and efficient management of HAT.  Supervised classification and fuzzy logic were integrated to classify land cover and ancillary datasets into HAT vector habitat. The importance of criteria and how they were prioritised were determined by the judgments of experts, the impact of the criteria on HAT propagation and previous studies. Spatial distribution/habitat characteristics play an important role in HAT propagation. Therefore, locations which have all or most of these criteria present are vital for HAT propagation.

This study helped distinguish HAT vector habitat into different zones (breed, feed and rest), the classification scheme is expected to offer effective decision support to all stakeholders.

Keywords: Geospatial, Remote Sensing, Geographic Information Systems, Fuzzy, Vector