Identifying Diseased Areas using a Geospatially Developed Human African Trypanosomiasis Vector Habitat Classification Scheme

Abstract

Human African Trypanosomiasis (HAT) is a vector-borne disease transmitted by the bite of the tsetse fly that results in high human morbidity and mortality.  There is no HAT vaccine, but biological control of the vector has been successful in reducing HAT incidence. However, in recent years the disease has re-emerged and spread. Due to insufficient knowledge of HAT endemic foci, the disease management remains challenging. Information is vital to effective disease management, but the level of underreporting of disease, most especially HAT in Nigeria, impedes progress. The information gap, such as comprehensive digital spatial epidemiological information/data, could be reduced with geo-referenced studies, lacking in some previous work.  To achieve effective deployment of control strategies, accurate knowledge of the spatial distribution of the disease is vital. The current study is based in Nigeria, and looks at part of Delta State, in which HAT has been identified.  The study utilizes a previously geospatially developed HAT vector habitat classification scheme, to explore the dynamics of HAT propagation. The goal was to map the direction and magnitude of HAT and identify HAT vulnerable/risk areas. This helped identify ‘HAT priority intervention areas’. The study highlights the significance of geospatial techniques where epidemiological data are limited, for improving understanding of HAT.   The study findings suggested HAT propagation in the study area was multidirectional, and that this may have been influenced by landscape characteristics.  The study also suggested that the study area could be regarded as highly hazardous and that the human population residing in the area could be said to be at moderate risk of HAT. The method employed in this study will facilitate efficient decision making, planning for resource allocation as well as support active HAT surveillance.

Keywords: Propagation, geospatially, multidirectional, disease,