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Geographic Information Systems (GIS) and Remote Sensing (RS) are now generally accepted by the scientific community as major tools contributing to the understanding of the epidemiological chain sensu lato, i.e. interactions between pathogens, vectors, hosts and environment.

The Geographic Information System is defined as an information tool that is used to input, store, retrieve, analyze and output geo-referenced data, in order to support decision making for planning and management of land use, natural resources, environment and a wide range of facilities and administrative records.

Remote Sensing can be defined as the acquisition of information about an object without being in physical contact with it. The term is often used to indicate the sensing of the Earth's surface from space by making use of the properties of electromagnetic waves emitted, reflected or diffracted by the sensed objects.

With regard to tsetse-transmitted Trypanosomosis, area-wide knowledge of the different factors affecting the interactions between vectors, parasites and hosts is of paramount importance for a rational disease management. In this regard, GIS and RS are widely used to map in space and time the distribution of tsetse species, trypanosomes, cattle and several ecological variables which are susceptible to affect vectors, pathogens and parasites distribution. Spatial analysis has also proven very powerful in the identification and prioritisation of intervention areas and in the investigation and prediction of environmental implications of different control measures. Furthermore, when socio-economic data are integrated in a geographical environment, a deeper insight into the impact of the disease can be given. Spatial layers on cattle breeds and density, husbandry systems, tsetse or disease distribution are put together to estimate the potential economic benefits of Trypanosomosis removal from a given area. Consequently, priority areas for intervention can be pinpointed with the ultimate goal of optimizing the cost/benefit ratio.

The combined use of the above tools can help the construction of spatial decision-support systems for planning integrated disease control.

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