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 trypanosomiasis, 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 trypanosomiasis 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. | 
