Malaria is still the major parasitic disease in the world, with
approximately 438,000 deaths in？2015. Environmental risk factors (ERF) have been widely studied, however,
there are discrepancies in the results about their influence on malaria
transmission. Recently, papers have been published about geospatial analysis of
ERF of malaria to explain why malaria varies from place to？place. Our
primary objective was to identify the environmental variables most used in the
geospatial analysis of malaria transmission. The secondary objective was to
identify the geo-analytic？methods and techniques, as well as geo-analytic
statistics commonly related to ERF and malaria.？We conducted a systematized
review of articles published from January 2004 to March 2015,？within Web
of Science, Pubmed and LILACS databases. Initially 676 articles were found,
after inclusion and exclusion criteria, 29 manuscripts were selected.
Temperature, land use and land？cover, surface moisture and vector breeding
site were the most frequent included variables. As for？geo-analytic
methods, geostatistical models with Bayesian framework were the most
applied.？Kriging interpolations, Geographical Weighted Regression as well
as Kulldorff’s spatial scan were？the techniques more widely used. The main
objective of many of these studies was to use these methods and techniques to
create malaria risk maps. Spatial analysis performed with satellite？images
and georeferenced data are increasing in relevance due to the use of remote
sensing and Geographic Information System. The combination of these new
technologies identifies ERF more accurately, and the use of Bayesian
geostatistical models allows a wide diffusion of malaria risk maps. It is known
that temperature, humidity vegetation and vector breeding site play a critical
role in malaria transmission; however, other environmental risk factors have
also been identified.？Risk maps have a tremendous potential to enhance the
effectiveness of malaria-control programs.
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