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      Habitat suitability mapping of Anopheles darlingi in the surroundings of the Manso hydropower plant reservoir, Mato Grosso, Central Brazil

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          Abstract

          Background

          Hydropower plants provide more than 78 % of Brazil's electricity generation, but the country's reservoirs are potential new habitats for main vectors of malaria. In a case study in the surroundings of the Manso hydropower plant in Mato Grosso state, Central Brazil, habitat suitability of Anopheles darlingi was studied. Habitat profile was characterized by collecting environmental data. Remote sensing and GIS techniques were applied to extract additional spatial layers of land use, distance maps, and relief characteristics for spatial model building.

          Results

          Logistic regression analysis and ROC curves indicate significant relationships between the environment and presence of An. darlingi. Probabilities of presence strongly vary as a function of land cover and distance from the lake shoreline. Vector presence was associated with spatial proximity to reservoir and semi-deciduous forests followed by Cerrado woodland. Vector absence was associated with open vegetation formations such as grasslands and agricultural areas. We suppose that non-significant differences of vector incidences between rainy and dry seasons are associated with the availability of anthropogenic breeding habitat of the reservoir throughout the year.

          Conclusion

          Satellite image classification and multitemporal shoreline simulations through DEM-based GIS-analyses consist in a valuable tool for spatial modeling of A. darlingi habitats in the studied hydropower reservoir area. Vector presence is significantly increased in forested areas near reservoirs in bays protected from wind and wave action. Construction of new reservoirs under the tropical, sub-humid climatic conditions should therefore be accompanied by entomologic studies to predict the risk of malaria epidemics.

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          Most cited references38

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          A new procedure for gridding elevation and stream line data with automatic removal of spurious pits

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            Satellite imagery in the study and forecast of malaria.

            More than 30 years ago, human beings looked back from the Moon to see the magnificent spectacle of Earth-rise. The technology that put us into space has since been used to assess the damage we are doing to our natural environment and is now being harnessed to monitor and predict diseases through space and time. Satellite sensor data promise the development of early-warning systems for diseases such as malaria, which kills between 1 and 2 million people each year.
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              Malaria risk on the Amazon frontier.

              Frontier malaria is a biological, ecological, and sociodemographic phenomenon operating over time at three spatial scales (micro/individual, community, and state and national). We explicate these linkages by integrating data from remote sensing surveys, ground-level surveys and ethnographic appraisal, focusing on the Machadinho settlement project in Rondônia, Brazil. Spatially explicit analyses reveal that the early stages of frontier settlement are dominated by environmental risks, consequential to ecosystem transformations that promote larval habitats of Anopheles darlingi. With the advance of forest clearance and the establishment of agriculture, ranching, and urban development, malaria transmission is substantially reduced, and risks of new infection are largely driven by human behavioral factors. Malaria mitigation strategies for frontier settlements require a combination of preventive and curative methods and close collaboration between the health and agricultural sectors. Of fundamental importance is matching the agricultural potential of specific plots to the economic and technical capacities of new migrants. Equally important is providing an effective agricultural extension service.
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                Author and article information

                Journal
                Int J Health Geogr
                International Journal of Health Geographics
                BioMed Central (London )
                1476-072X
                2007
                7 March 2007
                : 6
                : 7
                Affiliations
                [1 ]Department of Geography, Federal University of Mato Grosso, Av. F. Corrêa, Cuiabá, Brazil
                [2 ]Institute of Biology, Federal University of Mato Grosso, Av. F. Corrêa, Cuiabá, Brazil
                [3 ]Institute of Public Health, Federal University of Mato Grosso, Av. F. Corrêa, Cuiabá, Brazil
                Article
                1476-072X-6-7
                10.1186/1476-072X-6-7
                1851006
                17343728
                33035ba0-75da-48a4-ac91-e026c709eff3
                Copyright © 2007 Zeilhofer et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 26 July 2006
                : 7 March 2007
                Categories
                Research

                Public health
                Public health

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