Assessment of a Remote Sensing-Based Model for Predicting Malaria Transmission Risk in Villages of Chiapas, Mexico

Louisa R. BeckJohnson Controls World Services, National Aeronautics and Space Administration, Ames Research Center, Moffett Field, Instituto Nacional de Salud Publica, Centro de Investigaciones Sobre Enfermedades Infecciosas, Centro de Investigacion de Paludismo, Ministry of Health, Tapachula, Department of Entomology, University of California, Uniformed Services University of the Health Sciences, California, Mexico

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Mario H. RodriguezJohnson Controls World Services, National Aeronautics and Space Administration, Ames Research Center, Moffett Field, Instituto Nacional de Salud Publica, Centro de Investigaciones Sobre Enfermedades Infecciosas, Centro de Investigacion de Paludismo, Ministry of Health, Tapachula, Department of Entomology, University of California, Uniformed Services University of the Health Sciences, California, Mexico

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Sheri W. DisterJohnson Controls World Services, National Aeronautics and Space Administration, Ames Research Center, Moffett Field, Instituto Nacional de Salud Publica, Centro de Investigaciones Sobre Enfermedades Infecciosas, Centro de Investigacion de Paludismo, Ministry of Health, Tapachula, Department of Entomology, University of California, Uniformed Services University of the Health Sciences, California, Mexico

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Americo D. RodriguezJohnson Controls World Services, National Aeronautics and Space Administration, Ames Research Center, Moffett Field, Instituto Nacional de Salud Publica, Centro de Investigaciones Sobre Enfermedades Infecciosas, Centro de Investigacion de Paludismo, Ministry of Health, Tapachula, Department of Entomology, University of California, Uniformed Services University of the Health Sciences, California, Mexico

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Robert K. WashinoJohnson Controls World Services, National Aeronautics and Space Administration, Ames Research Center, Moffett Field, Instituto Nacional de Salud Publica, Centro de Investigaciones Sobre Enfermedades Infecciosas, Centro de Investigacion de Paludismo, Ministry of Health, Tapachula, Department of Entomology, University of California, Uniformed Services University of the Health Sciences, California, Mexico

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Donald R. RobertsJohnson Controls World Services, National Aeronautics and Space Administration, Ames Research Center, Moffett Field, Instituto Nacional de Salud Publica, Centro de Investigaciones Sobre Enfermedades Infecciosas, Centro de Investigacion de Paludismo, Ministry of Health, Tapachula, Department of Entomology, University of California, Uniformed Services University of the Health Sciences, California, Mexico

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Michael A. SpannerJohnson Controls World Services, National Aeronautics and Space Administration, Ames Research Center, Moffett Field, Instituto Nacional de Salud Publica, Centro de Investigaciones Sobre Enfermedades Infecciosas, Centro de Investigacion de Paludismo, Ministry of Health, Tapachula, Department of Entomology, University of California, Uniformed Services University of the Health Sciences, California, Mexico

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A blind test of two remote sensing-based models for predicting adult populations of Anopheles albimanus in villages, an indicator of malaria transmission risk, was conducted in southern Chiapas, Mexico. One model was developed using a discriminant analysis approach, while the other was based on regression analysis. The models were developed in 1992 for an area around Tapachula, Chiapas, using Landsat Thematic Mapper (TM) satellite data and geographic information system functions. Using two remotely sensed landscape elements, the discriminant model was able to successfully distinguish between villages with high and low An. albimanus abundance with an overall accuracy of 90%. To test the predictive capability of the models, multitemporal TM data were used to generate a landscape map of the Huixtla area, northwest of Tapachula, where the models were used to predict risk for 40 villages. The resulting predictions were not disclosed until the end of the test. Independently, An. albimanus abundance data were collected in the 40 randomly selected villages for which the predictions had been made. These data were subsequently used to assess the models' accuracies. The discriminant model accurately predicted 79% of the highabundance villages and 50% of the low-abundance villages, for an overall accuracy of 70%. The regression model correctly identified seven of the 10 villages with the highest mosquito abundance. This test demonstrated that remote sensing-based models generated for one area can be used successfully in another, comparable area.

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