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Malaria Modeling using Remote Sensing and GIS TechnologiesMalaria has been with the human race since the ancient time. In spite of the advances of biomedical research and the completion of genomic mapping of Plasmodium falciparum, the exact mechanisms of how the various strains of parasites evade the human immune system and how they have adapted and become resistant to multiple drugs remain elusive. Perhaps because of these reasons, effective vaccines against malaria are still not available. Worldwide, approximately one to three millions deaths are attributed to malaria annually. With the increased availability of remotely sensed data, researchers in medical entomology, epidemiology and ecology have started to associate environmental and ecological variables with malaria transmission. In several studies, it has been shown that transmission correlates well with certain environmental and ecological parameters, and that remote sensing can be used to measure these determinants. In a NASA project, we have taken a holistic approach to examine how remote sensing and GIs can contribute to vector and malaria controls. To gain a better understanding of the interactions among the possible promoting factors, we have been developing a habitat model, a transmission model, and a risk prediction model, all using remote sensing data as input. Our objectives are: 1) To identify the potential breeding sites of major vector species and the locations for larvicide and insecticide applications in order to reduce costs, lessen the chance of developing pesticide resistance, and minimize the damage to the environment; 2) To develop a malaria transmission model characterizing the interactions among hosts, vectors, parasites, landcover and environment in order to identify the key factors that sustain or intensify malaria transmission, and 3) To develop a risk model to predict the occurrence of malaria and its transmission intensity using epidemiological data and satellite-derived or ground-measured environmental and meteorological data.
Document ID
20040171207
Acquisition Source
Goddard Space Flight Center
Document Type
Conference Paper
Authors
Kiang, Richard
(NASA Goddard Space Flight Center Greenbelt, MD, United States)
Date Acquired
August 22, 2013
Publication Date
January 1, 2004
Subject Category
Earth Resources And Remote Sensing
Meeting Information
Meeting: Remote Sensing, GIS and Infectious Diseases Workshop
Location: Bangkok
Country: Thailand
Start Date: June 14, 2004
End Date: June 15, 2004
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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