期刊名称：Journal of Geoscience and Environment Protection
印刷版ISSN：2327-4336
电子版ISSN：2327-4344
出版年度：2017
卷号：5
期号：5
页码：30-53
DOI：10.4236/gep.2017.55003
语种：English
出版社：Scientific Research Pub
摘要：Introduction: Despite serious interventions worldwide, malaria remains a significant cause of global morbidity and mortality. Malaria endemic zones are predominant in the poorest tropical regions of the world, especially in continental Africa and South-Asia. Major Indian population reside in malaria endemic zones which are tribal dominated and inaccessible. Lack of suitable data, reporting and medical facilities in malaria vulnerable regions handicaps the decision makers in taking adequate steps. Natural resources were mapped to establish their possible linkage with malaria incidence and to delineate malaria hotspots using geo-spatial tools. Methods: Remote sensing data along with various ancillary data such as socio-economic (population in general, child population, tribal population, literacy), epidemiology (Malaria API and Pf cases) and environmental parameters (wetness, forest cover, rainfall, aspect, elevation, slope, drainage buffer, and breeding sites) were integrated on GIS platform using a designed weight matrix. Multi criteria evaluation was done to generate hotspot for effective monitoring of malaria incidences. Results: Various thematic layers were utilized for integrated mapping, and the final map depicted 59.1% of the study area is vulnerable to high to very high risk of malaria occurrence. Manoharpur Administrative Block consisted of 89% of its area under high to very high probability of malaria incidence and it needs to be prioritized first for preventing epidemic outbreak. Various village pockets were revealed for prioritizing it for focused intervention of malaria control measures. Conclusions: Geospatial technology can be potentially used to map in the field of vector-borne diseases including malaria. The maps produced enable easy update of information both spatially and temporally provide effortless accessibility of geo-referenced data to the policy makers to produce cost-effective measures for malaria control in the endemic regions.
