modeling spatio-temporal malaria risk using remote sensing and environmental factors

Background: remote sensing have been intensively used across many disciplines, however, such infor-mation was limited in spatial epidemiology. methods: two years (2009 & 2010) landsat tm satellite data was used to develop vegetation, water bod-ies, air temperature and humidity criterion maps to model malaria risk and its spatiotemporal seasonal vari-ation. the criterion maps were used in weighted overlay analysis to generate final categorized malaria risk map. results: overall, 25%, 68%, 18% and 16% of the total area of rawalpindi region was categorized as danger zone for jun 2009, oct 2009, jan 2010 and jun 2010, respectively. the malaria risk reached at its peak during the monsoon season whereas air temperature and relative humidity were the main contributing factors in seasonal variation. conclusion: malaria risk maps could be used for prioritizing areas for malaria control measures.