2013 Conference of the International Medical Geology Association (25–29 August 2013)

Paper No. 6
Presentation Time: 4:50 PM

MODELLING THE VULNERABILITY TO MALARIA AND CHOLERA IN THE CHENNAI REGION OF SOUTHERN INDIA USING REMOTE SENSING AND GIS AND DEVELOPMENT OF DECISION SUPPORT SYSTEM


SRINIVASA PERUMAL, Padma, Department of Geology, Anna University, Plot No. 1513 A, 9th cross street, Poompuhar Nagar, Kolathur, Chennai, 600099, India, SAKTHIVEL, Shanmuga Priyaa, Department of Geology, Anna University, Plot No 26, G2, Dwarak Villa, 7th cross street, Dandeeshwaram Nagar, Velachery, Chennai, 600042, India and KUMARI, Kavita, Flat 1E, Aarthi Apartment, 163, Jaganathan Salai, Periyar Nagar, Perambur, Chennai, 600082, India, padmagi91@gmail.com

Rapid population growth and the increase in makeshift settlements have increased human vulnerability to various man-made and natural hazards. Congestion, unsanitary conditions, poor disposal of liquid and solid waste, as well as inadequate public health facilities have resulted in the spread of water borne diseases and vector-borne diseases in urban areas.

The common link for these diseases is their relationship with water. Cholera, an infectious bacterial illness, can be acquired when sources of drinking water have been contaminated. Malaria is caused by a parasite which lay their larvae in still water. The World Health Organization estimates the following global annual impact as in 300 million malaria cases (WHO, 2009a) and has estimated that these diseases represent 17% of the global disease burden due to ­­all parasitic and infectious diseases recorded as disability-adjusted life years (Townson et al., 2005).

This work integrates various environmental and socio-economic parameters to generate the spatial pattern of Cholera (waterborne disease) and Malaria (vector borne disease) in an urban area like Chennai with the help of high resolution Remote Sensing data and Geographical Information System (GIS).

This vulnerability assessment suggests an effective monitoring of the causative factors in order to abate the spread of such epidemics. The spatial and aspatial data required for the study were obtained from remote sensing and ground-based study. Based on the spatial analysis, a Decision Support System (DSS) was designed to better enable resource allocation and management of the Health centres to tackle these epidemics.

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