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GIS in Decision support of control of Vector Borne Disease in India

By Dr. Aruna Srivastava, Director-National Institute of Malaria Research

 

The journey (1992 – 2009)

1994  – Mapping of malaria receptivity using GIS and identification of malaria determinants
1996  – Estimation of larval production using Remote Sensing images
1998 – GIS based malaria surveillance system (urban + rural)
1998 – 2002 –  2003 – Predictive habitat modeling for Indian anophelines

2006 & 2007 – State/ district/ village wise mapping for focused malaria control
2006 & 2007 – GIS for Dengue control in Delhi
2008  – GIS to map malaria safe areas, Assam
2009 – GIS  to facilitate distribution of bed nets
2009 – Identification of malaria Hot Spots for Focused Intervention in Tribal State of India, M. P.
2010 & 2011 –  Mapping of malaria receptivity using RS & GIS, Jharkhand

2010 & 2011 –  Deforestation and its impact on malaria epidemiology – RS & GIS based case study, Assam
2011 – GIS Mapping of Spatio-temporal trends of Malaria (1995-2008) to identify endemic and epidemic prone areas for decision support in malaria control in India
2012 …– Ecological succession of vectors
2010…2016 – Health Impact Assessment of Narmada Basin – GIS based approach

•GIS based malaria surveillance system was implemented in Dindigul municipality on November 19th 1999, ‘The World GIS Day’
•Health officers from the district and state head quarters Tamil Nadu were trained for its proper utilisation
•A Website URL www.malaria-tn.org was constructed for fast dissemination of information
•Info was entered at district level and revised maps were instantly available at State and Centre

GIS database for Vaccine Site Selection at Sundergargh

•Total 13 Villages with about 4000 population
•Eight forest villages with about 2000 population
•Five villages in plain area with about 2000 population
•A three tier database, village, house and personal level database

Advantages

•Instant retrieval of information
•dynamic maps, pinpointing areas requiring immediate attention
•web hosting eliminates the need for traditional flow of info
•universal accessibility of info
•once the infrastructure is ready can be used to build DSS for any other disease

Predictive habitat modeling of Indian Anophelines

Major Malaria Vectors

•An. sundaicus – a coastal species
•An. dirus – a forest species
•An. minimus – species of forest fringe areas
•An. fluviatilis – species of foothill areas
•An. culicifacies – a rural vector
•An. stephensi – an urban vector

Challenges

After even so many years GIS has not been geared up in Health
The projects are disease based and time based, not continuous – spatial data  has to be generated for each application and resource sharing is limited

No continuous input of data and analysis
Projects are initiated based on interest of the Organization
Limitation in distribution of maps, models, poor data security and integrity.
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