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Malar J. 2015 May 7;14:192. doi: 10.1186/s12936-015-0685-4.

Socio-economic, epidemiological and geographic features based on GIS-integrated mapping to identify malarial hotspots.

Author information

1
Centre for Biology & Bioinformatics, School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi, India. qayum.iitk@gmail.com.
2
Indira Gandhi National Forest Academy, Dehradun, India. qayum.iitk@gmail.com.
3
Centre for the Study of Regional Development, Jawaharlal Nehru University, New Delhi, India. aryarakesh.mnu@gmail.com.
4
Nepalganj Medical College, Banke, Nepal. pawanchaudhry16ngmc@gmail.com.
5
Centre for Biology & Bioinformatics, School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi, India. andrew@jnu.ac.in.

Abstract

BACKGROUND:

Malaria is a major health problem in the tropical and subtropical world. In India, 95% of the population resides in malaria endemic regions and it is major public health problem in most parts of the country. The present work has developed malaria maps by integrating socio-economic, epidemiology and geographical dimensions of three eastern districts of Uttar Pradesh, India. The area has been studied in each dimension separately, and later integrated to find a list of vulnerable pockets/villages, called as malarial hotspots.

METHODS:

The study has been done at village level. Seasonal variation of malaria, comparison of epidemiology indices and progress of the medical facility were studied. Ten independent geographical information system (GIS) maps of socio-economic aspects (population, child population, literacy, and work force participation), epidemiology (annual parasitic index (API) and slides collected and examined) and geographical features (settlement, forest cover, water bodies, rainfall, relative humidity, and temperature) were drawn and studied. These maps were overlaid based on computed weight matrix to find malarial hotspot.

RESULTS:

It was found that the studied dimensions were inter-weaving factors for malaria epidemic and closely affected malaria situations as evidenced from the obtained correlation matrix. The regions with water logging, high rainfall and proximity to forest, along with poor socio-economic conditions, are primarily hotspot regions. The work is presented through a series of GIS maps, tables, figures and graphs. A total of 2,054 out of 8,973 villages studied were found to be malarial hotspots and consequently suggestions were made to the concerned government malaria offices.

CONCLUSION:

With developing technology, information tools such as GIS, have captured almost every field of scientific research especially of vector-borne diseases, such as malaria. Malarial mapping enables easy update of information and effortless accessibility of geo-referenced data to policy makers to produce cost-effective measures for malaria control in endemic regions.

PMID:
25947349
PMCID:
PMC4435919
DOI:
10.1186/s12936-015-0685-4
[Indexed for MEDLINE]
Free PMC Article

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