Application of Multiple Linear Regression Model through GIS and Remote Sensing for Malaria Mapping in Varanasi District, INDIA

dc.contributor.author	Kumar Ra, Praveen	en
dc.contributor.author	Nathawat, Mahendra Singh	en
dc.contributor.author	Onagh, Mohhamad	en
dc.date.accessioned	2014-07-20T08:54:47Z
dc.date.available	2014-07-20T08:54:47Z
dc.date.issued	2014-07-20
dc.identifier.uri	http://hdl.handle.net/11400/1449
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	www.hsj.gr	en
dc.subject	Geographic information systems
dc.subject	Geology--Remote sensing
dc.subject	Hindu pilgrims and pilgrimages--India--VÄ rÄ nasi (Uttar Pradesh)
dc.subject	Ελονοσία
dc.subject	Multivariate linear regression
dc.subject	Malaria susceptibility model
dc.title	Application of Multiple Linear Regression Model through GIS and Remote Sensing for Malaria Mapping in Varanasi District, INDIA	en
heal.type	journalArticle
heal.classification	Medicine
heal.classification	Human geography
heal.classification	Ιατρική
heal.classification	Γεωγραφία
heal.classification	Ινδία
heal.classification	India
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85005570
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Γεωγραφία
heal.classificationURI	N/A-Ινδία
heal.classificationURI	N/A-India
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh90001880
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh2008117962
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh2009126351
heal.language	en
heal.access	free
heal.recordProvider	Τεχνολογικό Εκπαιδευτικό Ίδρυμα Αθήνας.Σχολή Επαγγελμάτων Υγείας και Πρόνοιας. Τμήμα Νοσηλευτικής	el
heal.publicationDate	2012-12
heal.bibliographicCitation	Kumar Ra, P., Nathawat, M. & Onagh, M. (December 2012). Application of Multiple Linear Regression Model through GIS and Remote Sensing for Malaria Mapping in Varanasi District, INDIA. “Health Science Journal”. 6(4):731-749.	en
heal.abstract	Background: The production of malaria maps relies on modeling to predict the risk for most of the area, with actual observations of malaria prevalence usually only known at a limited number of specific locations. However, the estimation is complicated by the fact that there is often local variation of risk that cannot easily be accounted for by the known variables. An attempt has to be made for Varanasi district to evaluate status of Malaria disease and to develop a model, by which malaria prone zones were predicted by five classes of relative malaria susceptibility i.e. Very Low, Low, Moderate, High, and Very High categories. Methodology: Multiple Linear regression models were built for malaria cases reported in study area, as the dependent variable and various time based groupings of average temperature, rainfall and NDVI data as the independent variables. GIS is be used to investigate associations between such variables and the distribution of the different species responsible for malaria transmission. Accurate prediction of risk is dependent on knowledge of a number of variables i.e Land Use, NDVI, climatic factors, distance to location of existing government health centers, population, distance to ponds, streams and roads etc. that are related to malaria transmission. Climatic factors, particularly rainfall, temperature and relative humidity are known to have a strong influence on the biology of mosquitoes. To produce malaria susceptibility map in this method, the amounts of quantitative and qualitative variables based on sampling of 50×50 networks in form of a 38622×9 matrix have been transferred from GIS software (ILWIS 3.4 and ARC GIS-9.3) into statistical software (SPSS). Results: Percentage of malaria area is very much related to distance to health facilities. It is found that, 4.77% of malaria area is belonging to 0-1000 m buffer distance to health facilities and 24.10% of malaria area comes in 6000-10000 m buffer distance. As the distance to health facilities increases, malaria area is also increasing.	en
heal.publisher	Μαρβάκη, Χριστίνα	el
heal.journalName	Health Science Journal	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true
heal.dateCreated	2012-10