δείτε την πρωτότυπη σελίδα τεκμηρίου στον ιστότοπο του αποθετηρίου του φορέα για περισσότερες πληροφορίες και για να δείτε όλα τα ψηφιακά αρχεία του τεκμηρίου*
Application of Multiple Linear Regression Model through GIS and Remote Sensing for Malaria Mapping in Varanasi District, INDIA
(EN)
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)
Health Science Journal
(EN)
Ιατρική και Επιστήμες Υγείας ▶ Επιστήμες Υγείας Νοσηλευτική
(EL)
*Η εύρυθμη και αδιάλειπτη λειτουργία των διαδικτυακών διευθύνσεων των συλλογών (ψηφιακό αρχείο, καρτέλα τεκμηρίου στο αποθετήριο) είναι αποκλειστική ευθύνη των αντίστοιχων Φορέων περιεχομένου.
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