MODELING OF MALARIA PREVALENCE IN INDONESIA WITH GEOGRAPHICALLY WEIGHTED REGRESSION
Journal Title: Kes Mas: Jurnal Fakultas Kesehatan Masyarakat - Year 2015, Vol 9, Issue 2
Abstract
Background: Malaria is a public health problem that can lead to death, especially in high-risk groups i.e. infants, toddlers and pregnant women. This disease is still endemic in most parts of Indonesia. The relation of location factor between regions with the surrounding region was assumed to give the effect of spatial variability in the prevalence of malaria in the region. It would lead to the prevalence of malaria modeling using classical regression methods become less precise due to the assumption of homogeneity of variance was not met. It could be overcome by Geographically Weighted Regression (GWR) modeling Methods: Conducting spatial correlation test between each adjacent observation error. Selecting explanatory variables to be included in the model with stepwise regression. Conducting spatial heterogeneity test on malaria prevalence data. Estimating the parameters of each GWR models and partial test parameters for each province by using a gaussian kernel weighting function and kernel weighting function bisquare. Choosing the best model between GWR models using gaussian kernel weighting function and bisquare kernel weighting function by using R2 and AIC. Results: GWR modeling is locally modeling therefore the value of the parameter estimators GWR models will be different for each research area .GWR models with gaussian kernel weighting has a R2 value of 87.82 and AIC value of 143.80 GWR models with bisquare kernel weighting have R2 value of 90.17% and AIC value of 137.81. Conclussion: GWR modeling using bisquare kernel weighting function on malaria prevalence data by province in Indonesia in 2013 provides better results than a gaussian kernel weighting function based on the value of R2 and AIC of both models.
Authors and Affiliations
Ita Miranti, Anik Djuraidah, Indahwati Indahwati
Keywords
Related Articles
- EP ID EP275592
- DOI 10.12928/kesmas.v9i2.2125
- Views 112
- Downloads 0
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