Forecasting the number of dengue fever based on weather conditions using ensemble forecasting method

Mursyidatun Nabilah, Raras Tyasnurita, Faizal Mahananto, Wiwik Anggraeni, Retno Aulia Vinarti, Ahmad Muklason


Dengue fever is still a crucial public health problem in Indonesia, with the highest case fatality rate (CFR) is 1.01% in East Java, Malang Regency. One of the solutions to control the death rate and cases is to forecast the cases number. This study proposed ensemble forecasting that build from several penalized regressions. Penalized regressions are able to overcome linear regression analysis’ shortcomings by using penalty values, that will affect regression’s coefficient, resulting on regression model with a slight bias in order to reduce parameter estimations and prediction values' variances. Penalized regressions are evaluated and built as ensemble forecasting method to minimize the shortcomings of other existing model, so it could produce more accurate values comparing to single penalized regression model. The result showed that the ensemble model `consists of smoothly clipped absolute deviation (SCAD) and Elastic-Net is sufficient to capture data patterns with root mean squared error (RMSE) 6.38. 


Dengue fever; Dengue forecast; Ensemble forecasting;

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IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN/e-ISSN 2089-4872/2252-8938 
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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