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Exponential smoothing on forecasting dengue cases in Colombo, Sri Lanka

Authors:

A. M. C. H. Attanayake ,

University of Kelaniya, LK
About A. M. C. H.
Department of Statistics & Computer Science, Faculty of Science
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S. S. N. Perera,

University of Colombo, LK
About S. S. N.
Research & Development Centre for Mathematical Modelling, Faculty of Science
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U. P. Liyanage

University of Kelaniya, LK
About U. P.
Department of Statistics & Computer Science, Faculty of Science
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Abstract

Prediction of number of people to be infected is an essential component in studying any leading diseases. Particularly it is important in dengue disease as it is the most critical mosquito-borne viral disease in the world. The number of reported dengue cases gradually increased all over the world as well as in Sri Lanka. In Sri Lanka, the majority of dengue cases reported in the Colombo district. The authors applied exponential smoothing technique in order to model and forecast dengue cases in Colombo, Sri Lanka. Data consist of monthly reported dengue cases in Colombo district from January 2010 to May 2019. January 2010 to February 2019 data used for model building and rest of the data used for model validation. Both original cases and log transformed cases considered for modelling and Holt Winters smoothing suits well with both cases. Best model in each case and finally the most parsimonious model within these two best models were selected by considering AIC, BIC, MAE, RMSE and MAPE measures. The most parsimonious model fits on log transformed dengue cases. Using the most parsimonious model predictions were made for June to August 2019. It can be concluded that the best model able to fit on the data in an adequate level and reported dengue will increase slowly during the prediction period.
DOI: https://doi.org/10.4038/jsc.v11i1.24
How to Cite: Attanayake, A.M.C.H., Perera, S.S.N. and Liyanage, U.P., 2020. Exponential smoothing on forecasting dengue cases in Colombo, Sri Lanka. Journal of Science, 11(1), pp.11–22. DOI: http://doi.org/10.4038/jsc.v11i1.24
Published on 27 Jul 2020.
Peer Reviewed

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