Ali Darwish1, Yasser Rahhal2, Assef Jafar2. 1. Department of Informatics, Higher Institute for Applied Sciences and Technology, Damascus, Syria. ali.darwish@hiast.edu.sy. 2. Department of Informatics, Higher Institute for Applied Sciences and Technology, Damascus, Syria.
Abstract
OBJECTIVE: An accurate forecasting of outbreaks of influenza-like illness (ILI) could support public health officials to suggest public health actions earlier. We investigated the performance of three different feature spaces in different models to forecast the weekly ILI rate in Syria using EWARS data from World Health Organization (WHO). Time series feature space was first used and we applied the seven models which are Naïve, Average, Seasonal naïve, drift, dynamic harmonic regression (Dhr), seasonal and trend decomposition using loess (STL) and TBATS. The Second feature space is like some state-of-the-art, which we named [Formula: see text] feature space. The third one, we proposed and named [Formula: see text] (YnWm) feature space. Machine learning (ML) and deep learning (DL) model were applied to the second and third feature spaces (generalized linear model (GLM), support vector regression (SVR), gradient boosting (GB), random forest (RF) and long short term memory (LSTM)). RESULTS: It was indicated that the LSTM model of four layers with [Formula: see text] feature space gave more accurate results than other models and reached the lowest MAPE of [Formula: see text] and the lowest RMSE of 0.01662. I hope that this modelling methodology can be applied in other countries and therefore help prevent and control influenza worldwide.
OBJECTIVE: An accurate forecasting of outbreaks of influenza-like illness (ILI) could support public health officials to suggest public health actions earlier. We investigated the performance of three different feature spaces in different models to forecast the weekly ILI rate in Syria using EWARS data from World Health Organization (WHO). Time series feature space was first used and we applied the seven models which are Naïve, Average, Seasonal naïve, drift, dynamic harmonic regression (Dhr), seasonal and trend decomposition using loess (STL) and TBATS. The Second feature space is like some state-of-the-art, which we named [Formula: see text] feature space. The third one, we proposed and named [Formula: see text] (YnWm) feature space. Machine learning (ML) and deep learning (DL) model were applied to the second and third feature spaces (generalized linear model (GLM), support vector regression (SVR), gradient boosting (GB), random forest (RF) and long short term memory (LSTM)). RESULTS: It was indicated that the LSTM model of four layers with [Formula: see text] feature space gave more accurate results than other models and reached the lowest MAPE of [Formula: see text] and the lowest RMSE of 0.01662. I hope that this modelling methodology can be applied in other countries and therefore help prevent and control influenza worldwide.
Entities:
Keywords:
Feature space; Influenza-like illness (ILI); Long short term memory (LSTM); Time series analysis
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