ArticleBMC research notes2020
A comparative study on predicting influenza outbreaks using different feature spaces: application of influenza-like illness data from Early Warning Alert and Response System in Syria.
Article in BMC research notes, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Analysis of influenza-like illness trends in Saudi Arabia: a comparative study of statistical and deep learning techniques.Osong public health and research perspectives · 2025Article
- Comparison of Three Influenza Surveillance Data Sources for Timely Detection of Epidemic Onset - Chengdu City, Sichuan Province and Beijing Municipality, China, 2017-2023.China CDC weekly · 2024Article
- Performance of discrete wavelet transform-based method in the detection of influenza outbreaks in Iran: An ecological study.Health science reports · 2023Article
- Machine learning-based diffusion model for prediction of coronavirus-19 outbreak.Neural computing & applications · 2023Article
- Predicting infectious disease for biopreparedness and response: A systematic review of machine learning and deep learning approaches.One health (Amsterdam, Netherlands) · 2022Review
- Simulation of the COVID-19 patient flow and investigation of the future patient arrival using a time-series prediction model: a real-case study.Medical & biological engineering & computing · 2022Article
- Could Emergency Diseases Surveillance Systems Be Transitioned to Routine Surveillance Systems? A Proposed Transition Strategy for Early Warning, Alert, and Response Network.Frontiers in medicine · 2022Review
- Estimating COVID-19Machine learning with applications · 2021Article
- A Bidirectional Long Short-Term Memory Model Algorithm for Predicting COVID-19 in Gulf Countries.Life (Basel, Switzerland) · 2021Article
- Deep learning model for forecasting COVID-19 outbreak in Egypt.Process safety and environmental protection : transactions of the Institution of Chemical Engineers, Part B · 2021Article
- A novel data-driven methodology for influenza outbreak detection and prediction.Scientific reports · 2021Article
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3 authors.
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Abstract
objectiveAn 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)).
resultsIt 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.
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