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Ayoobi N, Sharifrazi D, Alizadehsani R, Shoeibi A, Gorriz JM, Moosaei H, Khosravi A, Nahavandi S, Gholamzadeh Chofreh A, Goni FA, Klemeš JJ, Mosavi A. Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods. Results Phys 2021; 27:104495. [PMID: 34221854 PMCID: PMC8233414 DOI: 10.1016/j.rinp.2021.104495] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 06/19/2021] [Accepted: 06/22/2021] [Indexed: 05/17/2023]
Abstract
The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019. It has spread worldwide, leading to an ongoing pandemic, imposed restrictions and costs to many countries. Predicting the number of new cases and deaths during this period can be a useful step in predicting the costs and facilities required in the future. The purpose of this study is to predict new cases and deaths rate one, three and seven-day ahead during the next 100 days. The motivation for predicting every n days (instead of just every day) is the investigation of the possibility of computational cost reduction and still achieving reasonable performance. Such a scenario may be encountered in real-time forecasting of time series. Six different deep learning methods are examined on the data adopted from the WHO website. Three methods are LSTM, Convolutional LSTM, and GRU. The bidirectional extension is then considered for each method to forecast the rate of new cases and new deaths in Australia and Iran countries. This study is novel as it carries out a comprehensive evaluation of the aforementioned three deep learning methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate time series. To the best of our knowledge, this is the first time that Bi-GRU and Bi-Conv-LSTM models are used for prediction on COVID-19 new cases and new deaths time series. The evaluation of the methods is presented in the form of graphs and Friedman statistical test. The results show that the bidirectional models have lower errors than other models. A several error evaluation metrics are presented to compare all models, and finally, the superiority of bidirectional methods is determined. This research could be useful for organisations working against COVID-19 and determining their long-term plans.
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Key Words
- ANFIS, Adaptive Network-based Fuzzy Inference System
- ANN, Artificial Neural Network
- AU, Australia
- Bi-Conv-LSTM, Bidirectional Convolutional Long Short Term Memory
- Bi-GRU, Bidirectional Gated Recurrent Unit
- Bi-LSTM, Bidirectional Long Short-Term Memory
- Bidirectional
- COVID-19 Prediction
- COVID-19, Coronavirus Disease 2019
- Conv-LSTM, Convolutional Long Short Term Memory
- Convolutional Long Short Term Memory (Conv-LSTM)
- DL, Deep Learning
- DLSTM, Delayed Long Short-Term Memory
- Deep learning
- EMRO, Eastern Mediterranean Regional Office
- ES, Exponential Smoothing
- EV, Explained Variance
- GRU, Gated Recurrent Unit
- Gated Recurrent Unit (GRU)
- IR, Iran
- LR, Linear Regression
- LSTM, Long Short-Term Memory
- Lasso, Least Absolute Shrinkage and Selection Operator
- Long Short Term Memory (LSTM)
- MAE, Mean Absolute Error
- MAPE, Mean Absolute Percentage Error
- MERS, Middle East Respiratory Syndrome
- ML, Machine Learning
- MLP-ICA, Multi-layered Perceptron-Imperialist Competitive Calculation
- MSE, Mean Square Error
- MSLE, Mean Squared Log Error
- Machine learning
- New Cases of COVID-19
- New Deaths of COVID-19
- PRISMA, Preferred Reporting Items for Precise Surveys and Meta-Analyses
- RMSE, Root Mean Square Error
- RMSLE, Root Mean Squared Log Error
- RNN, Repetitive Neural Network
- ReLU, Rectified Linear Unit
- SARS, Serious Intense Respiratory Disorder
- SARS-COV, SARS coronavirus
- SARS-COV-2, Serious Intense Respiratory Disorder Coronavirus 2
- SVM, Support Vector Machine
- VAE, Variational Auto Encoder
- WHO, World Health Organization
- WPRO, Western Pacific Regional Office
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Affiliation(s)
- Nooshin Ayoobi
- Department of Mathematics, Savitribai Phule Pune University, Pune 411007, India
| | - Danial Sharifrazi
- Department of Computer Engineering, School of Technical and Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia
| | - Afshin Shoeibi
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran
- Faculty of Electrical and Computer Engineering, Biomedical Data Acquisition Lab, K. N. Toosi University of Technology, Tehran, Iran
| | - Juan M Gorriz
- Department of Signal Theory, Networking and Communications, Universidad de Granada, Spain
| | - Hossein Moosaei
- Department of Mathematics, Faculty of Science, University of Bojnord, Iran
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia
| | - Abdoulmohammad Gholamzadeh Chofreh
- Sustainable Process Integration Laboratory - SPIL, NETME Centre, Faculty of Mechanical Engineering, Brno University of Technology - VUT Brno, Technická 2896/2, 616 69 Brno, Czech Republic
| | - Feybi Ariani Goni
- Department of Management, Faculty of Business and Management, Brno University of Technology - VUT Brno, Kolejní 2906/4, 612 00 Brno, Czech Republic
| | - Jiří Jaromír Klemeš
- Sustainable Process Integration Laboratory - SPIL, NETME Centre, Faculty of Mechanical Engineering, Brno University of Technology - VUT Brno, Technická 2896/2, 616 69 Brno, Czech Republic
| | - Amir Mosavi
- John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary
- School of Economics and Business, Norwegian University of Life Sciences, 1430 Ås, Norway
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