Publication detail

Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods

Ayoobi, N., Sharifrazi, D., Alizadehsani, R., Shoeibi, A., Gorriz, J.M., Moosaei, H., Khosravi, A., Nahavandi, S., Chofreh, A.G., Goni, F.A., Klemeš, J.J., Mosavi, A.

Original Title

Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods

Type

journal article in Web of Science

Language

English

Original 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.

Keywords

Long Short Term Memory (LSTM); Convolutional Long Short Term Memory (Conv-LSTM); Gated Recurrent Unit (GRU); Bidirectional New Cases of COVID-19; New Deaths of COVID-19; COVID-19 Prediction; Deep learning; Machine learning

Authors

Ayoobi, N., Sharifrazi, D., Alizadehsani, R., Shoeibi, A., Gorriz, J.M., Moosaei, H., Khosravi, A., Nahavandi, S., Chofreh, A.G., Goni, F.A., Klemeš, J.J., Mosavi, A.

Released

1. 8. 2021

Publisher

Elsevier Ltd.

Location

ELSEVIER, RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS

ISBN

2211-3797

Periodical

Results in Physics

Number

27

State

Kingdom of the Netherlands

Pages from

104495

Pages to

104495

Pages count

15

URL

https://www.sciencedirect.com/science/article/pii/S2211379721006069

BibTex

@article{BUT172205,
  author="Abdoulmohammad {Gholamzadeh Chofreh} and Feybi Ariani {Goni} and Jiří {Klemeš}",
  title="Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods",
  journal="Results in Physics",
  year="2021",
  number="27",
  pages="104495--104495",
  doi="10.1016/j.rinp.2021.104495",
  issn="2211-3797",
  url="https://www.sciencedirect.com/science/article/pii/S2211379721006069"
}