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

Nooshin Ayoobi; Danial Sharifrazi; Roohallah Alizadehsani; Afshin Shoeibi; Juan M. Gorriz; Hossein Moosaei; Abbas Khosravi; Saeid Nahavandi; Abdoulmohammad Gholamzadeh Chofreh; Feybi Ariani Goni; Jiří Jaromír Klemeš; Amir Mosavi

doi:10.1016/j.rinp.2021.104495

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

Nooshin Ayoobi
, Danial Sharifrazi
, Roohallah Alizadehsani
, Afshin Shoeibi
, Juan M. Gorriz
, Hossein Moosaei
, Abbas Khosravi
, Saeid Nahavandi
, Abdoulmohammad Gholamzadeh Chofreh^*
, Feybi Ariani Goni
, Jiří Jaromír Klemeš
, Amir Mosavi

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

102 Citations (SciVal)

Original language	English
Article number	104495
Journal	Results in Physics
Volume	27
DOIs	https://doi.org/10.1016/j.rinp.2021.104495
Publication status	Published (VoR) - Aug 2021

Funding

Several researchers benefited from the EU supported project Sustainable Process Integration Laboratory \u2014 SPIL funded as project No. CZ.02.1.01/0.0/0.0/15_003/0000456, by Czech Republic Operational Programme Research and Development, Education, Priority 1: Strengthening capacity for quality research, based on the SPIL project. This work was also partly supported by the Ministerio de Ciencia e Innovaci\u00F3n (Espa\u00F1a)/ FEDER under the RTI2018-098913-B100 project, by the Consejer\u00EDa de Econom\u00EDa, Innovaci\u00F3n, Ciencia y Empleo (Junta de Andaluc\u00EDa) and FEDER under CV20-45250 and A-TIC-080-UGR18 projects.

Funders	Funder number
Consejería de Economía, Innovación, Ciencia y Empleo, Junta de Andalucía
Ministerio de Ciencia e Innovación
European Commission
Sustainable Process Integration Laboratory
Sustainable Process Integration Laboratory – SPIL	CZ.02.1.01/0.0/0.0/15_003/0000456
Junta de Andalucía	A-TIC-080-UGR18, CV20-45250
European Regional Development Fund	RTI2018-098913-B100

Keywords

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

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10.1016/j.rinp.2021.104495

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Ayoobi, N., Sharifrazi, D., Alizadehsani, R., Shoeibi, A., Gorriz, J. M., Moosaei, H., Khosravi, A., Nahavandi, S., Gholamzadeh Chofreh, A., Goni, F. A., Klemeš, J. J., & Mosavi, A. (2021). Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods. Results in Physics, 27, Article 104495. https://doi.org/10.1016/j.rinp.2021.104495

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

author = "Nooshin Ayoobi and Danial Sharifrazi and Roohallah Alizadehsani and Afshin Shoeibi and Gorriz, \{Juan M.\} and Hossein Moosaei and Abbas Khosravi and Saeid Nahavandi and \{Gholamzadeh Chofreh\}, Abdoulmohammad and Goni, \{Feybi Ariani\} and Kleme{\v s}, \{Ji{\v r}{\'i} Jarom{\'i}r\} and Amir Mosavi",

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year = "2021",

month = aug,

doi = "10.1016/j.rinp.2021.104495",

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AU - Ayoobi, Nooshin

AU - Sharifrazi, Danial

AU - Alizadehsani, Roohallah

AU - Shoeibi, Afshin

AU - Gorriz, Juan M.

AU - Moosaei, Hossein

AU - Khosravi, Abbas

AU - Nahavandi, Saeid

AU - Gholamzadeh Chofreh, Abdoulmohammad

AU - Goni, Feybi Ariani

AU - Klemeš, Jiří Jaromír

AU - Mosavi, Amir

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KW - COVID-19 Prediction

KW - Deep learning

KW - Gated Recurrent Unit (GRU)

KW - Long Short Term Memory (LSTM)

KW - Machine learning

KW - New Cases of COVID-19

KW - New Deaths of COVID-19

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U2 - 10.1016/j.rinp.2021.104495

DO - 10.1016/j.rinp.2021.104495

M3 - Article

AN - SCOPUS:85119023371

SN - 2211-3797

VL - 27

JO - Results in Physics

JF - Results in Physics

M1 - 104495

ER -

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

Funding

Keywords

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