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Open AccessArticle

COVID-19 Outbreak Prediction with Machine Learning

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Department of Biosystem Engineering, University of Mohaghegh Ardabili, Ardabil 5619911367, Iran
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School of Economics and Business, Norwegian University of Life Sciences, 1430 Ås, Norway
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Institute of Automation, Obuda University, 1034 Budapest, Hungary
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Department of Informatics, J. Selye University, 94501 Komarno, Slovakia
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Helmholtz-Zentrum Dresden-Rossendorf, Chemnitzer Str. 40, D-09599 Freiberg, Germany
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Department of Physics, Faculty of Science, the University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Belgium
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Department of Mathematics, J. Selye University, 94501 Komarno, Slovakia
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Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany
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Institute of Structural Mechanics, Bauhaus-Universität Weimar, 99423 Weimar, Germany
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Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UK
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Author to whom correspondence should be addressed.
Algorithms 2020, 13(10), 249; https://doi.org/10.3390/a13100249
Received: 8 September 2020 / Revised: 26 September 2020 / Accepted: 27 September 2020 / Published: 1 October 2020
(This article belongs to the Special Issue Feature Papers in Evolutionary Algorithms and Machine Learning)
Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and these models are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models need to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to susceptible–infected–recovered (SIR) and susceptible-exposed-infectious-removed (SEIR) models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP; and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior across nations, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. This paper further suggests that a genuine novelty in outbreak prediction can be realized by integrating machine learning and SEIR models. View Full-Text
Keywords: COVID-19; coronavirus disease; coronavirus; SARS-CoV-2; prediction; machine learning; coronavirus disease (COVID-19); deep learning; health informatics; severe acute respiratory syndrome coronavirus 2; supervised learning; outbreak prediction; pandemic; epidemic; forecasting; artificial intelligence; artificial neural networks COVID-19; coronavirus disease; coronavirus; SARS-CoV-2; prediction; machine learning; coronavirus disease (COVID-19); deep learning; health informatics; severe acute respiratory syndrome coronavirus 2; supervised learning; outbreak prediction; pandemic; epidemic; forecasting; artificial intelligence; artificial neural networks
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Ardabili, S.F.; Mosavi, A.; Ghamisi, P.; Ferdinand, F.; Varkonyi-Koczy, A.R.; Reuter, U.; Rabczuk, T.; Atkinson, P.M. COVID-19 Outbreak Prediction with Machine Learning. Algorithms 2020, 13, 249.

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