{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T16:51:07Z","timestamp":1781715067026,"version":"3.54.5"},"reference-count":98,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T00:00:00Z","timestamp":1601510400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>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\u2013infected\u2013recovered (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.<\/jats:p>","DOI":"10.3390\/a13100249","type":"journal-article","created":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T09:04:12Z","timestamp":1601543052000},"page":"249","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":238,"title":["COVID-19 Outbreak Prediction with Machine Learning"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7744-7906","authenticated-orcid":false,"given":"Sina","family":"Ardabili","sequence":"first","affiliation":[{"name":"Department of Biosystem Engineering, University of Mohaghegh Ardabili, Ardabil 5619911367, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4842-0613","authenticated-orcid":false,"given":"Amir","family":"Mosavi","sequence":"additional","affiliation":[{"name":"School of Economics and Business, Norwegian University of Life Sciences, 1430 \u00c5s, Norway"},{"name":"Institute of Automation, Obuda University, 1034 Budapest, Hungary"},{"name":"Department of Informatics, J. Selye University, 94501 Komarno, Slovakia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1203-741X","authenticated-orcid":false,"given":"Pedram","family":"Ghamisi","sequence":"additional","affiliation":[{"name":"Helmholtz-Zentrum Dresden-Rossendorf, Chemnitzer Str. 40, D-09599 Freiberg, Germany"},{"name":"Department of Physics, Faculty of Science, the University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Filip","family":"Ferdinand","sequence":"additional","affiliation":[{"name":"Department of Mathematics, J. Selye University, 94501 Komarno, Slovakia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6932-8608","authenticated-orcid":false,"given":"Annamaria","family":"Varkonyi-Koczy","sequence":"additional","affiliation":[{"name":"Institute of Automation, Obuda University, 1034 Budapest, Hungary"},{"name":"Department of Informatics, J. 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