COVID-19 Outbreak Prediction with 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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/13/10/249 |
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| author | Sina F. Ardabili Amir Mosavi Pedram Ghamisi Filip Ferdinand Annamaria R. Varkonyi-Koczy Uwe Reuter Timon Rabczuk Peter M. Atkinson |
| author_facet | Sina F. Ardabili Amir Mosavi Pedram Ghamisi Filip Ferdinand Annamaria R. Varkonyi-Koczy Uwe Reuter Timon Rabczuk Peter M. Atkinson |
| author_sort | Sina F. Ardabili |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T15:53:37Z |
| format | Article |
| id | doaj.art-208772302fe046fb8687994cb96b1385 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-10T15:53:37Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-208772302fe046fb8687994cb96b13852023-11-20T15:49:00ZengMDPI AGAlgorithms1999-48932020-10-01131024910.3390/a13100249COVID-19 Outbreak Prediction with Machine LearningSina F. Ardabili0Amir Mosavi1Pedram Ghamisi2Filip Ferdinand3Annamaria R. Varkonyi-Koczy4Uwe Reuter5Timon Rabczuk6Peter M. Atkinson7Department of Biosystem Engineering, University of Mohaghegh Ardabili, Ardabil 5619911367, IranSchool of Economics and Business, Norwegian University of Life Sciences, 1430 Ås, NorwayHelmholtz-Zentrum Dresden-Rossendorf, Chemnitzer Str. 40, D-09599 Freiberg, GermanyDepartment of Mathematics, J. Selye University, 94501 Komarno, SlovakiaInstitute of Automation, Obuda University, 1034 Budapest, HungaryFaculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, GermanyInstitute of Structural Mechanics, Bauhaus-Universität Weimar, 99423 Weimar, GermanyLancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UKSeveral 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.https://www.mdpi.com/1999-4893/13/10/249COVID-19coronavirus diseasecoronavirusSARS-CoV-2predictionmachine learning |
| spellingShingle | Sina F. Ardabili Amir Mosavi Pedram Ghamisi Filip Ferdinand Annamaria R. Varkonyi-Koczy Uwe Reuter Timon Rabczuk Peter M. Atkinson COVID-19 Outbreak Prediction with Machine Learning Algorithms COVID-19 coronavirus disease coronavirus SARS-CoV-2 prediction machine learning |
| title | COVID-19 Outbreak Prediction with Machine Learning |
| title_full | COVID-19 Outbreak Prediction with Machine Learning |
| title_fullStr | COVID-19 Outbreak Prediction with Machine Learning |
| title_full_unstemmed | COVID-19 Outbreak Prediction with Machine Learning |
| title_short | COVID-19 Outbreak Prediction with Machine Learning |
| title_sort | covid 19 outbreak prediction with machine learning |
| topic | COVID-19 coronavirus disease coronavirus SARS-CoV-2 prediction machine learning |
| url | https://www.mdpi.com/1999-4893/13/10/249 |
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