COVID-19 Outbreak Prediction with Machine Learning

39 Pages Posted: 22 Apr 2020

See all articles by Sina F. Ardabili

Sina F. Ardabili

Thuringian Institute of Sustainability and Climate Protection

Amir Mosavi

TU Dresden; Obuda University

Pedram Ghamisi

Helmholtz-Zentrum Dresden-Rossendorf

Filip Ferdinand

J. Selye University

Annamaria R. Varkonyi-Koczy

J. Selye University

Uwe Reuter

Dresden University of Technology

Timon Rabczuk

Bauhaus University - Institute of Structural Mechanics

Peter M. Atkinson

Lancaster University

Date Written: April 19, 2020

Abstract

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 they 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 needs 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 SIR and 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 from nation-to-nation, 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. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

Note: Funding: This research is supported within the project of “Support of research and development activities of the J. Selye University in the field of Digital Slovakia and creative industry” of the Research and Innovation Operational Programme (ITMS code: NFP313010T504) co-funded by the European Regional Development Fund.

Conflict of Interest: The authors declare no conflict of interest.

Keywords: COVID-19; Coronavirus disease; Coronavirus; SARS-CoV-2; model; prediction; machine learning

Suggested Citation

Ardabili, Sina F. and Mosavi, Amir and Ghamisi, Pedram and Ferdinand, Filip and Varkonyi-Koczy, Annamaria R. and Reuter, Uwe and Rabczuk, Timon and Atkinson, Peter M., COVID-19 Outbreak Prediction with Machine Learning (April 19, 2020). Available at SSRN: https://ssrn.com/abstract=3580188 or http://dx.doi.org/10.2139/ssrn.3580188

Sina F. Ardabili

Thuringian Institute of Sustainability and Climate Protection ( email )

Jena, 07743
Germany

Amir Mosavi (Contact Author)

TU Dresden ( email )

Münchner Platz 2 - 3
Dresden, 01069
Germany

Obuda University ( email )

Bécsi út 96/B
Budapest, 034
Hungary

Pedram Ghamisi

Helmholtz-Zentrum Dresden-Rossendorf ( email )

Bautzner Landstraße 400
Dresden, 01328
Germany

Filip Ferdinand

J. Selye University ( email )

Slovakia

Annamaria R. Varkonyi-Koczy

J. Selye University

Slovakia

Uwe Reuter

Dresden University of Technology ( email )

Einsteinstrasse 3
Dresden, 01062
Germany

Timon Rabczuk

Bauhaus University - Institute of Structural Mechanics ( email )

Weimar
Germany

Peter M. Atkinson

Lancaster University ( email )

Lancaster LA1 4YX
United Kingdom

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