COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach

Gergo Pinter, Imre Felde, Amir Mosavi, Pedram Ghamisi and Richard Gloaguen
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Gergo Pinter: John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary
Imre Felde: John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary
Amir Mosavi: Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany
Pedram Ghamisi: Machine Learning Group, Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Chemnitzer Straße 40, 09599 Freiberg, Germany
Richard Gloaguen: Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Chemnitzer Straße 40, 09599 Freiberg, Germany

Mathematics, 2020, vol. 8, issue 6, 1-20

Abstract: Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to the lack of essential data and uncertainty, the epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19, and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are proposed to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for 9 days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

Keywords: machine learning; prediction model; COVID-19 (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

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