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

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ID: 266327
2020
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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.
Reference Key
pinter2020mathematicscovid-19 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Gergo Pinter;Imre Felde;Amir Mosavi;Pedram Ghamisi;Richard Gloaguen;Pinter, Gergo;Felde, Imre;Mosavi, Amir;Ghamisi, Pedram;Gloaguen, Richard;
Journal Mathematics
Year 2020
DOI
10.3390/math8060890
URL
https://doi.org/10.3390/math8060890##authors
Keywords
Machine learning prediction model covid-19

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