About: COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach
Creator	Felde, Imre Ghamisi, Pedram Gloaguen, Richard Mosavi, Amir Pinter, Gergo
Source	MedRxiv
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 a high level of uncertainty or even lack of essential data, the standard 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 used 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 nine 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. Based on the results reported here, and due to the 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.
has issue date	2020-05-06(xsd:dateTime)
bibo:doi	10.1101/2020.05.02.20088427
has license	medrxiv
sha1sum (hex)	28589070457f151a182d1b40564951ced4a99c71
schema:url	https://doi.org/10.1101/2020.05.02.20088427
resource representing a document's title	COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach
resource representing a document's body	covid:28589070457f151a182d1b40564951ced4a99c71#body_text
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