The DOI matches its Crossref record; 78 of the 78 references it cites were found in a registry or an index.
COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach
Open accessAbstract
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.
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COVID-19 and Italy: what next?
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Flood Prediction Using Machine Learning Models: Literature Review
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Self-citationThe International Journal of Production Research
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Cited by
Applications of artificial intelligence in battling against covid-19: A literature review
Tayarani N., M.-H. (2021) · Chaos, Solitons & Fractals · doi:10.1016/j.chaos.2020.110338 · Cited by 210
Potential neutralizing antibodies discovered for novel corona virus using machine learning
Magar, R., Yadav, P., & Barati Farimani, A. (2021) · Scientific Reports · doi:10.1038/s41598-021-84637-4 · Cited by 114
Metadata
- Authors
- Pinter, G., Felde, I., Mosavi, A., Ghamisi, P., & Gloaguen, R.
- Venue
- Mathematics
- Publisher
- MDPI AG
- Published
- 2020-06-02
- Volume
- 8
- Issue
- 6
- Pages
- 890
- ISSN
- 2227-7390 (ISSN-L)
- OpenAlex
- W3022163878
- Cited by
- 328
- References
- 78
- FWCI
- 11.15
- Type
- journal article
- Language
- en
- Open access
- gold
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