From Data to Action : Empowering COVID-19 Monitoring and Forecasting with Intelligent Algorithms

Charles, V., Mousavi, S. M. H., Gherman, T. and Mosavi, S. M. H. (2023) From Data to Action : Empowering COVID-19 Monitoring and Forecasting with Intelligent Algorithms. Journal of the Operational Research Society. 75(7), pp. 1261-1278. 0160-5682.

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Abstract:

The COVID-19 pandemic has profoundly impacted every aspect of our lives, from economic to the social facets of contemporary society. While the new COVID-19 waves may not be anticipated to be as severe as previous ones, it would be unreasonable to assume that they will cease any time soon. Consequently, forecasting the numbers of future infections, recovered patients, and death cases remains a very much logical step in trying to fight against further waves, in conjunction with ongoing vaccination efforts. In this paper, we investigate the efficiency of three intelligent machine-learning algorithms, namely GMDH, Bi-LSTM, and GA+NN, for COVID-19 forecasting, with an application to Iran and the United Kingdom. The experimental results show that the algorithms can be used to forecast the next six months of COVID-19 in terms of confirmed, recovered, and death cases, which gives a more ample timeframe for using the results to make better practical yet strategic decisions and take appropriate actions or measures to deploy resources effectively to contain or curb the spread of the coronavirus. Despite the distinct dynamics observed in the data, our analysis proves the robustness of the employed models.

Uncontrolled Keywords:

/dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_being

Creators:

Charles, V., Mousavi, S. M. H., Gherman, T. and Mosavi, S. M. H.

Faculties, Divisions and Institutes:

Faculties > Faculty of Business & Law > International Strategy & Business
University Faculties, Divisions and Research Centres - OLD > Research Centre > Centre for Sustainable Business Practices
Research Centres > Centre for Sustainable Business Practices
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Date:

30 September 2023

Date Type:

Publication

Page Range:

pp. 1261-1278

Journal or Publication Title:

Journal of the Operational Research Society

Volume:

Number:

Number of Pages:

Language:

English

ISSN:

0160-5682

Status:

Published / Disseminated

Refereed:

Yes