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Proceedings of the 17th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 30

The effectiveness analysis of selected IT tools for predictions of the COVID-19 pandemic

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DOI: http://dx.doi.org/10.15439/2022F65

Citation: Proceedings of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 30, pages 583–586 ()

Full text

Abstract. The article presents the problem of the complexity of prediction and the analysis of the effectiveness of selected IT tools in the example of the COVID-19 pandemic data in Poland. The study used a variety of tools and methods to obtain predictions of extinct infections and mortality for each wave of the COVID-19 pandemic. The results are presented for the 4th wave with a detailed description of selected models and methods implemented in the prognostic package of the statistical programming language R, as well as in the Statistica and Microsoft Excel programs. Naive methods, regression models, exponential smoothing methods (including ETS models), ARIMA models, and the method of artificial intelligence - autoregressive models built by neural networks (NNAR) were used. Detailed analysis was performed and the results for each of these methods were compared.

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