The effectiveness analysis of selected IT tools for predictions of the COVID-19 pandemic
Paweł Dymora, Mirosław Mazurek, Kamil Łyczko
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 (2022)
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.
- Le Ha Anh and Nguyen Minh Trang and Nguyen Thi Phuong Linh, The Influence of Work-from-home on job performance during COVID-19 pandemic: Empirical evidence Hanoi, Vietnam, Proceedings of the International Conference on Research in Management & Technovation, vol. 28, pp. 73-81, http://dx.doi.org/10.15439/2021KM59, 2021
- F. Grabowski, A. Paszkiewicz, M. Bolanowski: Wireless networks environment and complex networks, Lecture Notes in Electrical Engineering, Analysis and Simulation of Electrical and Computer Systems, Springer International Publishing Switzerland, ISBN 978-3-319-38545-7, vol. 324, str. 261-270, 2015.
- P. Nadella, A. Swaminathan, S. V. Subramanian, SV, “Forecasting efforts from prior epidemics and COVID-19 predictions”. EUROPEAN JOURNAL OF EPIDEMIOLOGY, Vol. 35, Issue 8, Page 727-729, http://dx.doi.org/10.1007/s10654-020-00661-0, 2020.
- D. Prajapati, M. Kanojia, “Forecasting of COVID-19 Cases in INDIA Using AReIMA and AR Time-Series Algorithm”, Proceedings of the 13 th International Conference on Soft Computing and Pattern Recognition (SOCPAR 2021), Book Series Lecture Notes in Networks and Systems, Vol. 417, Page 361-370, http://dx.doi.org/10.1007/978-3-030-96302-6_33, 2022.
- G. Alabdulrazzaq, M. N. Alenezi, Y. Rawajfih, B. A. Alghannam, A. A. Al-Hassan, F. S. Al-Anzi, “On the accuracy of ARIMA based prediction of COVID-19 spread “, RESULTS IN PHYSICS, Vol. 27, Article Number 104509, http://dx.doi.org/10.1016/j.rinp.2021.104509, 2021.
- F. Rustam, A. A. Reshi, A. Mehmood, S. Ullah, B. W. On, W. Aslam, G. S. Choi, “COVID-19 Future Forecasting Using Supervised Machine Learning Models”, IEEE ACCESS, Vol. 8, Page 101489-101499, http://dx.doi.org/10.1109/ACCESS.2020.2997311, 2020.
- K. Rajab, F. Kamalov, A. K. Cherukuri, “Forecasting COVID-19: Vector Autoregression-Based Model”, Arabian Journal for Science and Engineering, http://dx.doi.org/10.1007/s13369-021-06526-2, 2022.
- S. Kumar, R. Sharma, T. Tsunoda, T. Kumarevel, A. Sharma, “Forecasting the spread of COVID-19 using LSTM network”, BMC BIOINFORMATICS, Vol. 22, Issue SUPPL 6, Article Number 316, http://dx.doi.org/10.1186/s12859-021-04224-2, 2021.
- M. Wieczorek, J. Silka, M. Wozniak, “Neural network powered COVID-19 spread forecasting model”, CHAOS SOLITONS & FRACTALS, Vol. 140, Article Number 110203, http://dx.doi.org/10.1016/j.chaos.2020.110203, 2020.
- M. Sobczyk, “Prognozowanie. Teoria, przykłady, zadania.” Wydawnictwo Placet, 2008.
- R. J. Hyndman, G. Athanasopoulos, “Forecasting: Principles and Practise”, https://otexts.com/fpp2/. Access 15.09.2021 r.
- https://www.machinelearningplus.com/time-series/arima-model-time-series-forecasting-python/ Access 27.09.2021 r.
- https://www.rstudio.com/. Access 27.09.2021 r.
- https://www.rdocumentation.org/packages/forecast/versions/8.15. Access 27.09.2021 r.
- https://www.statsoft.pl/Programy/Architektura- STATISTICA/Programy-desktop/. Access 27.09.2021 r.