Machine-learning method for analyzing and predicting the number of hospitalizations of children during the fourth wave of the COVID-19 pandemic in the Lviv region

Olena Pavliuk; Halyna Kolesnyk

doi:10.1007/s40860-022-00188-z

Machine-learning method for analyzing and predicting the number of hospitalizations of children during the fourth wave of the COVID-19 pandemic in the Lviv region

J Reliab Intell Environ. 2023;9(1):17-26. doi: 10.1007/s40860-022-00188-z. Epub 2022 Sep 1.

Authors

Olena Pavliuk¹, Halyna Kolesnyk²

Affiliations

¹ Department of Automated Control Systems, Lviv Polytechnic National University, Stepan Bandera Str., 12, Lviv, 79013 Ukraine.
² Foreign Languages Department, Lviv Polytechnic National University, Stepan Bandera Str., 12, Lviv, 79013 Ukraine.

Abstract

The purpose of this paper is to develop a machine-learning model for analyzing and predicting the number of hospitalizations of children in the Lviv region during the fourth wave of the COVID-19 pandemic. This wave is characterized by dominance of a new strain of the virus-Omicron-that spreads faster than previous ones and often affects children. Their high sociability and a low level of vaccination in Ukraine resulted in a sharp increase in the number of hospitalizations. The complexity of the research is also related to the geolocation of the Lviv region. This article analyzes and predicts the number of hospitalizations of children during the fourth wave of the COVID-19 pandemic for the first time for the Lviv region. Data were obtained from publicly available resources. Public Domain Software-the Python programming language and the Pandas library-was used for software implementation of the machine-learning method: the developed model consists of two components-analysis and prediction. The analysis of the number of hospitalized children was performed using the Pearson correlation coefficient. Short- and medium-term predictions were made with the use of non-iterative SGTM neural-like structures that were taught in supervised mode and tested in online mode. The RMS and maximum ones that were reduced to the range of error values of short-term (up to a week) and medium-term (up to 2 weeks) predictions did not exceed 0.48% and 0.61% and 1.81% and 2.83%, respectively. The developed model can also be used for predicting other COVID-19 parameters.

Keywords: COVID; Correlation; Pandemic; Prediction; SGTM neural-like structures; The Lviv region.

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