Pavliuk Olena, Kolesnyk Halyna
Department of Automated Control Systems, Lviv Polytechnic National University, Stepan Bandera Str., 12, Lviv, 79013 Ukraine.
Foreign Languages Department, Lviv Polytechnic National University, Stepan Bandera Str., 12, Lviv, 79013 Ukraine.
J Reliab Intell Environ. 2023;9(1):17-26. doi: 10.1007/s40860-022-00188-z. Epub 2022 Sep 1.
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.
本文的目的是开发一种机器学习模型，用于分析和预测利沃夫地区在新冠疫情第四波期间儿童的住院人数。这一波疫情的特点是一种新的病毒株——奥密克戎——占主导地位，其传播速度比以前的毒株更快，且经常感染儿童。乌克兰儿童社交性强且疫苗接种率低，导致住院人数急剧增加。该研究的复杂性还与利沃夫地区的地理位置有关。本文首次对利沃夫地区新冠疫情第四波期间儿童的住院人数进行了分析和预测。数据来自公开可用资源。使用公共领域软件——Python编程语言和Pandas库——来实现机器学习方法的软件：所开发的模型由分析和预测两个组件组成。使用皮尔逊相关系数对住院儿童人数进行分析。使用在监督模式下训练并在在线模式下测试的非迭代SGTM类神经结构进行短期和中期预测。短期（最长一周）和中期（最长两周）预测的误差值范围内的均方根（RMS）和最大值分别不超过0.48%和0.61%以及1.81%和2.83%。所开发的模型还可用于预测其他新冠参数。
