Analysis of the spread of COVID-19 in Ukraine using Google Trends tools

2022 IEEE 17th International Conference on Computer Sciences and Information Technologies (CSIT) Pub Date : 2022-11-10 DOI:10.1109/CSIT56902.2022.10000436

M. Medykovskyy, O. Pavliuk, Myroslav Mishchuk

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Abstract

The paper is devoted to the analysis of the spread of the COVID-19 pandemic in Ukraine based on finding the correlation between search terms in Google search engine and laboratory-confirmed cases. Statistics were obtained from open sources. The analysis was performed on matrices based on the Pearson correlation coefficient. To do this, we analyzed 25 typical search phrases, and after grouping them – 7 remained. The data were reduced to the same discreteness. Correlation matrices were calculated for each wave of the pandemic and for altogether. As a result, the correlation between search phrases and laboratory-confirmed cases was observed only in the second and third waves of the pandemic. Moreover, in the first wave, the preconditions for its occurrence were found; in the second - Pearson’s correlation coefficient was 0.74, and in the third wave, it decreased to 0.57. Other correlations that are specific to each pandemic wave are also analyzed. Additionally, it was proved that polynomials of the 6th degree most effectively restore lost data.

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使用谷歌趋势工具分析COVID-19在乌克兰的传播

本文通过寻找谷歌搜索引擎中的搜索词与实验室确诊病例之间的相关性，致力于分析COVID-19大流行在乌克兰的传播。统计数据来自公开来源。分析在基于Pearson相关系数的矩阵上进行。为此，我们分析了25个典型的搜索短语，在对它们进行分组后，只剩下7个。数据被简化为同样的离散性。计算了每一波大流行和总体大流行的相关矩阵。因此，仅在大流行的第二波和第三波中观察到搜索短语与实验室确诊病例之间的相关性。此外，在第一波浪潮中，发现了它发生的先决条件;第二波Pearson相关系数为0.74，第三波Pearson相关系数降至0.57。还分析了每个大流行波特有的其他相关性。此外，还证明了六次多项式最有效地恢复了丢失的数据。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2022 IEEE 17th International Conference on Computer Sciences and Information Technologies (CSIT)

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