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. © 2022 IEEE.
ABSTRACT
With the rapid spread of the Covid-19 pandemic in Ukraine, there is a need to use existing and develop new methods for its analysis and forecast. This article proposes a machine learning model that analyzes and provides a short-term forecast of the pandemic in Ukraine based on “standard” open source statistics and the six most commonly used Covid-related phrases in Google Trends. The lost information was restored using power polynomials. Covid-19 distribution in Ukraine was analyzed for each pandemic wave using correlation dependences with Pearson’s correlation coefficients. It has been proven that the increase in the number of laboratory-confirmed Covid cases in Ukraine is happening right before the increase in Google user activity on this topic. 8 models based on SGTM neural structures were developed to study the average weekly data, both reduced and not reduced to the range from 0 to 1. In the most accurate model that implemented a short-term forecast of laboratory-confirmed cases of Covid in Ukraine, the root square error was 5.55%. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
A review of the COVID-19 pandemic in Bahrain has been conducted. Correlations between the parameters describing the coronavirus pandemic have been established. Partially lost data was supplemented by polynomial functions, as well as by linear approximation. The number of those who suffered and those who died from COVID-19 was predicted using SGTM neural-like structure topologies supervised mode. © 2020 IEEE.