<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The article refers to the topic of the SARS CoV-2 virus pandemic and focuses on the effect of vaccines against this virus. The relation between the administered vaccines and the development of the global pandemic is very pertinent as the problem is being faced by the whole world. The difficulty lies in the fight against the pandemic, which is the cause of the very high death rate due to the virus, and has caused a global economic crisis. Demonstrating patterns and possible anomalies between data on the number of people vaccinated and the course of the disease and the number of deaths is an important factor in raising awareness of the risk of spreading the virus. The methods presented in the second chapter are data agglomeration and the k-means method. The study compared the results obtained in six selected countries from different regions of the world and presented the most important factors influencing the development of the pandemic. The presented methodology was also the basis for a deeper discussion of the factors determining the spread of the virus and can be an introduction to the analysis of time series. At the same time, it enabled the creation of patterns related to the studied phenomenon (for selected countries) defining local factors contributing to the spread of the disease and determining the effectiveness of the vaccines administered in them. The empirical analysis was conducted on the basis of data available in the electronic scientific publication <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://ourworldindata.org/">https://ourworldindata.org/</ext-link>. The visualisations were made in the Tableau program, and the cluster analysis was carried out using the Statistica package.</p>
</abstract>

The article refers to the topic of the SARS CoV-2 virus pandemic and focuses on the effect of vaccines against this virus. The relation between the administered vaccines and the development of the global pandemic is very pertinent as the problem is being faced by the whole world. The difficulty lies in the fight against the pandemic, which is the cause of the very high death rate due to the virus, and has caused a global economic crisis. Demonstrating patterns and possible anomalies between data on the number of people vaccinated and the course of the disease and the number of deaths is an important factor in raising awareness of the risk of spreading the virus. The methods presented in the second chapter are data agglomeration and the k-means method. The study compared the results obtained in six selected countries from different regions of the world and presented the most important factors influencing the development of the pandemic. The presented methodology was also the basis for a deeper discussion of the factors determining the spread of the virus and can be an introduction to the analysis of time series. At the same time, it enabled the creation of patterns related to the studied phenomenon (for selected countries) defining local factors contributing to the spread of the disease and determining the effectiveness of the vaccines administered in them. The empirical analysis was conducted on the basis of data available in the electronic scientific publication https://ourworldindata.org/. The visualisations were made in the Tableau program, and the cluster analysis was carried out using the Statistica package.

Cluster Analysis and Visualisation Describing the Phenomenon of the Covid-19 Virus Pandemic

Econometrics

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

The article refers to the topic of the SARS CoV-2 virus pandemic and focuses on the effect of vaccines against this virus. The relation between the...

Cluster Analysis and Visualisation Describing the Phenomenon of the Covid-19 Virus Pandemic

Online veröffentlicht: 22. Aug. 2023

Seitenbereich: 45 - 61

DOI: https://doi.org/10.15611/eada.2023.2.03

Schlüsselwörter
Covid-19, virus, vaccinations, morbidity cluster analysis coronavirus

© 2023 Grażyna Trzpiot et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

Cluster Analysis and Visualisation Describing the Phenomenon of the Covid-19 Virus Pandemic

Online veröffentlicht: 22. Aug. 2023

Seitenbereich: 45 - 61

DOI: https://doi.org/10.15611/eada.2023.2.03

SchlüsselwörterCovid-19, virus, vaccinations, morbidity cluster analysis coronavirus

© 2023 Grażyna Trzpiot et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

Schlüsselwörter
Covid-19, virus, vaccinations, morbidity cluster analysis coronavirus