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Clustering of Covid-19 morbidity cases in Germany

The Covid-19 coronavirus has spread almost all over the world. Though it has been reported recently that the epidemic declines in China, in other countries it still hasn’t achieved peak level. The data analysis methods may help struggling against the disease. The Covid-19 Tracking Germany dataset ha...

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Autor principal: Petrusevich, D A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: IOP Publishing 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7350573/
https://www.ncbi.nlm.nih.gov/pubmed/34191947
http://dx.doi.org/10.1088/1757-899X/862/4/042037
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author Petrusevich, D A
author_facet Petrusevich, D A
author_sort Petrusevich, D A
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description The Covid-19 coronavirus has spread almost all over the world. Though it has been reported recently that the epidemic declines in China, in other countries it still hasn’t achieved peak level. The data analysis methods may help struggling against the disease. The Covid-19 Tracking Germany dataset has been handled in the research. It’s daily refreshed dataset available at the kaggle.com site. It contains information on number of fallen ill people in Germany. The cases are grouped by federal land, city, age diapason and date. The main goal of the research is to underline differences in morbidity registered in different lands of Germany. There have been published new suggestions about connection between coronavirus morbidity and BCG vaccination. This question is also taken into account. Analysis based on the handled dataset is able to make only oblique conclusions because of lack of information. Differences in coronavirus morbidity in various regions and various age groups are highlighted. The regions of Germany are clustered into groups by gravity of recent situation.
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spelling pubmed-73505732020-07-10 Clustering of Covid-19 morbidity cases in Germany Petrusevich, D A IOP Conf Ser Mater Sci Eng Paper The Covid-19 coronavirus has spread almost all over the world. Though it has been reported recently that the epidemic declines in China, in other countries it still hasn’t achieved peak level. The data analysis methods may help struggling against the disease. The Covid-19 Tracking Germany dataset has been handled in the research. It’s daily refreshed dataset available at the kaggle.com site. It contains information on number of fallen ill people in Germany. The cases are grouped by federal land, city, age diapason and date. The main goal of the research is to underline differences in morbidity registered in different lands of Germany. There have been published new suggestions about connection between coronavirus morbidity and BCG vaccination. This question is also taken into account. Analysis based on the handled dataset is able to make only oblique conclusions because of lack of information. Differences in coronavirus morbidity in various regions and various age groups are highlighted. The regions of Germany are clustered into groups by gravity of recent situation. IOP Publishing 2020-05 /pmc/articles/PMC7350573/ /pubmed/34191947 http://dx.doi.org/10.1088/1757-899X/862/4/042037 Text en Published under licence by IOP Publishing Ltd https://creativecommons.org/licenses/by/3.0/Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence (https://creativecommons.org/licenses/by/3.0/) . Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
spellingShingle Paper
Petrusevich, D A
Clustering of Covid-19 morbidity cases in Germany
title Clustering of Covid-19 morbidity cases in Germany
title_full Clustering of Covid-19 morbidity cases in Germany
title_fullStr Clustering of Covid-19 morbidity cases in Germany
title_full_unstemmed Clustering of Covid-19 morbidity cases in Germany
title_short Clustering of Covid-19 morbidity cases in Germany
title_sort clustering of covid-19 morbidity cases in germany
topic Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7350573/
https://www.ncbi.nlm.nih.gov/pubmed/34191947
http://dx.doi.org/10.1088/1757-899X/862/4/042037
work_keys_str_mv AT petrusevichda clusteringofcovid19morbiditycasesingermany