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Fuzzy Clustering Methods to Identify the Epidemiological Situation and Its Changes in European Countries during COVID-19

cris.virtual.author-orcid0000-0002-3149-7748
cris.virtual.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtualsource.author-orcidff55c10b-268e-4ec2-b05a-81b6280fb50b
cris.virtualsource.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
dc.abstract.enThe main research question concerned the identification of changes in the COVID-19 epidemiological situation using fuzzy clustering methods. This research used cross-sectional time series data obtained from the European Centre for Disease Prevention and Control. The identification of country types in terms of epidemiological risk was carried out using the fuzzy c-means clustering method. We also used the entropy index to measure the degree of fuzziness in the classification and evaluate the uncertainty of epidemiological states. The proposed approach allowed us to identify countries’ epidemic states. Moreover, it also made it possible to determine the time of transition from one state to another, as well as to observe fluctuations during changes of state. Three COVID-19 epidemic states were identified in Europe, i.e., stabilisation, destabilisation, and expansion. The methodology is universal and can also be useful for other countries, as well as the research results being important for governments, politicians and other policy-makers working to mitigate the effects of the COVID-19 pandemic.
dc.affiliationWydział Ekonomiczny
dc.affiliation.instituteKatedra Finansów i Rachunkowości
dc.contributor.authorŁuczak, Aleksandra
dc.contributor.authorKalinowski, Sławomir
dc.date.access2026-02-10
dc.date.accessioned2026-02-26T10:11:04Z
dc.date.available2026-02-26T10:11:04Z
dc.date.copyright2021-12-22
dc.date.issued2022
dc.description.abstract<jats:p>The main research question concerned the identification of changes in the COVID-19 epidemiological situation using fuzzy clustering methods. This research used cross-sectional time series data obtained from the European Centre for Disease Prevention and Control. The identification of country types in terms of epidemiological risk was carried out using the fuzzy c-means clustering method. We also used the entropy index to measure the degree of fuzziness in the classification and evaluate the uncertainty of epidemiological states. The proposed approach allowed us to identify countries’ epidemic states. Moreover, it also made it possible to determine the time of transition from one state to another, as well as to observe fluctuations during changes of state. Three COVID-19 epidemic states were identified in Europe, i.e., stabilisation, destabilisation, and expansion. The methodology is universal and can also be useful for other countries, as well as the research results being important for governments, politicians and other policy-makers working to mitigate the effects of the COVID-19 pandemic.</jats:p>
dc.description.accesstimeat_publication
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.if2,7
dc.description.number1
dc.description.points100
dc.description.versionfinal_published
dc.description.volume24
dc.identifier.doi10.3390/e24010014
dc.identifier.issn1099-4300
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/7488
dc.identifier.weblinkhttps://www.mdpi.com/1099-4300/24/1/14
dc.languageen
dc.relation.ispartofEntropy
dc.relation.pagesart. 14
dc.rightsCC-BY
dc.sciencecloudnosend
dc.share.typeOPEN_JOURNAL
dc.subject.enfuzzy c-means classification method
dc.subject.enentropy
dc.subject.enCOVID-19
dc.subject.enepidemic states
dc.subject.enEurope
dc.titleFuzzy Clustering Methods to Identify the Epidemiological Situation and Its Changes in European Countries during COVID-19
dc.title.volumeSpecial Issue Entropy-Based Applications in Economics, Finance, and Management
dc.typeJournalArticle
dspace.entity.typePublication
oaire.citation.issue1
oaire.citation.volume24