Fuzzy clustering method to compare the spread rate of Covid-19 in the high risks countries.
Identifieur interne : 000F05 ( Main/Exploration ); précédent : 000F04; suivant : 000F06Fuzzy clustering method to compare the spread rate of Covid-19 in the high risks countries.
Auteurs : Mohammad Reza Mahmoudi [Viêt Nam, Iran] ; Dumitru Baleanu [Turquie, Roumanie] ; Zulkefli Mansor [Malaisie] ; Bui Anh Tuan [Viêt Nam] ; Kim-Hung Pho [Viêt Nam]Source :
- Chaos, solitons, and fractals [ 0960-0779 ] ; 2020.
Abstract
The numbers of confirmed cases of new coronavirus (Covid-19) are increased daily in different countries. To determine the policies and plans, the study of the relations between the distributions of the spread of this virus in other countries is critical. In this work, the distributions of the spread of Covid-19 in Unites States America, Spain, Italy, Germany, United Kingdom, France, and Iran were compared and clustered using fuzzy clustering technique. At first, the time series of Covid-19 datasets in selected countries were considered. Then, the relation between spread of Covid-19 and population's size was studied using Pearson correlation. The effect of the population's size was eliminated by rescaling the Covid-19 datasets based on the population's size of USA. Finally, the rescaled Covid-19 datasets of the countries were clustered using fuzzy clustering. The results of Pearson correlation indicated that there were positive and significant between total confirmed cases, total dead cases and population's size of the countries. The clustering results indicated that the distribution of spreading in Spain and Italy was approximately similar and differed from other countries.
DOI: 10.1016/j.chaos.2020.110230
PubMed: 32863611
PubMed Central: PMC7442906
Affiliations:
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type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The numbers of confirmed cases of new coronavirus (Covid-19) are increased daily in different countries. To determine the policies and plans, the study of the relations between the distributions of the spread of this virus in other countries is critical. In this work, the distributions of the spread of Covid-19 in Unites States America, Spain, Italy, Germany, United Kingdom, France, and Iran were compared and clustered using fuzzy clustering technique. At first, the time series of Covid-19 datasets in selected countries were considered. Then, the relation between spread of Covid-19 and population's size was studied using Pearson correlation. The effect of the population's size was eliminated by rescaling the Covid-19 datasets based on the population's size of USA. Finally, the rescaled Covid-19 datasets of the countries were clustered using fuzzy clustering. The results of Pearson correlation indicated that there were positive and significant between total confirmed cases, total dead cases and population's size of the countries. The clustering results indicated that the distribution of spreading in Spain and Italy was approximately similar and differed from other countries.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">32863611</PMID><DateRevised><Year>2020</Year><Month>09</Month><Day>01</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">0960-0779</ISSN><JournalIssue CitedMedium="Print"><Volume>140</Volume><PubDate><Year>2020</Year><Month>Nov</Month></PubDate></JournalIssue><Title>Chaos, solitons, and fractals</Title><ISOAbbreviation>Chaos Solitons Fractals</ISOAbbreviation></Journal><ArticleTitle>Fuzzy clustering method to compare the spread rate of Covid-19 in the high risks countries.</ArticleTitle><Pagination><MedlinePgn>110230</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.chaos.2020.110230</ELocationID><Abstract><AbstractText>The numbers of confirmed cases of new coronavirus (Covid-19) are increased daily in different countries. To determine the policies and plans, the study of the relations between the distributions of the spread of this virus in other countries is critical. In this work, the distributions of the spread of Covid-19 in Unites States America, Spain, Italy, Germany, United Kingdom, France, and Iran were compared and clustered using fuzzy clustering technique. At first, the time series of Covid-19 datasets in selected countries were considered. Then, the relation between spread of Covid-19 and population's size was studied using Pearson correlation. The effect of the population's size was eliminated by rescaling the Covid-19 datasets based on the population's size of USA. Finally, the rescaled Covid-19 datasets of the countries were clustered using fuzzy clustering. The results of Pearson correlation indicated that there were positive and significant between total confirmed cases, total dead cases and population's size of the countries. The clustering results indicated that the distribution of spreading in Spain and Italy was approximately similar and differed from other countries.</AbstractText><CopyrightInformation>© 2020 Elsevier Ltd. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Mahmoudi</LastName><ForeName>Mohammad Reza</ForeName><Initials>MR</Initials><AffiliationInfo><Affiliation>Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Statistics, Faculty of Science, Fasa University, Fasa, Fars, Iran.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Baleanu</LastName><ForeName>Dumitru</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>Department of Mathematics, Faculty of Art and Sciences, Cankaya University Balgat 06530, Ankara, Turkey.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Institute of Space Sciences, Magurele-Bucharest, Romania.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Mansor</LastName><ForeName>Zulkefli</ForeName><Initials>Z</Initials><AffiliationInfo><Affiliation>Fakulti Teknologi dan Sains Maklumat, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Tuan</LastName><ForeName>Bui Anh</ForeName><Initials>BA</Initials><AffiliationInfo><Affiliation>Department of Mathematics Education, Teachers College, Can Tho University, Vietnam.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Pho</LastName><ForeName>Kim-Hung</ForeName><Initials>KH</Initials><AffiliationInfo><Affiliation>Fractional Calculus, Optimization and Algebra Research Group, Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City, Vietnam.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>08</Month><Day>22</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Chaos Solitons Fractals</MedlineTA><NlmUniqueID>100971564</NlmUniqueID><ISSNLinking>0960-0779</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Coronaviruses</Keyword><Keyword MajorTopicYN="N">Correlation</Keyword><Keyword MajorTopicYN="N">Covid-19</Keyword><Keyword MajorTopicYN="N">Fuzzy Clustering</Keyword><Keyword MajorTopicYN="N">Statistics</Keyword></KeywordList><CoiStatement>The authors declare no conflict of interest.</CoiStatement></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>05</Month><Day>04</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2020</Year><Month>08</Month><Day>09</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>08</Month><Day>21</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>9</Month><Day>1</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>8</Month><Day>31</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>8</Month><Day>31</Day><Hour>6</Hour><Minute>1</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32863611</ArticleId><ArticleId IdType="doi">10.1016/j.chaos.2020.110230</ArticleId><ArticleId IdType="pii">S0960-0779(20)30626-3</ArticleId><ArticleId IdType="pii">110230</ArticleId><ArticleId IdType="pmc">PMC7442906</ArticleId></ArticleIdList><pmc-dir>pmcsd</pmc-dir>
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