Application of the ARIMA model on the COVID-2019 epidemic dataset.
Identifieur interne : 000408 ( PubMed/Corpus ); précédent : 000407; suivant : 000409Application of the ARIMA model on the COVID-2019 epidemic dataset.
Auteurs : Domenico Benvenuto ; Marta Giovanetti ; Lazzaro Vassallo ; Silvia Angeletti ; Massimo CiccozziSource :
- Data in brief [ 2352-3409 ] ; 2020.
Abstract
Coronavirus disease 2019 (COVID-2019) has been recognized as a global threat, and several studies are being conducted using various mathematical models to predict the probable evolution of this epidemic. These mathematical models based on various factors and analyses are subject to potential bias. Here, we propose a simple econometric model that could be useful to predict the spread of COVID-2019. We performed Auto Regressive Integrated Moving Average (ARIMA) model prediction on the Johns Hopkins epidemiological data to predict the epidemiological trend of the prevalence and incidence of COVID-2019. For further comparison or for future perspective, case definition and data collection have to be maintained in real time.
DOI: 10.1016/j.dib.2020.105340
PubMed: 32181302
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These mathematical models based on various factors and analyses are subject to potential bias. Here, we propose a simple econometric model that could be useful to predict the spread of COVID-2019. We performed Auto Regressive Integrated Moving Average (ARIMA) model prediction on the Johns Hopkins epidemiological data to predict the epidemiological trend of the prevalence and incidence of COVID-2019. For further comparison or for future perspective, case definition and data collection have to be maintained in real time.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">32181302</PMID><DateRevised><Year>2020</Year><Month>03</Month><Day>20</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">2352-3409</ISSN><JournalIssue CitedMedium="Internet"><Volume>29</Volume><PubDate><Year>2020</Year><Month>Apr</Month></PubDate></JournalIssue><Title>Data in brief</Title><ISOAbbreviation>Data Brief</ISOAbbreviation></Journal><ArticleTitle>Application of the ARIMA model on the COVID-2019 epidemic dataset.</ArticleTitle><Pagination><MedlinePgn>105340</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.dib.2020.105340</ELocationID><Abstract><AbstractText>Coronavirus disease 2019 (COVID-2019) has been recognized as a global threat, and several studies are being conducted using various mathematical models to predict the probable evolution of this epidemic. These mathematical models based on various factors and analyses are subject to potential bias. Here, we propose a simple econometric model that could be useful to predict the spread of COVID-2019. We performed Auto Regressive Integrated Moving Average (ARIMA) model prediction on the Johns Hopkins epidemiological data to predict the epidemiological trend of the prevalence and incidence of COVID-2019. For further comparison or for future perspective, case definition and data collection have to be maintained in real time.</AbstractText><CopyrightInformation>© 2020 The Authors.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Benvenuto</LastName><ForeName>Domenico</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>Unit of Medical Statistics and Molecular Epidemiology, University Campus Bio-Medico of Rome, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Giovanetti</LastName><ForeName>Marta</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Laboratório de Flavivírus, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Vassallo</LastName><ForeName>Lazzaro</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>Department of Financial and Statistical Sciences, University of Salerno, Salerno, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Angeletti</LastName><ForeName>Silvia</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Unit of Clinical Laboratory Science, University Campus Bio-Medico of Rome, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ciccozzi</LastName><ForeName>Massimo</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Laboratório de Flavivírus, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>02</Month><Day>26</Day></ArticleDate></Article><MedlineJournalInfo><Country>Netherlands</Country><MedlineTA>Data Brief</MedlineTA><NlmUniqueID>101654995</NlmUniqueID><ISSNLinking>2352-3409</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">ARIMA model</Keyword><Keyword MajorTopicYN="N">COVID-2019 epidemic</Keyword><Keyword MajorTopicYN="N">Forecast</Keyword><Keyword MajorTopicYN="N">Infection control</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>02</Month><Day>14</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2020</Year><Month>02</Month><Day>21</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>02</Month><Day>21</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>3</Month><Day>18</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>3</Month><Day>18</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>3</Month><Day>18</Day><Hour>6</Hour><Minute>1</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32181302</ArticleId><ArticleId IdType="doi">10.1016/j.dib.2020.105340</ArticleId><ArticleId IdType="pii">S2352-3409(20)30234-1</ArticleId><ArticleId IdType="pii">105340</ArticleId><ArticleId IdType="pmc">PMC7063124</ArticleId></ArticleIdList><ReferenceList><Reference><Citation>BMC Med Inform Decis Mak. 2013 May 02;13:56</Citation><ArticleIdList><ArticleId IdType="pubmed">23638635</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>PLoS One. 2018 Sep 4;13(9):e0201987</Citation><ArticleIdList><ArticleId IdType="pubmed">30180159</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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