Application of the ARIMA model on the COVID-2019 epidemic dataset
Identifieur interne : 000563 ( Pmc/Curation ); précédent : 000562; suivant : 000564Application of the ARIMA model on the COVID-2019 epidemic dataset
Auteurs : Domenico Benvenuto [Italie] ; Marta Giovanetti [Brésil] ; Lazzaro Vassallo [Italie] ; Silvia Angeletti [Italie] ; Massimo Ciccozzi [Brésil]Source :
- 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.
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DOI: 10.1016/j.dib.2020.105340
PubMed: 32181302
PubMed Central: 7063124
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Laboratory Science, University Campus Bio-Medico of Rome</wicri:regionArea></affiliation></author><author><name sortKey="Ciccozzi, Massimo" sort="Ciccozzi, Massimo" uniqKey="Ciccozzi M" first="Massimo" last="Ciccozzi">Massimo Ciccozzi</name><affiliation wicri:level="1"><nlm:aff id="aff2">Laboratório de Flavivírus, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil</nlm:aff><country xml:lang="fr">Brésil</country><wicri:regionArea>Laboratório de Flavivírus, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro</wicri:regionArea></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">32181302</idno><idno type="pmc">7063124</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7063124</idno><idno type="RBID">PMC:7063124</idno><idno type="doi">10.1016/j.dib.2020.105340</idno><date when="2020">2020</date><idno type="wicri:Area/Pmc/Corpus">000563</idno><idno type="wicri:explorRef" wicri:stream="Pmc" 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Giovanetti</name><affiliation wicri:level="1"><nlm:aff id="aff2">Laboratório de Flavivírus, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil</nlm:aff><country xml:lang="fr">Brésil</country><wicri:regionArea>Laboratório de Flavivírus, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro</wicri:regionArea></affiliation></author><author><name sortKey="Vassallo, Lazzaro" sort="Vassallo, Lazzaro" uniqKey="Vassallo L" first="Lazzaro" last="Vassallo">Lazzaro Vassallo</name><affiliation wicri:level="1"><nlm:aff id="aff3">Department of Financial and Statistical Sciences, University of Salerno, Salerno, Italy</nlm:aff><country xml:lang="fr">Italie</country><wicri:regionArea>Department of Financial and Statistical Sciences, University of Salerno, Salerno</wicri:regionArea></affiliation></author><author><name sortKey="Angeletti, Silvia" sort="Angeletti, Silvia" uniqKey="Angeletti S" first="Silvia" last="Angeletti">Silvia Angeletti</name><affiliation wicri:level="1"><nlm:aff id="aff4">Unit of Clinical Laboratory Science, University Campus Bio-Medico of Rome, Italy</nlm:aff><country xml:lang="fr">Italie</country><wicri:regionArea>Unit of Clinical Laboratory Science, University Campus Bio-Medico of Rome</wicri:regionArea></affiliation></author><author><name sortKey="Ciccozzi, Massimo" sort="Ciccozzi, Massimo" uniqKey="Ciccozzi M" first="Massimo" last="Ciccozzi">Massimo Ciccozzi</name><affiliation wicri:level="1"><nlm:aff id="aff2">Laboratório de Flavivírus, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil</nlm:aff><country xml:lang="fr">Brésil</country><wicri:regionArea>Laboratório de Flavivírus, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro</wicri:regionArea></affiliation></author></analytic><series><title level="j">Data in Brief</title><idno type="eISSN">2352-3409</idno><imprint><date when="2020">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>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.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Fattah, J" uniqKey="Fattah J">J. Fattah</name></author><author><name sortKey="Ezzine, L" uniqKey="Ezzine L">L. Ezzine</name></author><author><name sortKey="Aman, Z" uniqKey="Aman Z">Z. Aman</name></author><author><name sortKey="El Moussami, H" uniqKey="El Moussami H">H. El Moussami</name></author><author><name sortKey="Lachhab, A" uniqKey="Lachhab A">A. Lachhab</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Cao, S" uniqKey="Cao S">S. Cao</name></author><author><name sortKey="Wang, F" uniqKey="Wang F">F. Wang</name></author><author><name sortKey="Tam, W" uniqKey="Tam W">W. Tam</name></author><author><name sortKey="Tse, L A" uniqKey="Tse L">L.A. Tse</name></author><author><name sortKey="Kim, J H" uniqKey="Kim J">J.H. Kim</name></author><author><name sortKey="Liu, J" uniqKey="Liu J">J. Liu</name></author><author><name sortKey="Lu, Z" uniqKey="Lu Z">Z. Lu</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Cheung, Y W" uniqKey="Cheung Y">Y.-W. Cheung</name></author><author><name sortKey="Lai, K S" uniqKey="Lai K">K.S. Lai</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Baiocchi, G" uniqKey="Baiocchi G">G. Baiocchi</name></author><author><name sortKey="Distaso, W" uniqKey="Distaso W">W. Distaso</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Wang, Y W" uniqKey="Wang Y">Y.W. Wang</name></author><author><name sortKey="Shen, Z Z" uniqKey="Shen Z">Z.Z. Shen</name></author><author><name sortKey="Jiang, Y" uniqKey="Jiang Y">Y. Jiang</name></author></analytic></biblStruct></listBibl></div1></back></TEI><pmc article-type="data-paper"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Data Brief</journal-id><journal-id journal-id-type="iso-abbrev">Data Brief</journal-id><journal-title-group><journal-title>Data in Brief</journal-title></journal-title-group><issn pub-type="epub">2352-3409</issn><publisher><publisher-name>Elsevier</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">32181302</article-id><article-id pub-id-type="pmc">7063124</article-id><article-id pub-id-type="publisher-id">S2352-3409(20)30234-1</article-id><article-id pub-id-type="doi">10.1016/j.dib.2020.105340</article-id><article-id pub-id-type="publisher-id">105340</article-id><article-categories><subj-group subj-group-type="heading"><subject>Immunology and Microbiology</subject></subj-group></article-categories><title-group><article-title>Application of the ARIMA model on the COVID-2019 epidemic dataset</article-title></title-group><contrib-group><contrib contrib-type="author" id="au1"><name><surname>Benvenuto</surname><given-names>Domenico</given-names></name><xref rid="aff1" ref-type="aff">a</xref><xref rid="fn1" ref-type="fn">1</xref></contrib><contrib contrib-type="author" id="au2"><name><surname>Giovanetti</surname><given-names>Marta</given-names></name><xref rid="aff2" ref-type="aff">b</xref><xref rid="fn1" ref-type="fn">1</xref></contrib><contrib contrib-type="author" id="au3"><name><surname>Vassallo</surname><given-names>Lazzaro</given-names></name><xref rid="aff3" ref-type="aff">c</xref></contrib><contrib contrib-type="author" id="au4"><name><surname>Angeletti</surname><given-names>Silvia</given-names></name><email>s.angeletti@unicampus.it</email><xref rid="aff4" ref-type="aff">d</xref><xref rid="cor1" ref-type="corresp">∗</xref><xref rid="fn1" ref-type="fn">1</xref></contrib><contrib contrib-type="author" id="au5"><name><surname>Ciccozzi</surname><given-names>Massimo</given-names></name><xref rid="aff2" ref-type="aff">b</xref><xref rid="fn1" ref-type="fn">1</xref></contrib></contrib-group><aff id="aff1"><label>a</label>Unit of Medical Statistics and Molecular Epidemiology, University Campus Bio-Medico of Rome, Italy</aff><aff id="aff2"><label>b</label>Laboratório de Flavivírus, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil</aff><aff id="aff3"><label>c</label>Department of Financial and Statistical Sciences, University of Salerno, Salerno, Italy</aff><aff id="aff4"><label>d</label>Unit of Clinical Laboratory Science, University Campus Bio-Medico of Rome, Italy</aff><author-notes><corresp id="cor1"><label>∗</label>Corresponding author. <email>s.angeletti@unicampus.it</email></corresp><fn id="fn1"><label>1</label><p id="ntpara0010">These authors contributed equally to this article.</p></fn></author-notes><pub-date pub-type="pmc-release"><day>26</day><month>2</month><year>2020</year></pub-date><pmc-comment> PMC Release delay is 0 months and 0 days and was based on .</pmc-comment>
<pub-date pub-type="collection"><month>4</month><year>2020</year></pub-date><pub-date pub-type="epub"><day>26</day><month>2</month><year>2020</year></pub-date><volume>29</volume><elocation-id>105340</elocation-id><history><date date-type="received"><day>14</day><month>2</month><year>2020</year></date><date date-type="rev-recd"><day>21</day><month>2</month><year>2020</year></date><date date-type="accepted"><day>21</day><month>2</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 The Authors</copyright-statement><copyright-year>2020</copyright-year><license license-type="CC BY" xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).</license-p></license></permissions><abstract id="abs0010"><p>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.</p></abstract><kwd-group id="kwrds0010"><title>Keywords</title><kwd>COVID-2019 epidemic</kwd><kwd>ARIMA model</kwd><kwd>Forecast</kwd><kwd>Infection control</kwd></kwd-group></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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