Application of the ARIMA model on the COVID-2019 epidemic dataset
Identifieur interne : 000563 ( Pmc/Corpus ); précédent : 000562; suivant : 000564Application 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.
Url:
DOI: 10.1016/j.dib.2020.105340
PubMed: 32181302
PubMed Central: 7063124
Links to Exploration step
PMC:7063124Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Application of the ARIMA model on the COVID-2019 epidemic dataset</title><author><name sortKey="Benvenuto, Domenico" sort="Benvenuto, Domenico" uniqKey="Benvenuto D" first="Domenico" last="Benvenuto">Domenico Benvenuto</name><affiliation><nlm:aff id="aff1">Unit of Medical Statistics and Molecular Epidemiology, University Campus Bio-Medico of Rome, Italy</nlm:aff></affiliation></author><author><name sortKey="Giovanetti, Marta" sort="Giovanetti, Marta" uniqKey="Giovanetti M" first="Marta" last="Giovanetti">Marta Giovanetti</name><affiliation><nlm:aff id="aff2">Laboratório de Flavivírus, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil</nlm:aff></affiliation></author><author><name sortKey="Vassallo, Lazzaro" sort="Vassallo, Lazzaro" uniqKey="Vassallo L" first="Lazzaro" last="Vassallo">Lazzaro Vassallo</name><affiliation><nlm:aff id="aff3">Department of Financial and Statistical Sciences, University of Salerno, Salerno, Italy</nlm:aff></affiliation></author><author><name sortKey="Angeletti, Silvia" sort="Angeletti, Silvia" uniqKey="Angeletti S" first="Silvia" last="Angeletti">Silvia Angeletti</name><affiliation><nlm:aff id="aff4">Unit of Clinical Laboratory Science, University Campus Bio-Medico of Rome, Italy</nlm:aff></affiliation></author><author><name sortKey="Ciccozzi, Massimo" sort="Ciccozzi, Massimo" uniqKey="Ciccozzi M" first="Massimo" last="Ciccozzi">Massimo Ciccozzi</name><affiliation><nlm:aff id="aff2">Laboratório de Flavivírus, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil</nlm:aff></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" wicri:step="Corpus" wicri:corpus="PMC">000563</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Application of the ARIMA model on the COVID-2019 epidemic dataset</title><author><name sortKey="Benvenuto, Domenico" sort="Benvenuto, Domenico" uniqKey="Benvenuto D" first="Domenico" last="Benvenuto">Domenico Benvenuto</name><affiliation><nlm:aff id="aff1">Unit of Medical Statistics and Molecular Epidemiology, University Campus Bio-Medico of Rome, Italy</nlm:aff></affiliation></author><author><name sortKey="Giovanetti, Marta" sort="Giovanetti, Marta" uniqKey="Giovanetti M" first="Marta" last="Giovanetti">Marta Giovanetti</name><affiliation><nlm:aff id="aff2">Laboratório de Flavivírus, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil</nlm:aff></affiliation></author><author><name sortKey="Vassallo, Lazzaro" sort="Vassallo, Lazzaro" uniqKey="Vassallo L" first="Lazzaro" last="Vassallo">Lazzaro Vassallo</name><affiliation><nlm:aff id="aff3">Department of Financial and Statistical Sciences, University of Salerno, Salerno, Italy</nlm:aff></affiliation></author><author><name sortKey="Angeletti, Silvia" sort="Angeletti, Silvia" uniqKey="Angeletti S" first="Silvia" last="Angeletti">Silvia Angeletti</name><affiliation><nlm:aff id="aff4">Unit of Clinical Laboratory Science, University Campus Bio-Medico of Rome, Italy</nlm:aff></affiliation></author><author><name sortKey="Ciccozzi, Massimo" sort="Ciccozzi, Massimo" uniqKey="Ciccozzi M" first="Massimo" last="Ciccozzi">Massimo Ciccozzi</name><affiliation><nlm:aff id="aff2">Laboratório de Flavivírus, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil</nlm:aff></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><body><p id="p0010"><table-wrap position="float" id="undtbl1"><caption><p>Specifications Table</p></caption><table frame="hsides" rules="groups"><tbody><tr><td>Subject</td><td>Infectious Diseases</td></tr><tr><td>Specific subject area</td><td>Econometric models applied to infectious diseases epidemiological data to forecast the prevalence and incidence of COVID-2019</td></tr><tr><td>Type of data</td><td>Chart<break></break>Graph<break></break>Figure</td></tr><tr><td>How data were acquired</td><td>Gretl 2019d <ext-link ext-link-type="uri" xlink:href="http://gretl.sourceforge.net/win32/index_it.html" id="intref0010">http://gretl.sourceforge.net/win32/index_it.html</ext-link></td></tr><tr><td>Data format</td><td>Data are in raw format and have been analyzed. An Excel file with data has been uploaded.</td></tr><tr><td>Parameters for data collection</td><td>Parameters used for ARIMA were model ARIMA (1,2,0) and ARIMA (1,0,4)</td></tr><tr><td>Description of data collection</td><td>The daily prevalence data of COVID-2019 from January 20, 2020 to February 10, 2020 were collected from the official website of Johns Hopkins university (<ext-link ext-link-type="uri" xlink:href="https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html" id="intref0015">https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html</ext-link>), and Excel 2019 was used to build a time-series database. Descriptive analysis of the data was performed, and to evaluate the incidence of new confirmed cases of COVID-2019 and to prevent eventual bias, the difference between the cases confirmed on that day and the cases confirmed on the previous day were calculated Δ(X<sub>n</sub>-X<sub>n-1</sub>).</td></tr><tr><td>Data source location</td><td>University Campus Bio-Medico of Rome</td></tr><tr><td>Data accessibility</td><td>Raw data can be retrieved from the Github repository <ext-link ext-link-type="uri" xlink:href="https://github.com/CSSEGISandData/COVID-19" id="intref0020">https://github.com/CSSEGISandData/COVID-19</ext-link></td></tr></tbody></table></table-wrap><table-wrap position="float" id="undtbl2"><table frame="hsides" rules="groups"><tbody><tr><td><bold>Value of the Data</bold><list list-type="simple" id="ulist0010"><list-item id="u0010"><label>•</label><p id="p0015">These data are useful because they provide a forecast for COVID-2019 epidemic, thus representing a valid and objective tool for monitoring infection control.</p></list-item><list-item id="u0015"><label>•</label><p id="p0020">All institutions involved in public health and infection control can benefit from these data because by using this model, they can daily construct a reliable forecast for COVID-2019 epidemic.</p></list-item><list-item id="u0020"><label>•</label><p id="p0025">The additional value of these data lies in their easy collection and in the possibility to provide valid forecast for COVID-2019 daily monitoring after the application of the ARIMA model.</p></list-item><list-item id="u0025"><label>•</label><p id="p0030">These data represent an easy way to evaluate the transmission dynamics of COVID-2019 to verify whether the strategy plan for infection control or quarantine is efficient.</p></list-item></list></td></tr></tbody></table></table-wrap></p><sec id="sec1"><label>1</label><title>Data description</title><p id="p0035">The daily prevalence data of COVID-2019 from January 20, 2020 to February 10, 2020 were collected from the official website of Johns Hopkins University (<ext-link ext-link-type="uri" xlink:href="https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html" id="intref0025">https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html</ext-link>), and Excel 2019 was used to build a time-series database [<xref rid="bib1" ref-type="bibr">1</xref>]. ARIMA model was applied to a dataset consisting of 22 number determinations. <xref rid="fig1" ref-type="fig">Fig. 1</xref> shows that the overall prevalence of COVID-2019 presented an increasing trend that is reaching the epidemic plateau. The difference between cases of one day and cases of the previous day Δ(Xn-Xn-1) showed a nonconstant increase in the number of confirmed cases. Descriptive analysis of the data was performed to evaluate the incidence of new confirmed cases of COVID-2019 and to prevent eventual bias.<fig id="fig1"><label>Fig. 1</label><caption><p>Correlogram and ARIMA forecast graph for the 2019-nCoV prevalence.</p></caption><alt-text id="alttext0015">Fig. 1</alt-text><graphic xlink:href="gr1"></graphic></fig></p></sec><sec id="sec2"><label>2</label><title>Experimental design, materials, and methods</title><p id="p0040">The ARIMA model includes autoregressive (AR) model, moving average (MA) model, and seasonal autoregressive integrated moving average (SARIMA) model [<xref rid="bib2" ref-type="bibr">2</xref>]. The Augmented Dickey-Fuller (ADF) [<xref rid="bib3" ref-type="bibr">3</xref>] unit-root test helps in estimating whether the time series is stationary. Log transformation and differences are the preferred approaches to stabilize the time series [<xref rid="bib4" ref-type="bibr">4</xref>]. Seasonal and nonseasonal differences were used to stabilize the term trend and periodicity.</p><p id="p0045">Parameters of the ARIMA model were estimated by autocorrelation function (ACF) graph and partial autocorrelation (PACF) correlogram. To determine the prevalence of COVID-2019, ARIMA (1,0,4) was selected as the best ARIMA model, while ARIMA (1,0,3) was selected as the best ARIMA model for determining the incidence of COVID-2019. Gretl2019d statistical software [<xref rid="bib5" ref-type="bibr">5</xref>] was used to perform statistical analysis on the prevalence and incidence datasets, and the statistical significance level was set at 0.05. A previous study was considered as reference for the methodology of the analysis [<xref rid="bib6" ref-type="bibr">6</xref>].</p><p id="p0050">Logarithmic transformation was performed to evaluate the influence of seasonality on the forecast. The correlogram reporting the ACF and PACF showed that both prevalence and incidence of COVID-2019 are not influenced by the seasonality. The forecast of prevalence and incidence data with relative 95% confidence intervals are reported in <xref rid="tbl1" ref-type="table">Table 1</xref>.<table-wrap position="float" id="tbl1"><label>Table 1</label><caption><p>Forecast value for the 2 days after the analysis for the prevalence and for the incidence of the COVID-2019.</p></caption><alt-text id="alttext0025">Table 1</alt-text><table frame="hsides" rules="groups"><thead><tr><th></th><th>Date</th><th>Forecast</th><th>95% Confidence Interval</th></tr></thead><tbody><tr><td rowspan="2">Prevalence</td><td>11/02/2020</td><td>43599.71</td><td>42347.53–44851.9</td></tr><tr><td>12/02/2020</td><td>45151.45</td><td>42084.88–48218.02</td></tr><tr><td rowspan="2">Incidence</td><td>11/02/2020</td><td>2070.66</td><td>1305.23–2836.09</td></tr><tr><td>12/02/2020</td><td>2418.47</td><td>1534.43–3302.51</td></tr></tbody></table></table-wrap></p><p id="p0055">Although more data are needed to have a more detailed prevision, the spread of the virus seems to be slightly decreasing. Moreover, although the number of confirmed cases is still increasing, the incidence is slightly decreasing. If the virus does not develop new mutations, the number of cases should reach a plateau (<xref rid="fig1" ref-type="fig">Fig. 1</xref>, <xref rid="fig2" ref-type="fig">Fig. 2</xref>). The forecast and the estimate obtained are influenced by the “case” definition and the modality of data collection. For further comparison or for future perspective, case definition and data collection must be maintained in real time.<fig id="fig2"><label>Fig. 2</label><caption><p>Correlogram and ARIMA forecast graph for the 2019-nCoV incidence.</p></caption><alt-text id="alttext0020">Fig. 2</alt-text><graphic xlink:href="gr2"></graphic></fig></p></sec></body><back><ref-list id="cebib0010"><title>References</title><ref id="bib1"><label>1</label><element-citation publication-type="book" id="sref1"><source>Johns Hopkins University Center for Systems Science and Engineering</source><year>2019</year><ext-link ext-link-type="uri" xlink:href="https://github.com/CSSEGISandData/COVID-19" id="intref0010a">https://github.com/CSSEGISandData/COVID-19</ext-link></element-citation></ref><ref id="bib2"><label>2</label><element-citation publication-type="journal" id="sref2"><person-group person-group-type="author"><name><surname>Fattah</surname><given-names>J.</given-names></name><name><surname>Ezzine</surname><given-names>L.</given-names></name><name><surname>Aman</surname><given-names>Z.</given-names></name><name><surname>El Moussami</surname><given-names>H.</given-names></name><name><surname>Lachhab</surname><given-names>A.</given-names></name></person-group><article-title>Forecasting of demand using ARIMA model</article-title><source>Int. J. Eng. Bus. Manag.</source><volume>10</volume><year>2018</year></element-citation></ref><ref id="bib3"><label>3</label><element-citation publication-type="journal" id="sref3"><person-group person-group-type="author"><name><surname>Cao</surname><given-names>S.</given-names></name><name><surname>Wang</surname><given-names>F.</given-names></name><name><surname>Tam</surname><given-names>W.</given-names></name><name><surname>Tse</surname><given-names>L.A.</given-names></name><name><surname>Kim</surname><given-names>J.H.</given-names></name><name><surname>Liu</surname><given-names>J.</given-names></name><name><surname>Lu</surname><given-names>Z.</given-names></name></person-group><article-title>A hybrid seasonal prediction model for tuberculosis incidence in China</article-title><source>BMC Med. Inf. Decis. Making</source><volume>13</volume><year>2013</year><fpage>56</fpage></element-citation></ref><ref id="bib4"><label>4</label><element-citation publication-type="journal" id="sref4"><person-group person-group-type="author"><name><surname>Cheung</surname><given-names>Y.-W.</given-names></name><name><surname>Lai</surname><given-names>K.S.</given-names></name></person-group><article-title>Lag order and critical values of the augmented Dickey–Fuller test</article-title><source>J. Bus. Econ. Stat.</source><volume>13</volume><year>1995</year><fpage>277</fpage><lpage>280</lpage></element-citation></ref><ref id="bib5"><label>5</label><element-citation publication-type="journal" id="sref5"><person-group person-group-type="author"><name><surname>Baiocchi</surname><given-names>G.</given-names></name><name><surname>Distaso</surname><given-names>W.</given-names></name></person-group><article-title>GRETL: econometric software for the GNU generation</article-title><source>J. Appl. Econom.</source><volume>18</volume><year>2003</year><fpage>105</fpage><lpage>110</lpage></element-citation></ref><ref id="bib6"><label>6</label><element-citation publication-type="journal" id="sref6"><person-group person-group-type="author"><name><surname>Wang</surname><given-names>Y.W.</given-names></name><name><surname>Shen</surname><given-names>Z.Z.</given-names></name><name><surname>Jiang</surname><given-names>Y.</given-names></name></person-group><article-title>Comparison of ARIMA and GM(1,1) models for prediction of hepatitis B in China</article-title><source>PloS One</source><volume>13</volume><year>2018</year><fpage>e0201987</fpage><pub-id pub-id-type="pmid">30180159</pub-id></element-citation></ref></ref-list><sec id="appsec1"><title>Conflict of Interest</title><p id="p0060">The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.</p></sec><sec id="appsec2" sec-type="supplementary-material"><label>Appendix A</label><title>Supplementary data</title><p id="p0065">The following is the Supplementary data to this article:<supplementary-material content-type="local-data" id="mmc1"><caption><title>Multimedia component 1</title></caption><media xlink:href="mmc1.xlsx"><alt-text>Multimedia component 1</alt-text></media></supplementary-material></p></sec><fn-group><fn id="appsec3" fn-type="supplementary-material"><label>Appendix A</label><p id="p0070">Supplementary data to this article can be found online at <ext-link ext-link-type="uri" xlink:href="https://doi.org/10.1016/j.dib.2020.105340" id="intref0030">https://doi.org/10.1016/j.dib.2020.105340</ext-link>.</p></fn></fn-group></back></pmc></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/Sante/explor/CovidV2/Data/Pmc/Corpus
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000563 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Pmc/Corpus/biblio.hfd -nk 000563 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Wicri/Sante
|area= CovidV2
|flux= Pmc
|étape= Corpus
|type= RBID
|clé= PMC:7063124
|texte= Application of the ARIMA model on the COVID-2019 epidemic dataset
}}
Pour générer des pages wiki
HfdIndexSelect -h $EXPLOR_AREA/Data/Pmc/Corpus/RBID.i -Sk "pubmed:32181302" \
| HfdSelect -Kh $EXPLOR_AREA/Data/Pmc/Corpus/biblio.hfd \
| NlmPubMed2Wicri -a CovidV2
| This area was generated with Dilib version V0.6.33. |  |