Underlying trend, seasonality, prediction, forecasting and the contribution of risk factors: an analysis of globally reported cases of Middle East Respiratory Syndrome Coronavirus
Identifieur interne : 000443 ( Pmc/Checkpoint ); précédent : 000442; suivant : 000444Underlying trend, seasonality, prediction, forecasting and the contribution of risk factors: an analysis of globally reported cases of Middle East Respiratory Syndrome Coronavirus
Auteurs : Omar B. Da'Ar ; Anwar E. AhmedSource :
- Epidemiology and Infection [ 0950-2688 ] ; 2018.
Abstract
This study set out to identify and analyse trends and seasonal variations of monthly global reported cases of the Middle East respiratory syndrome coronavirus (MERS-CoV). It also made a prediction based on the reported and extrapolated into the future by forecasting the trend. Finally, the study assessed contributions of various risk factors in the reported cases. The motivation for this study is that MERS-CoV remains among the list of blueprint priority and potential pandemic diseases globally. Yet, there is a paucity of empirical literature examining trends and seasonality as the available evidence is generally descriptive and anecdotal. The study is a time series analysis using monthly global reported cases of MERS-CoV by the World Health Organisation between January 2015 and January 2018. We decomposed the series into seasonal, irregular and trend components and identified patterns, smoothened series, generated predictions and employed forecasting techniques based on linear regression. We assessed contributions of various risk factors in MERS-CoV cases over time. Successive months of the MERS-CoV cases suggest a significant decreasing trend (
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DOI: 10.1017/S0950268818001541
PubMed: 29886854
PubMed Central: 7113021
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Underlying trend, seasonality, prediction, forecasting and the contribution of risk factors: an analysis of globally reported cases of Middle East Respiratory Syndrome Coronavirus</title><author><name sortKey="Da Ar, Omar B" sort="Da Ar, Omar B" uniqKey="Da Ar O" first="Omar B." last="Da'Ar">Omar B. Da'Ar</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author><author><name sortKey="Ahmed, Anwar E" sort="Ahmed, Anwar E" uniqKey="Ahmed A" first="Anwar E." last="Ahmed">Anwar E. Ahmed</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">29886854</idno><idno type="pmc">7113021</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7113021</idno><idno type="RBID">PMC:7113021</idno><idno type="doi">10.1017/S0950268818001541</idno><date when="2018">2018</date><idno type="wicri:Area/Pmc/Corpus">000C53</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000C53</idno><idno type="wicri:Area/Pmc/Curation">000C53</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000C53</idno><idno type="wicri:Area/Pmc/Checkpoint">000443</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Checkpoint">000443</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Underlying trend, seasonality, prediction, forecasting and the contribution of risk factors: an analysis of globally reported cases of Middle East Respiratory Syndrome Coronavirus</title><author><name sortKey="Da Ar, Omar B" sort="Da Ar, Omar B" uniqKey="Da Ar O" first="Omar B." last="Da'Ar">Omar B. Da'Ar</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author><author><name sortKey="Ahmed, Anwar E" sort="Ahmed, Anwar E" uniqKey="Ahmed A" first="Anwar E." last="Ahmed">Anwar E. Ahmed</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author></analytic><series><title level="j">Epidemiology and Infection</title><idno type="ISSN">0950-2688</idno><idno type="eISSN">1469-4409</idno><imprint><date when="2018">2018</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>This study set out to identify and analyse trends and seasonal variations of monthly global reported cases of the Middle East respiratory syndrome coronavirus (MERS-CoV). It also made a prediction based on the reported and extrapolated into the future by forecasting the trend. Finally, the study assessed contributions of various risk factors in the reported cases. The motivation for this study is that MERS-CoV remains among the list of blueprint priority and potential pandemic diseases globally. Yet, there is a paucity of empirical literature examining trends and seasonality as the available evidence is generally descriptive and anecdotal. The study is a time series analysis using monthly global reported cases of MERS-CoV by the World Health Organisation between January 2015 and January 2018. We decomposed the series into seasonal, irregular and trend components and identified patterns, smoothened series, generated predictions and employed forecasting techniques based on linear regression. We assessed contributions of various risk factors in MERS-CoV cases over time. Successive months of the MERS-CoV cases suggest a significant decreasing trend (<italic>P</italic> = 0.026 for monthly series and <italic>P</italic> = 0.047 for Quarterly series). The MERS-CoV cases are forecast to wane by end 2018. Seasonality component of the cases oscillated below or above the baseline (the centred moving average), but no association with the series over time was noted. The results revealed contributions of risk factors such as camel contact, male, old age and being from Saudi Arabia and Middle East regions to the overall reported cases of MERS-CoV. The trend component and several risk factors for global MERS-CoV cases, including camel contact, male, age and geography/region significantly affected the series. Our statistical models appear to suggest significant predictive capacity and the findings may well inform healthcare practitioners and policymakers about the underlying dynamics that produced the globally reported MERS-CoV cases.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Zyoud, Sh" uniqKey="Zyoud S">SH Zyoud</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="He, D" uniqKey="He D">D He</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ahmed, Ae" uniqKey="Ahmed A">AE Ahmed</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ahmed, Ae" uniqKey="Ahmed A">AE Ahmed</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Assiri, Aj" uniqKey="Assiri A">AJ Assiri</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Mahallawi, Wh" uniqKey="Mahallawi W">WH 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Oboho</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Gossner, C" uniqKey="Gossner C">C Gossner</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ahmed, Ae" uniqKey="Ahmed A">AE Ahmed</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Memish, Za" uniqKey="Memish Z">ZA Memish</name></author></analytic></biblStruct></listBibl></div1></back></TEI><pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Epidemiol Infect</journal-id><journal-id journal-id-type="iso-abbrev">Epidemiol. Infect</journal-id><journal-id journal-id-type="publisher-id">HYG</journal-id><journal-title-group><journal-title>Epidemiology and Infection</journal-title></journal-title-group><issn pub-type="ppub">0950-2688</issn><issn pub-type="epub">1469-4409</issn><publisher><publisher-name>Cambridge University Press</publisher-name><publisher-loc>Cambridge, UK</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">29886854</article-id><article-id pub-id-type="pmc">7113021</article-id><article-id pub-id-type="doi">10.1017/S0950268818001541</article-id><article-id pub-id-type="pii">S0950268818001541</article-id><article-id pub-id-type="publisher-id">00154</article-id><article-categories><subj-group subj-group-type="heading"><subject>Original Paper</subject></subj-group><subj-group subj-group-type="section"><subject>MERS-CoV</subject></subj-group></article-categories><title-group><article-title>Underlying trend, seasonality, prediction, forecasting and the contribution of risk factors: an analysis of globally reported cases of Middle East Respiratory Syndrome Coronavirus</article-title><alt-title alt-title-type="left-running">Omar B. Da'ar and Anwar E. Ahmed</alt-title><alt-title alt-title-type="right-running"><italic>Epidemiology & Infection</italic></alt-title></title-group><contrib-group><contrib contrib-type="author"><contrib-id contrib-id-type="orcid" authenticated="false">http://orcid.org/0000-0002-2153-8761</contrib-id><name><surname>Da'ar</surname><given-names>Omar B.</given-names></name><xref ref-type="aff" rid="aff1"></xref><xref ref-type="corresp" rid="cor1"></xref></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid" authenticated="false">http://orcid.org/0000-0001-8743-6007</contrib-id><name><surname>Ahmed</surname><given-names>Anwar E.</given-names></name><xref ref-type="aff" rid="aff1"></xref></contrib></contrib-group><aff id="aff1"><addr-line>King Abdullah International Medical Research Centre (KAIMRC)/College of Public Health & Health Informatics</addr-line>,<institution>King Saud bin Abdulaziz University of Health Sciences</institution>,<addr-line>National Guard Health Affairs</addr-line>,<addr-line>Riyadh</addr-line>,<country>Saudi Arabia</country></aff><author-notes><corresp id="cor1"><bold>Author for correspondence:</bold> Omar B. Da'ar, E-mail: <email>odaar@smumn.edu</email></corresp></author-notes><pub-date pub-type="collection"><month>8</month><year>2018</year></pub-date><pub-date pub-type="epub"><day>11</day><month>6</month><year>2018</year></pub-date><volume>146</volume><issue>11</issue><fpage>1343</fpage><lpage>1349</lpage><history><date date-type="received"><day>24</day><month>4</month><year>2018</year></date><date date-type="rev-recd"><day>04</day><month>5</month><year>2018</year></date><date date-type="accepted"><day>15</day><month>5</month><year>2018</year></date></history><permissions><copyright-statement>© Cambridge University Press 2018</copyright-statement><copyright-year>2018</copyright-year><copyright-holder>Cambridge University Press</copyright-holder><license license-type="open-access"><license-p>This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means subject to acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.</license-p></license></permissions><self-uri xlink:title="pdf" xlink:href="S0950268818001541a.pdf"></self-uri><abstract abstract-type="normal"><p>This study set out to identify and analyse trends and seasonal variations of monthly global reported cases of the Middle East respiratory syndrome coronavirus (MERS-CoV). It also made a prediction based on the reported and extrapolated into the future by forecasting the trend. Finally, the study assessed contributions of various risk factors in the reported cases. The motivation for this study is that MERS-CoV remains among the list of blueprint priority and potential pandemic diseases globally. Yet, there is a paucity of empirical literature examining trends and seasonality as the available evidence is generally descriptive and anecdotal. The study is a time series analysis using monthly global reported cases of MERS-CoV by the World Health Organisation between January 2015 and January 2018. We decomposed the series into seasonal, irregular and trend components and identified patterns, smoothened series, generated predictions and employed forecasting techniques based on linear regression. We assessed contributions of various risk factors in MERS-CoV cases over time. Successive months of the MERS-CoV cases suggest a significant decreasing trend (<italic>P</italic> = 0.026 for monthly series and <italic>P</italic> = 0.047 for Quarterly series). The MERS-CoV cases are forecast to wane by end 2018. Seasonality component of the cases oscillated below or above the baseline (the centred moving average), but no association with the series over time was noted. The results revealed contributions of risk factors such as camel contact, male, old age and being from Saudi Arabia and Middle East regions to the overall reported cases of MERS-CoV. The trend component and several risk factors for global MERS-CoV cases, including camel contact, male, age and geography/region significantly affected the series. Our statistical models appear to suggest significant predictive capacity and the findings may well inform healthcare practitioners and policymakers about the underlying dynamics that produced the globally reported MERS-CoV cases.</p></abstract><kwd-group><title>Key words</title><kwd>Forecasting</kwd><kwd>MERS-COV cases</kwd><kwd>prediction</kwd><kwd>risk factors</kwd><kwd>seasonality</kwd><kwd>trend</kwd></kwd-group><counts><fig-count count="2"></fig-count><table-count count="3"></table-count><ref-count count="35"></ref-count><page-count count="7"></page-count></counts></article-meta></front></pmc><affiliations><list></list><tree><noCountry><name sortKey="Ahmed, Anwar E" sort="Ahmed, Anwar E" uniqKey="Ahmed A" first="Anwar E." last="Ahmed">Anwar E. Ahmed</name><name sortKey="Da Ar, Omar B" sort="Da Ar, Omar B" uniqKey="Da Ar O" first="Omar B." last="Da'Ar">Omar B. Da'Ar</name></noCountry></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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