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 : 000874 ( PubMed/Curation ); précédent : 000873; suivant : 000875Underlying 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 [Arabie saoudite] ; Anwar E. Ahmed [Arabie saoudite]Source :
- Epidemiology and infection [ 1469-4409 ] ; 2018.
Descripteurs français
- KwdFr :
- Animaux, Chameaux, Coronavirus du syndrome respiratoire du Moyen-Orient, Facteurs de l'âge, Facteurs de risque, Facteurs sexuels, Facteurs temps, Femelle, Humains, Infections à coronavirus (épidémiologie), Infections à coronavirus (étiologie), Modèles linéaires, Mâle, Prévision, Saisons, Santé mondiale.
- MESH :
- épidémiologie : Infections à coronavirus.
- étiologie : Infections à coronavirus.
- Animaux, Chameaux, Coronavirus du syndrome respiratoire du Moyen-Orient, Facteurs de l'âge, Facteurs de risque, Facteurs sexuels, Facteurs temps, Femelle, Humains, Modèles linéaires, Mâle, Prévision, Saisons, Santé mondiale.
English descriptors
- KwdEn :
- MESH :
- epidemiology : Coronavirus Infections.
- etiology : Coronavirus Infections.
- Age Factors, Animals, Camelus, Female, Forecasting, Global Health, Humans, Linear Models, Male, Middle East Respiratory Syndrome Coronavirus, Risk Factors, Seasons, Sex Factors, Time Factors.
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 (P = 0.026 for monthly series and P = 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.
DOI: 10.1017/S0950268818001541
PubMed: 29886854
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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 (P = 0.026 for monthly series and P = 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.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">29886854</PMID><DateCompleted><Year>2019</Year><Month>03</Month><Day>18</Day></DateCompleted><DateRevised><Year>2020</Year><Month>04</Month><Day>03</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1469-4409</ISSN><JournalIssue CitedMedium="Internet"><Volume>146</Volume><Issue>11</Issue><PubDate><Year>2018</Year><Month>08</Month></PubDate></JournalIssue><Title>Epidemiology and infection</Title><ISOAbbreviation>Epidemiol. Infect.</ISOAbbreviation></Journal><ArticleTitle>Underlying trend, seasonality, prediction, forecasting and the contribution of risk factors: an analysis of globally reported cases of Middle East Respiratory Syndrome Coronavirus.</ArticleTitle><Pagination><MedlinePgn>1343-1349</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1017/S0950268818001541</ELocationID><Abstract><AbstractText>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 (P = 0.026 for monthly series and P = 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.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Da'ar</LastName><ForeName>Omar B</ForeName><Initials>OB</Initials><Identifier Source="ORCID">0000-0002-2153-8761</Identifier><AffiliationInfo><Affiliation>King Abdullah International Medical Research Centre (KAIMRC)/College of Public Health & Health Informatics,King Saud bin Abdulaziz University of Health Sciences,National Guard Health Affairs,Riyadh,Saudi Arabia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ahmed</LastName><ForeName>Anwar E</ForeName><Initials>AE</Initials><Identifier Source="ORCID">0000-0001-8743-6007</Identifier><AffiliationInfo><Affiliation>King Abdullah International Medical Research Centre (KAIMRC)/College of Public Health & Health 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