Predicting the international spread of Middle East respiratory syndrome (MERS).
Identifieur interne : 001028 ( PubMed/Corpus ); précédent : 001027; suivant : 001029Predicting the international spread of Middle East respiratory syndrome (MERS).
Auteurs : Kyeongah Nah ; Shiori Otsuki ; Gerardo Chowell ; Hiroshi NishiuraSource :
- BMC infectious diseases [ 1471-2334 ] ; 2016.
English descriptors
- KwdEn :
- MESH :
- epidemiology : Coronavirus Infections, Saudi Arabia.
- transmission : Coronavirus Infections.
- Aircraft, Global Health, Humans, Incidence, Middle East Respiratory Syndrome Coronavirus, Models, Statistical, Risk Assessment, Travel.
Abstract
The Middle East respiratory syndrome (MERS) associated coronavirus has been imported via travelers into multiple countries around the world. In order to support risk assessment practice, the present study aimed to devise a novel statistical model to quantify the country-level risk of experiencing an importation of MERS case.
DOI: 10.1186/s12879-016-1675-z
PubMed: 27449387
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Predicting the international spread of Middle East respiratory syndrome (MERS).</title><author><name sortKey="Nah, Kyeongah" sort="Nah, Kyeongah" uniqKey="Nah K" first="Kyeongah" last="Nah">Kyeongah Nah</name><affiliation><nlm:affiliation>Bolyai Institute, University of Szeged, Aradi vértanúk tere 1, Szeged, H-6720, Hungary.</nlm:affiliation></affiliation></author><author><name sortKey="Otsuki, Shiori" sort="Otsuki, Shiori" uniqKey="Otsuki S" first="Shiori" last="Otsuki">Shiori Otsuki</name><affiliation><nlm:affiliation>Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 1130033, Japan.</nlm:affiliation></affiliation></author><author><name sortKey="Chowell, Gerardo" sort="Chowell, Gerardo" uniqKey="Chowell G" first="Gerardo" last="Chowell">Gerardo Chowell</name><affiliation><nlm:affiliation>School of Public Health, Georgia State University, Atlanta, Georgia, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Nishiura, Hiroshi" sort="Nishiura, Hiroshi" uniqKey="Nishiura H" first="Hiroshi" last="Nishiura">Hiroshi Nishiura</name><affiliation><nlm:affiliation>Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 1130033, Japan. nishiurah@m.u-tokyo.ac.jp.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2016">2016</date><idno type="RBID">pubmed:27449387</idno><idno type="pmid">27449387</idno><idno type="doi">10.1186/s12879-016-1675-z</idno><idno type="wicri:Area/PubMed/Corpus">001028</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">001028</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Predicting the international spread of Middle East respiratory syndrome (MERS).</title><author><name sortKey="Nah, Kyeongah" sort="Nah, Kyeongah" uniqKey="Nah K" first="Kyeongah" last="Nah">Kyeongah Nah</name><affiliation><nlm:affiliation>Bolyai Institute, University of Szeged, Aradi vértanúk tere 1, Szeged, H-6720, Hungary.</nlm:affiliation></affiliation></author><author><name sortKey="Otsuki, Shiori" sort="Otsuki, Shiori" uniqKey="Otsuki S" first="Shiori" last="Otsuki">Shiori Otsuki</name><affiliation><nlm:affiliation>Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 1130033, Japan.</nlm:affiliation></affiliation></author><author><name sortKey="Chowell, Gerardo" sort="Chowell, Gerardo" uniqKey="Chowell G" first="Gerardo" last="Chowell">Gerardo Chowell</name><affiliation><nlm:affiliation>School of Public Health, Georgia State University, Atlanta, Georgia, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Nishiura, Hiroshi" sort="Nishiura, Hiroshi" uniqKey="Nishiura H" first="Hiroshi" last="Nishiura">Hiroshi Nishiura</name><affiliation><nlm:affiliation>Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 1130033, Japan. nishiurah@m.u-tokyo.ac.jp.</nlm:affiliation></affiliation></author></analytic><series><title level="j">BMC infectious diseases</title><idno type="eISSN">1471-2334</idno><imprint><date when="2016" type="published">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Aircraft</term><term>Coronavirus Infections (epidemiology)</term><term>Coronavirus Infections (transmission)</term><term>Global Health</term><term>Humans</term><term>Incidence</term><term>Middle East Respiratory Syndrome Coronavirus</term><term>Models, Statistical</term><term>Risk Assessment</term><term>Saudi Arabia (epidemiology)</term><term>Travel</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Coronavirus Infections</term><term>Saudi Arabia</term></keywords><keywords scheme="MESH" qualifier="transmission" xml:lang="en"><term>Coronavirus Infections</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Aircraft</term><term>Global Health</term><term>Humans</term><term>Incidence</term><term>Middle East Respiratory Syndrome Coronavirus</term><term>Models, Statistical</term><term>Risk Assessment</term><term>Travel</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The Middle East respiratory syndrome (MERS) associated coronavirus has been imported via travelers into multiple countries around the world. In order to support risk assessment practice, the present study aimed to devise a novel statistical model to quantify the country-level risk of experiencing an importation of MERS case.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">27449387</PMID><DateCompleted><Year>2017</Year><Month>06</Month><Day>30</Day></DateCompleted><DateRevised><Year>2018</Year><Month>12</Month><Day>02</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1471-2334</ISSN><JournalIssue CitedMedium="Internet"><Volume>16</Volume><PubDate><Year>2016</Year><Month>07</Month><Day>22</Day></PubDate></JournalIssue><Title>BMC infectious diseases</Title><ISOAbbreviation>BMC Infect. Dis.</ISOAbbreviation></Journal><ArticleTitle>Predicting the international spread of Middle East respiratory syndrome (MERS).</ArticleTitle><Pagination><MedlinePgn>356</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/s12879-016-1675-z</ELocationID><Abstract><AbstractText Label="BACKGROUND">The Middle East respiratory syndrome (MERS) associated coronavirus has been imported via travelers into multiple countries around the world. In order to support risk assessment practice, the present study aimed to devise a novel statistical model to quantify the country-level risk of experiencing an importation of MERS case.</AbstractText><AbstractText Label="METHODS">We analyzed the arrival time of each reported MERS importation around the world, i.e., the date on which imported cases entered a specific country, which was modeled as a dependent variable in our analysis. We also used openly accessible data including the airline transportation network to parameterize a hazard-based risk prediction model. The hazard was assumed to follow an inverse function of the effective distance (i.e., the minimum effective length of a path from origin to destination), which was calculated from the airline transportation data, from Saudi Arabia to each country. Both country-specific religion and the incidence data of MERS in Saudi Arabia were used to improve our model prediction.</AbstractText><AbstractText Label="RESULTS">Our estimates of the risk of MERS importation appeared to be right skewed, which facilitated the visual identification of countries at highest risk of MERS importations in the right tail of the distribution. The simplest model that relied solely on the effective distance yielded the best predictive performance (Area under the curve (AUC) = 0.943) with 100 % sensitivity and 79.6 % specificity. Out of the 30 countries estimated to be at highest risk of MERS case importation, 17 countries (56.7 %) have already reported at least one importation of MERS. Although model fit measured by Akaike Information Criterion (AIC) was improved by including country-specific religion (i.e. Muslim majority country), the predictive performance as measured by AUC was not improved after accounting for this covariate.</AbstractText><AbstractText Label="CONCLUSIONS">Our relatively simple statistical model based on the effective distance derived from the airline transportation network data was found to help predicting the risk of importing MERS at the country level. The successful application of the effective distance model to predict MERS importations, particularly when computationally intensive large-scale transmission models may not be immediately applicable could have been benefited from the particularly low transmissibility of the MERS coronavirus.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Nah</LastName><ForeName>Kyeongah</ForeName><Initials>K</Initials><AffiliationInfo><Affiliation>Bolyai Institute, University of Szeged, Aradi vértanúk tere 1, Szeged, H-6720, Hungary.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 1130033, Japan.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Graduate School of Medicine, Hokkaido University, Kita 15 Jo Nishi 7 Chome, Kita-ku, Sapporo-shi, Hokkaido, 060-8638, Japan.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Otsuki</LastName><ForeName>Shiori</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 1130033, Japan.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>CREST, Japan Science and Technology Agency, Honcho 4-1-8, Kawaguchi, Saitama, 332-0012, Japan.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Chowell</LastName><ForeName>Gerardo</ForeName><Initials>G</Initials><AffiliationInfo><Affiliation>School of Public Health, Georgia State University, Atlanta, Georgia, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Nishiura</LastName><ForeName>Hiroshi</ForeName><Initials>H</Initials><Identifier Source="ORCID">0000-0003-0941-8537</Identifier><AffiliationInfo><Affiliation>Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 1130033, Japan. nishiurah@m.u-tokyo.ac.jp.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>CREST, Japan Science and Technology Agency, Honcho 4-1-8, Kawaguchi, Saitama, 332-0012, Japan. nishiurah@m.u-tokyo.ac.jp.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Graduate School of Medicine, Hokkaido University, Kita 15 Jo Nishi 7 Chome, Kita-ku, Sapporo-shi, Hokkaido, 060-8638, Japan. nishiurah@m.u-tokyo.ac.jp.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>BB/M008894/1</GrantID><Agency>Biotechnology and Biological Sciences Research Council</Agency><Country>United Kingdom</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate 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