Quantifying spatiotemporal heterogeneity of MERS-CoV transmission in the Middle East region: A combined modelling approach.
Identifieur interne : 000F30 ( PubMed/Checkpoint ); précédent : 000F29; suivant : 000F31Quantifying spatiotemporal heterogeneity of MERS-CoV transmission in the Middle East region: A combined modelling approach.
Auteurs : Chiara Poletto [France] ; Vittoria Colizza [Italie] ; Pierre-Yves Boëlle [France]Source :
- Epidemics [ 1878-0067 ] ; 2016.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- epidemiology : Coronavirus Infections, Middle East.
- transmission : Coronavirus Infections.
- Epidemics, Humans, Incidence, Middle East Respiratory Syndrome Coronavirus, Spatio-Temporal Analysis, Stochastic Processes.
Abstract
MERS coronavirus cases notified in the Middle East region since the identification of the virus in 2012 have displayed variations in time and across geography. Through a combined modelling approach, we estimate the rates of generation of cases along the zoonotic and human-to-human transmission routes and assess their spatiotemporal heterogeneity. We consider all cases notified to WHO from March 2012 to mid-September 2014. We use a stochastic modelling of the time series of case incidence in the Middle East region to estimate time- and space-dependent zoonotic and human-to-human transmission parameters. The model also accounts for possible lack of identification of secondary transmissions among notified cases. This approach is combined with the analysis of imported cases out of the region to assess the rate of underreporting of cases. Out of a total of 32 possible models, based on different parameterisation and scenario considered, the best-fit model is characterised by a large heterogeneity in time and across space for both zoonotic and human-to-human transmission. The variation in time that occurred during Spring 2014 led to a 17-fold and 3-fold increase in the two transmissions, respectively, bringing the reproductive rate to values above 1 during that period for all regions under study. The model suggests that 75% of MERS-CoV cases are secondary cases (human-to-human transmission), which is substantially higher than the 34% of reported cases with an epidemiological link to another case. Overall, estimated reporting rate is 0.26. Our findings show a higher level of spatial heterogeneity in zoonotic transmission compared to human-to-human, highlighting the strong environmental component of the epidemic. Since sporadic introductions are predicted to be a small proportion of notified cases and are responsible for triggering secondary transmissions, a more comprehensive understanding of zoonotic source and path of transmission could be critical to limit the epidemic spread.
DOI: 10.1016/j.epidem.2015.12.001
PubMed: 27266844
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Through a combined modelling approach, we estimate the rates of generation of cases along the zoonotic and human-to-human transmission routes and assess their spatiotemporal heterogeneity. We consider all cases notified to WHO from March 2012 to mid-September 2014. We use a stochastic modelling of the time series of case incidence in the Middle East region to estimate time- and space-dependent zoonotic and human-to-human transmission parameters. The model also accounts for possible lack of identification of secondary transmissions among notified cases. This approach is combined with the analysis of imported cases out of the region to assess the rate of underreporting of cases. Out of a total of 32 possible models, based on different parameterisation and scenario considered, the best-fit model is characterised by a large heterogeneity in time and across space for both zoonotic and human-to-human transmission. The variation in time that occurred during Spring 2014 led to a 17-fold and 3-fold increase in the two transmissions, respectively, bringing the reproductive rate to values above 1 during that period for all regions under study. The model suggests that 75% of MERS-CoV cases are secondary cases (human-to-human transmission), which is substantially higher than the 34% of reported cases with an epidemiological link to another case. Overall, estimated reporting rate is 0.26. Our findings show a higher level of spatial heterogeneity in zoonotic transmission compared to human-to-human, highlighting the strong environmental component of the epidemic. Since sporadic introductions are predicted to be a small proportion of notified cases and are responsible for triggering secondary transmissions, a more comprehensive understanding of zoonotic source and path of transmission could be critical to limit the epidemic spread.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">27266844</PMID><DateCompleted><Year>2018</Year><Month>02</Month><Day>07</Day></DateCompleted><DateRevised><Year>2020</Year><Month>04</Month><Day>01</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1878-0067</ISSN><JournalIssue CitedMedium="Internet"><Volume>15</Volume><PubDate><Year>2016</Year><Month>06</Month></PubDate></JournalIssue><Title>Epidemics</Title><ISOAbbreviation>Epidemics</ISOAbbreviation></Journal><ArticleTitle>Quantifying spatiotemporal heterogeneity of MERS-CoV transmission in the Middle East region: A combined modelling approach.</ArticleTitle><Pagination><MedlinePgn>1-9</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.epidem.2015.12.001</ELocationID><ELocationID EIdType="pii" ValidYN="Y">S1755-4365(15)00096-1</ELocationID><Abstract><AbstractText>MERS coronavirus cases notified in the Middle East region since the identification of the virus in 2012 have displayed variations in time and across geography. Through a combined modelling approach, we estimate the rates of generation of cases along the zoonotic and human-to-human transmission routes and assess their spatiotemporal heterogeneity. We consider all cases notified to WHO from March 2012 to mid-September 2014. We use a stochastic modelling of the time series of case incidence in the Middle East region to estimate time- and space-dependent zoonotic and human-to-human transmission parameters. The model also accounts for possible lack of identification of secondary transmissions among notified cases. This approach is combined with the analysis of imported cases out of the region to assess the rate of underreporting of cases. Out of a total of 32 possible models, based on different parameterisation and scenario considered, the best-fit model is characterised by a large heterogeneity in time and across space for both zoonotic and human-to-human transmission. The variation in time that occurred during Spring 2014 led to a 17-fold and 3-fold increase in the two transmissions, respectively, bringing the reproductive rate to values above 1 during that period for all regions under study. The model suggests that 75% of MERS-CoV cases are secondary cases (human-to-human transmission), which is substantially higher than the 34% of reported cases with an epidemiological link to another case. Overall, estimated reporting rate is 0.26. Our findings show a higher level of spatial heterogeneity in zoonotic transmission compared to human-to-human, highlighting the strong environmental component of the epidemic. Since sporadic introductions are predicted to be a small proportion of notified cases and are responsible for triggering secondary transmissions, a more comprehensive understanding of zoonotic source and path of transmission could be critical to limit the epidemic spread.</AbstractText><CopyrightInformation>Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Poletto</LastName><ForeName>Chiara</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>Sorbonne Universités, UPMC Univ Paris 06, INSERM, Institut Pierre Louis d'épidémiologie et de Santé Publique (IPLESP UMRS 1136), F75012, 27 rue Chaligny, Paris 75012, France. 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IdType="pubmed">24239323</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Am J Epidemiol. 2004 Sep 15;160(6):509-16</Citation><ArticleIdList><ArticleId IdType="pubmed">15353409</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Lancet Infect Dis. 2015 May;15(5):559-64</Citation><ArticleIdList><ArticleId IdType="pubmed">25863564</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Euro Surveill. 2014 Jun 12;19(23):</Citation><ArticleIdList><ArticleId IdType="pubmed">24957746</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Lancet. 2013 Aug 24;382(9893):694-9</Citation><ArticleIdList><ArticleId IdType="pubmed">23831141</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>N Engl J Med. 2013 Aug 29;369(9):884-6</Citation><ArticleIdList><ArticleId IdType="pubmed">23923992</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Emerg Infect Dis. 2015 Jun;21(6):1019-22</Citation><ArticleIdList><ArticleId IdType="pubmed">25989145</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Euro Surveill. 2015 Jul 09;20(27):</Citation><ArticleIdList><ArticleId IdType="pubmed">26212063</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>PLoS Curr. 2014 Dec 18;6:</Citation><ArticleIdList><ArticleId IdType="pubmed">25685622</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>mBio. 2014 Feb 25;5(2):e00884-14</Citation><ArticleIdList><ArticleId IdType="pubmed">24570370</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Int J Infect Dis. 2014 Dec;29:301-6</Citation><ArticleIdList><ArticleId IdType="pubmed">25303830</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>N Engl J Med. 2013 Aug 1;369(5):407-16</Citation><ArticleIdList><ArticleId IdType="pubmed">23782161</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>PLoS Curr. 2013 Nov 12;5:</Citation><ArticleIdList><ArticleId IdType="pubmed">24270606</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Euro Surveill. 2015 Jun 25;20(25):14-8</Citation><ArticleIdList><ArticleId IdType="pubmed">26132768</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Proc Natl Acad Sci U S A. 2009 Dec 22;106(51):21484-9</Citation><ArticleIdList><ArticleId IdType="pubmed">20018697</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Epidemics. 2014 Dec;9:40-51</Citation><ArticleIdList><ArticleId IdType="pubmed">25480133</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Clin Infect Dis. 2015 Feb 1;60(3):369-77</Citation><ArticleIdList><ArticleId IdType="pubmed">25323704</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>BMC Med. 2009 Sep 10;7:45</Citation><ArticleIdList><ArticleId IdType="pubmed">19744314</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Trends Microbiol. 2014 Oct;22(10):573-9</Citation><ArticleIdList><ArticleId IdType="pubmed">25178651</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Clin Microbiol Infect. 2014 May;20(5):469-74</Citation><ArticleIdList><ArticleId IdType="pubmed">24460984</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>N Engl J Med. 2015 Feb 26;372(9):846-54</Citation><ArticleIdList><ArticleId IdType="pubmed">25714162</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Zoonoses Public Health. 2016 Feb;63(1):1-9</Citation><ArticleIdList><ArticleId IdType="pubmed">25545147</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>France</li><li>Italie</li></country><region><li>Piémont</li><li>Île-de-France</li></region><settlement><li>Turin</li></settlement></list><tree><country name="France"><region name="Île-de-France"><name sortKey="Poletto, Chiara" sort="Poletto, Chiara" uniqKey="Poletto C" first="Chiara" last="Poletto">Chiara Poletto</name></region><name sortKey="Boelle, Pierre Yves" sort="Boelle, Pierre Yves" uniqKey="Boelle P" first="Pierre-Yves" last="Boëlle">Pierre-Yves Boëlle</name></country><country name="Italie"><region name="Piémont"><name sortKey="Colizza, Vittoria" sort="Colizza, Vittoria" uniqKey="Colizza V" first="Vittoria" last="Colizza">Vittoria 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