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Quantifying spatiotemporal heterogeneity of MERS-CoV transmission in the Middle East region: A combined modelling approach

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 route...

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Autores principales: Poletto, Chiara, Colizza, Vittoria, Boëlle, Pierre-Yves
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Authors. Published by Elsevier B.V. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7104927/
https://www.ncbi.nlm.nih.gov/pubmed/27266844
http://dx.doi.org/10.1016/j.epidem.2015.12.001
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author Poletto, Chiara
Colizza, Vittoria
Boëlle, Pierre-Yves
author_facet Poletto, Chiara
Colizza, Vittoria
Boëlle, Pierre-Yves
author_sort Poletto, Chiara
collection PubMed
description 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.
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spelling pubmed-71049272020-03-31 Quantifying spatiotemporal heterogeneity of MERS-CoV transmission in the Middle East region: A combined modelling approach Poletto, Chiara Colizza, Vittoria Boëlle, Pierre-Yves Epidemics Article 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. The Authors. Published by Elsevier B.V. 2016-06 2015-12-17 /pmc/articles/PMC7104927/ /pubmed/27266844 http://dx.doi.org/10.1016/j.epidem.2015.12.001 Text en © 2015 The Authors Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Poletto, Chiara
Colizza, Vittoria
Boëlle, Pierre-Yves
Quantifying spatiotemporal heterogeneity of MERS-CoV transmission in the Middle East region: A combined modelling approach
title Quantifying spatiotemporal heterogeneity of MERS-CoV transmission in the Middle East region: A combined modelling approach
title_full Quantifying spatiotemporal heterogeneity of MERS-CoV transmission in the Middle East region: A combined modelling approach
title_fullStr Quantifying spatiotemporal heterogeneity of MERS-CoV transmission in the Middle East region: A combined modelling approach
title_full_unstemmed Quantifying spatiotemporal heterogeneity of MERS-CoV transmission in the Middle East region: A combined modelling approach
title_short Quantifying spatiotemporal heterogeneity of MERS-CoV transmission in the Middle East region: A combined modelling approach
title_sort quantifying spatiotemporal heterogeneity of mers-cov transmission in the middle east region: a combined modelling approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7104927/
https://www.ncbi.nlm.nih.gov/pubmed/27266844
http://dx.doi.org/10.1016/j.epidem.2015.12.001
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