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Characterising routes of H5N1 and H7N9 spread in China using Bayesian phylogeographical analysis

Avian influenza H5N1 subtype has caused a global public health concern due to its high pathogenicity in poultry and high case fatality rates in humans. The recently emerged H7N9 is a growing pandemic risk due to its sustained high rates of human infections, and recently acquired high pathogenicity i...

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Autores principales: Bui, Chau M., Adam, Dillon C., Njoto, Edwin, Scotch, Matthew, MacIntyre, C. Raina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6246557/
https://www.ncbi.nlm.nih.gov/pubmed/30459301
http://dx.doi.org/10.1038/s41426-018-0185-z
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author Bui, Chau M.
Adam, Dillon C.
Njoto, Edwin
Scotch, Matthew
MacIntyre, C. Raina
author_facet Bui, Chau M.
Adam, Dillon C.
Njoto, Edwin
Scotch, Matthew
MacIntyre, C. Raina
author_sort Bui, Chau M.
collection PubMed
description Avian influenza H5N1 subtype has caused a global public health concern due to its high pathogenicity in poultry and high case fatality rates in humans. The recently emerged H7N9 is a growing pandemic risk due to its sustained high rates of human infections, and recently acquired high pathogenicity in poultry. Here, we used Bayesian phylogeography on 265 H5N1 and 371 H7N9 haemagglutinin sequences isolated from humans, animals and the environment, to identify and compare migration patterns and factors predictive of H5N1 and H7N9 diffusion rates in China. H7N9 diffusion dynamics and predictor contributions differ from H5N1. Key determinants of spatial diffusion included: proximity between locations (for H5N1 and H7N9), and lower rural population densities (H5N1 only). For H7N9, additional predictors included low avian influenza vaccination rates, low percentage of nature reserves and high humidity levels. For both H5N1 and H7N9, we found viral migration rates from Guangdong to Guangxi and Guangdong to Hunan were highly supported transmission routes (Bayes Factor > 30). We show fundamental differences in wide-scale transmission dynamics between H5N1 and H7N9. Importantly, this indicates that avian influenza initiatives designed to control H5N1 may not be sufficient for controlling the H7N9 epidemic. We suggest control and prevention activities to specifically target poultry transportation networks between Central, Pan-Pearl River Delta and South-West regions.
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spelling pubmed-62465572018-11-26 Characterising routes of H5N1 and H7N9 spread in China using Bayesian phylogeographical analysis Bui, Chau M. Adam, Dillon C. Njoto, Edwin Scotch, Matthew MacIntyre, C. Raina Emerg Microbes Infect Article Avian influenza H5N1 subtype has caused a global public health concern due to its high pathogenicity in poultry and high case fatality rates in humans. The recently emerged H7N9 is a growing pandemic risk due to its sustained high rates of human infections, and recently acquired high pathogenicity in poultry. Here, we used Bayesian phylogeography on 265 H5N1 and 371 H7N9 haemagglutinin sequences isolated from humans, animals and the environment, to identify and compare migration patterns and factors predictive of H5N1 and H7N9 diffusion rates in China. H7N9 diffusion dynamics and predictor contributions differ from H5N1. Key determinants of spatial diffusion included: proximity between locations (for H5N1 and H7N9), and lower rural population densities (H5N1 only). For H7N9, additional predictors included low avian influenza vaccination rates, low percentage of nature reserves and high humidity levels. For both H5N1 and H7N9, we found viral migration rates from Guangdong to Guangxi and Guangdong to Hunan were highly supported transmission routes (Bayes Factor > 30). We show fundamental differences in wide-scale transmission dynamics between H5N1 and H7N9. Importantly, this indicates that avian influenza initiatives designed to control H5N1 may not be sufficient for controlling the H7N9 epidemic. We suggest control and prevention activities to specifically target poultry transportation networks between Central, Pan-Pearl River Delta and South-West regions. Nature Publishing Group UK 2018-11-21 /pmc/articles/PMC6246557/ /pubmed/30459301 http://dx.doi.org/10.1038/s41426-018-0185-z Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Bui, Chau M.
Adam, Dillon C.
Njoto, Edwin
Scotch, Matthew
MacIntyre, C. Raina
Characterising routes of H5N1 and H7N9 spread in China using Bayesian phylogeographical analysis
title Characterising routes of H5N1 and H7N9 spread in China using Bayesian phylogeographical analysis
title_full Characterising routes of H5N1 and H7N9 spread in China using Bayesian phylogeographical analysis
title_fullStr Characterising routes of H5N1 and H7N9 spread in China using Bayesian phylogeographical analysis
title_full_unstemmed Characterising routes of H5N1 and H7N9 spread in China using Bayesian phylogeographical analysis
title_short Characterising routes of H5N1 and H7N9 spread in China using Bayesian phylogeographical analysis
title_sort characterising routes of h5n1 and h7n9 spread in china using bayesian phylogeographical analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6246557/
https://www.ncbi.nlm.nih.gov/pubmed/30459301
http://dx.doi.org/10.1038/s41426-018-0185-z
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