Characterising routes of H5N1 and H7N9 spread in China using Bayesian phylogeographical analysis
Abstract 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 pathog...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2018-11-01
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| Series: | Emerging Microbes and Infections |
| Online Access: | http://link.springer.com/article/10.1038/s41426-018-0185-z |
| _version_ | 1818269093309972480 |
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| author | Chau M. Bui Dillon C. Adam Edwin Njoto Matthew Scotch C. Raina MacIntyre |
| author_facet | Chau M. Bui Dillon C. Adam Edwin Njoto Matthew Scotch C. Raina MacIntyre |
| author_sort | Chau M. Bui |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-12T20:48:54Z |
| format | Article |
| id | doaj.art-5afc060f7e3f4f5eb5791d8518f36f9a |
| institution | Directory Open Access Journal |
| issn | 2222-1751 |
| language | English |
| last_indexed | 2024-12-12T20:48:54Z |
| publishDate | 2018-11-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Emerging Microbes and Infections |
| spelling | doaj.art-5afc060f7e3f4f5eb5791d8518f36f9a2022-12-22T00:12:28ZengTaylor & Francis GroupEmerging Microbes and Infections2222-17512018-11-01711810.1038/s41426-018-0185-zCharacterising routes of H5N1 and H7N9 spread in China using Bayesian phylogeographical analysisChau M. Bui0Dillon C. Adam1Edwin Njoto2Matthew Scotch3C. Raina MacIntyre4University of New South Wales (UNSW)University of New South Wales (UNSW)University of New South Wales (UNSW)University of New South Wales (UNSW)University of New South Wales (UNSW)Abstract 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.http://link.springer.com/article/10.1038/s41426-018-0185-z |
| spellingShingle | Chau M. Bui Dillon C. Adam Edwin Njoto Matthew Scotch C. Raina MacIntyre Characterising routes of H5N1 and H7N9 spread in China using Bayesian phylogeographical analysis Emerging Microbes and Infections |
| 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 |
| url | http://link.springer.com/article/10.1038/s41426-018-0185-z |
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