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Article: Multiannual patterns of influenza A transmission in Chinese live bird market systems
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TitleMultiannual patterns of influenza A transmission in Chinese live bird market systems
 
AuthorsPepin, KM6 2 4
Wang, J6 3
Webb, CT6 4
Smith, GJD6 1
Poss, M4 5
Hudson, PJ4 5
Hong, W6
Zhu, H6 3
Riley, S6 7
Guan, Y6 3
 
KeywordsAvian influenza
Coinfection
H5N1
Host specificity
Live bird market
Risk
 
Issue Date2013
 
PublisherBlackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/subs.asp?ref=1750-2640&site=1
 
CitationInfluenza And Other Respiratory Viruses, 2013, v. 7 n. 1, p. 97-107 [How to Cite?]
DOI: http://dx.doi.org/10.1111/j.1750-2659.2012.00354.x
 
AbstractBackground Avian influenza viruses (AIV) cause huge economic losses in poultry industries and pose a substantial threat to human health. However, predicting AIV epizootics and emergence in humans is confounded by insufficient empirical data on the ecology and dynamics of AIV in poultry systems. To address this gap, we quantified incidence patterns for 13 hemagglutinin subtypes of AIV using 6years of surveillance data that were collected from ten different species of poultry and three different types of poultry holdings (contexts) - retail, wholesale, or farms. Methods We collected 42646 samples in Shantou, China between 2000 and 2006. We screened samples for hemagglutinin subtypes 1-13 of AIV and Avian Paramyxovirus-type-1 (APMV-1) using monospecific antisera in hemagglutination inhibition tests. We analyzed the data to determine seasonality patterns, subtype-host, and subtype-subtype interactions as well as subtype bias in incidence in different contexts. Results H3, H6, H9, and APMV-1 were the most prevalent. No significant seasonality was found when all subtypes were considered together. For most AIV subtypes and APMV-1, there was subtype specificity for host, context, and coinfection partner. H5 showed the most generalized host usage pattern, followed by H9 and H6. Conclusion Subtype-specific patterns because of host, context, and other subtypes suggest that risk assessments that exclude these details are likely inaccurate. Surveillance should include longitudinal sampling of multiple host species in multiple contexts. Quantitative models of control strategies must consider multiple subtypes, hosts, and source contexts to assess the effectiveness of interventions. © 2012 Blackwell Publishing Ltd.
 
ISSN1750-2640
2012 Impact Factor: 1.471
2012 SCImago Journal Rankings: 1.443
 
DOIhttp://dx.doi.org/10.1111/j.1750-2659.2012.00354.x
 
DC FieldValue
dc.contributor.authorPepin, KM
 
dc.contributor.authorWang, J
 
dc.contributor.authorWebb, CT
 
dc.contributor.authorSmith, GJD
 
dc.contributor.authorPoss, M
 
dc.contributor.authorHudson, PJ
 
dc.contributor.authorHong, W
 
dc.contributor.authorZhu, H
 
dc.contributor.authorRiley, S
 
dc.contributor.authorGuan, Y
 
dc.date.accessioned2012-06-26T06:28:07Z
 
dc.date.available2012-06-26T06:28:07Z
 
dc.date.issued2013
 
dc.description.abstractBackground Avian influenza viruses (AIV) cause huge economic losses in poultry industries and pose a substantial threat to human health. However, predicting AIV epizootics and emergence in humans is confounded by insufficient empirical data on the ecology and dynamics of AIV in poultry systems. To address this gap, we quantified incidence patterns for 13 hemagglutinin subtypes of AIV using 6years of surveillance data that were collected from ten different species of poultry and three different types of poultry holdings (contexts) - retail, wholesale, or farms. Methods We collected 42646 samples in Shantou, China between 2000 and 2006. We screened samples for hemagglutinin subtypes 1-13 of AIV and Avian Paramyxovirus-type-1 (APMV-1) using monospecific antisera in hemagglutination inhibition tests. We analyzed the data to determine seasonality patterns, subtype-host, and subtype-subtype interactions as well as subtype bias in incidence in different contexts. Results H3, H6, H9, and APMV-1 were the most prevalent. No significant seasonality was found when all subtypes were considered together. For most AIV subtypes and APMV-1, there was subtype specificity for host, context, and coinfection partner. H5 showed the most generalized host usage pattern, followed by H9 and H6. Conclusion Subtype-specific patterns because of host, context, and other subtypes suggest that risk assessments that exclude these details are likely inaccurate. Surveillance should include longitudinal sampling of multiple host species in multiple contexts. Quantitative models of control strategies must consider multiple subtypes, hosts, and source contexts to assess the effectiveness of interventions. © 2012 Blackwell Publishing Ltd.
 
dc.description.naturelink_to_OA_fulltext
 
dc.identifier.citationInfluenza And Other Respiratory Viruses, 2013, v. 7 n. 1, p. 97-107 [How to Cite?]
DOI: http://dx.doi.org/10.1111/j.1750-2659.2012.00354.x
 
dc.identifier.doihttp://dx.doi.org/10.1111/j.1750-2659.2012.00354.x
 
dc.identifier.hkuros203144
 
dc.identifier.issn1750-2640
2012 Impact Factor: 1.471
2012 SCImago Journal Rankings: 1.443
 
dc.identifier.scopuseid_2-s2.0-84871378516
 
dc.identifier.urihttp://hdl.handle.net/10722/151768
 
dc.languageeng
 
dc.publisherBlackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/subs.asp?ref=1750-2640&site=1
 
dc.publisher.placeUnited Kingdom
 
dc.relation.ispartofInfluenza and other Respiratory Viruses
 
dc.subjectAvian influenza
 
dc.subjectCoinfection
 
dc.subjectH5N1
 
dc.subjectHost specificity
 
dc.subjectLive bird market
 
dc.subjectRisk
 
dc.titleMultiannual patterns of influenza A transmission in Chinese live bird market systems
 
dc.typeArticle
 
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<contributor.author>Wang, J</contributor.author>
<contributor.author>Webb, CT</contributor.author>
<contributor.author>Smith, GJD</contributor.author>
<contributor.author>Poss, M</contributor.author>
<contributor.author>Hudson, PJ</contributor.author>
<contributor.author>Hong, W</contributor.author>
<contributor.author>Zhu, H</contributor.author>
<contributor.author>Riley, S</contributor.author>
<contributor.author>Guan, Y</contributor.author>
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<description.abstract>Background Avian influenza viruses (AIV) cause huge economic losses in poultry industries and pose a substantial threat to human health. However, predicting AIV epizootics and emergence in humans is confounded by insufficient empirical data on the ecology and dynamics of AIV in poultry systems. To address this gap, we quantified incidence patterns for 13 hemagglutinin subtypes of AIV using 6years of surveillance data that were collected from ten different species of poultry and three different types of poultry holdings (contexts) - retail, wholesale, or farms. Methods We collected 42646 samples in Shantou, China between 2000 and 2006. We screened samples for hemagglutinin subtypes 1-13 of AIV and Avian Paramyxovirus-type-1 (APMV-1) using monospecific antisera in hemagglutination inhibition tests. We analyzed the data to determine seasonality patterns, subtype-host, and subtype-subtype interactions as well as subtype bias in incidence in different contexts. Results H3, H6, H9, and APMV-1 were the most prevalent. No significant seasonality was found when all subtypes were considered together. For most AIV subtypes and APMV-1, there was subtype specificity for host, context, and coinfection partner. H5 showed the most generalized host usage pattern, followed by H9 and H6. Conclusion Subtype-specific patterns because of host, context, and other subtypes suggest that risk assessments that exclude these details are likely inaccurate. Surveillance should include longitudinal sampling of multiple host species in multiple contexts. Quantitative models of control strategies must consider multiple subtypes, hosts, and source contexts to assess the effectiveness of interventions. &#169; 2012 Blackwell Publishing Ltd.</description.abstract>
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Author Affiliations
  1. Duke-NUS Graduate Medical School Singapore
  2. Colorado State University
  3. The University of Hong Kong
  4. John E. Fogarty International Center for advanced study in the health sciences
  5. Pennsylvania State University
  6. Shantou University, Medical College (SUMC)
  7. Imperial College London