Inferring patterns of influenza transmission in swine from multiple streams of surveillance data

Christopher C. Strelioff, Dhanasekaran Vijaykrishna, Steven Riley, Yi Guan, J. S. Malik Peiris, James O. Lloyd-Smith

Research output: Contribution to journalArticleResearchpeer-review

9 Citations (Scopus)

Abstract

Swine populations are known to be an important source of new human strains of influenza A, including those responsible for global pandemics. Yet our knowledge of the epidemiology of influenza in swine is dismayingly poor, as highlighted by the emergence of the 2009 pandemic strain and the paucity of data describing its origins. Here, we analyse a unique dataset arising from surveillance of swine influenza at a Hong Kong abattoir from 1998 to 2010. We introduce a state-space model that estimates disease exposure histories by joint inference from multiple modes of surveillance, integrating both virological and serological data. We find that an observed decrease in virus isolation rates is not due to a reduction in the regional prevalence of influenza. Instead, a more likely explanation is increased infection of swine in production farms, creating greater immunity to disease early in life. Consistent with this, we find that the weekly risk of exposure on farms equals or exceeds the exposure risk during transport to slaughter.We discuss potential causes for these patterns, including competition between influenza strains and shifts in the Chinese pork industry, and suggest opportunities to improve knowledge and reduce prevalence of influenza in the region.
Original languageEnglish
Number of pages8
JournalProceedings of the Royal Society B: Biological Sciences
Volume280
Issue number1762
DOIs
Publication statusPublished - 7 Jul 2013
Externally publishedYes

Keywords

  • Disease ecology
  • Infectious disease surveillance
  • Influenza
  • State-space model
  • Swine
  • Zoonosis

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