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Prediction of the spread of highly pathogenic avianinfluenza using a multifactor network: Part2- Comprehensive network analysis with direct/indirect infection route

  • Hyung jin Lee
  • , Kyo Suh*
  • , Nam su Jung
  • , In bok Lee
  • , Il hwan Seo
  • , Oun kyung Moon
  • , Jeong jae Lee
  • *Corresponding author for this work
  • Seoul National University
  • Kongju National University
  • Animal and Plant Quarantine Agency

Research output: Contribution to journalJournal articlepeer-review

Abstract

Epidemic diseases of domestic animals, such as high pathogenic avian influenza, can be directly spread by poultry-related business personnel/vehicles visiting farms and can be indirectly caused by aerial spread. However, most of comprehensive analysis studies have considered only direct or indirect causes. The purpose of this study is to construct a direct HPAI spread network based on the relationships between farms using poultry-related business data from 39 farms in the Kimje district of South Korea and an indirect HPAI spread network using the aerial spread from each farm during the HPAI outbreak in 2008. Direct/indirect HPAI spread networks were also analysed using centrality analyses to identify highly vulnerable farms for infection and highly influential farms on other farms so the initial spread can be prevented. This study proposes an optimal prevention solution through a comprehensive spread simulation and compares the centrality of infected/suspected farms by tracing the infection routes for network validation.

Original languageEnglish
Pages (from-to)115-127
Number of pages13
JournalBiosystems Engineering
Volume118
Issue number1
DOIs
StatePublished - 2014.02

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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