Disease intelligence for highly pathogenic avian influenza

Dev Biol (Basel). 2007:130:7-12.

Abstract

A comprehensive approach to highly pathogenic avian influenza (HPAI) is crucial for identifying all the factors that contribute to its emergence, spread and persistence. Epidemiological understanding makes it possible to predict the evolution of the virus and to prevent and control the socioeconomic, environmental, institutional and policy consequences. At FAO, risk assessment and intelligence with regard to HPAI are based on lessons learnt from assisting countries to design strategies and on implementation of technical assistance programmes, which reveal important elements, such as the roles of ducks, live-bird markets and trade. Wild birds were found to contribute, by transporting the H5N1 virus over long distances. The contributions of different poultry farming systems and market chains in the epidemiology of HPAI are well recognized; however, the respective roles of smallholder systems and commercial farms are unclear. FAO considers that smallholders will continue to be an important factor and should be taken into account in control and prevention programmes. Changes in poultry farming are essentially driven by the private sector and market forces and could have negative consequences on the livelihoods of smallholders and on ecologically balanced production systems and agricultural biodiversity. Biosecurity can, however, be improved at the level of farms and markets. Institutional factors, such as the capacity of animal health systems to deliver control programmes, are also important, requiring strengthening and innovation in risk analysis and management.

MeSH terms

  • Agriculture / economics
  • Agriculture / methods
  • Animals
  • Birds
  • Commerce
  • Communicable Disease Control / methods*
  • Disease Outbreaks / veterinary
  • Humans
  • Influenza A Virus, H5N1 Subtype*
  • Influenza in Birds / epidemiology*
  • Poultry
  • Risk Factors
  • Socioeconomic Factors
  • United Nations / statistics & numerical data