Risk based culling for highly infectious diseases of livestock.

Dennis E. te Beest*, Thomas J. Hagenaars, J. Arjan Stegeman, Marion P.G. Koopmans, Michiel van Boven

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

19 Citations (Scopus)

Abstract

The control of highly infectious diseases of livestock such as classical swine fever, foot-and-mouth disease, and avian influenza is fraught with ethical, economic, and public health dilemmas. Attempts to control outbreaks of these pathogens rely on massive culling of infected farms, and farms deemed to be at risk of infection. Conventional approaches usually involve the preventive culling of all farms within a certain radius of an infected farm. Here we propose a novel culling strategy that is based on the idea that farms that have the highest expected number of secondary infections should be culled first. We show that, in comparison with conventional approaches (ring culling), our new method of risk based culling can reduce the total number of farms that need to be culled, the number of culled infected farms (and thus the expected number of human infections in case of a zoonosis), and the duration of the epidemic. Our novel risk based culling strategy requires three pieces of information, viz. the location of all farms in the area at risk, the moments when infected farms are detected, and an estimate of the distance-dependent probability of transmission.

Original languageEnglish
Pages (from-to)81
Number of pages1
JournalVeterinary Research
Volume42
DOIs
Publication statusPublished - 29 Jun 2011

Bibliographical note

Funding Information:
This work was supported by CDC under the grant agreement U19 CI000404-01, Avian influenza collaborative research centers, Studies at the human animal interface, by the “Impulse Veterinary Avian Influenza Research in The Netherlands” program of the Dutch government, and by the Strategic Research theme Infectious Disease Dynamics of the Dutch National Institute of Public Health and the Environment. We would like to thank Jacco Wallinga for his input, and Jan van de Kassteele for his statistical advice.

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