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Spatial and spatio-temporal analysis of Salmonella infection in dairy herds in England and Wales

  • S. E. Fenton
  • , H. E. Clough
  • , Peter Diggle
  • , S. J. Evans
  • , H. C. Davison
  • , W. D. Vink
  • , N. P. French
  • University of Liverpool
  • AstraZeneca
  • Lancaster University
  • UK Department for Environment, Food and Rural Affairs
  • Massey University

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)

Abstract

Using data from a cohort study conducted by the Veterinary Laboratories Agency (VLA), evidence of spatial clustering at distances up to 30 km was found for S. Agama and S. Dublin (P values of 0.001) and borderline evidence was found for spatial clustering of S. Typhimurium (P = 0.077). The evolution of infection status of study farms over time was modelled using a Markov Chain model with transition probabilities describing changes in status at each of four visits, allowing for the effect of sampling visit. The degree of geographical clustering of infection, having allowed for temporal effects, was assessed by comparing the residual deviance from a model including a measure of recent neighbourhood infection levels with one excluding this variable. The number of cases arising within a defined distance and time period of an index case was higher than expected. This provides evidence for spatial and spatio-temporal clustering, which suggests either a contagious process (e.g. through direct or indirect farm-to-farm transmission) or geographically localized environmental and/or farm factors which increase the risk of infection. The results emphasize the different epidemiology of the three Salmonella serovars investigated.
Original languageEnglish
Pages (from-to)847-857
Number of pages11
JournalEpidemiology and Infection
Volume137
Issue number6
DOIs
Publication statusPublished - 1 Jan 2009
Externally publishedYes

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

Keywords

  • Epidemiology
  • K-function analysis
  • Markov chain
  • Salmonella
  • Spatial clustering

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