Using Bayesian networks to explore the role of weather as a potential determinant of disease in pigs

B. J.J. McCormick, M. J. Sanchez-Vazquez, F. I. Lewis*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


Many pathogens are sensitive to climatic variables and this is reflected in their seasonality of occurrence and transmission. The identification of environmental conditions that influence disease occurrence can be subtle, particularly considering their complex interdependencies in addition to those relationships between climate and disease. Statistical treatment of environmental variables is often dependent on their correlations and thus descriptions of climate are often restricted to means rather than accounting for the more precise aspects (including mean, maximum, minimum, variability). Here we utilize a novel multivariate statistical modelling approach, additive Bayesian network (ABN) analyses, to identify the inter-linkages of different weather variables to better capture short-term environmental conditions that are important drivers of disease. We present a case study that explores weather as a driver of disease in livestock systems. We utilize quality assurance health scheme data on ten major diseases of pigs from 875 finishing pig herds distributed across the United Kingdom over 7 years (2005-2011). We examine the relationship between the occurrence of these pathologies and contemporary weather conditions measured by local meteorological stations. All ten pathologies were associated with at least 2 other pathologies (maximum 6). Three pathologies were associated directly with temperature variables: papular dermatitis, enzootic pneumonia and milk spots. Latitude was strongly associated with multiple pathologies, though associations with longitude were eliminated when clustering for repeated observations of farms was assessed. The identification of relationships between climatic factors and different (potentially related) diseases offers a more comprehensive insight into the complex role of seasonal drivers and herd health status than traditional analytical methods.

Original languageEnglish
Pages (from-to)54-63
Number of pages10
JournalPreventive Veterinary Medicine
Issue number1
Early online date4 Mar 2013
Publication statusPublished - 15 May 2013

Bibliographical note

The authors are grateful to the BPEX for providing the abattoir surveillance data, to the British Atmospheric Data Centre and the UK Met Office for access to the meteorological data and to the Schroedinger computing facility at the University of Zurich.


  • Bayesian networks
  • Climate
  • Pigs
  • Surveillance
  • Weather


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