Applying Bayesian network modelling to understand the links between on-farm biosecurity practice during the 2007 equine influenza outbreak and horse managers' perceptions of a subsequent outbreak

Abstract

Australia experienced its first ever outbreak of equine influenza in August 2007. Horses on 9359 premises were infected over a period of 5 months before the disease was successfully eradicated through the combination of horse movement controls, on-farm biosecurity and vaccination. In a previous premises-level case-control study of the 2007 equine influenza outbreak in Australia, the protective effect of several variables representing on-farm biosecurity practices were identified. Separately, factors associated with horse managers' perceptions of the effectiveness of biosecurity measures have been identified. In this analysis we applied additive Bayesian network modelling to describe the complex web of associations linking variables representing on-farm human behaviours during the 2007 equine influenza outbreak (compliance or lack thereof with advised personal biosecurity measures) and horse managers' perceptions of the effectiveness of such measures in the event of a subsequent outbreak. Heuristic structure discovery enabled identification of a robust statistical model for 31 variables representing biosecurity practices and perceptions of the owners and managers of 148 premises. The Bayesian graphical network model we present statistically describes the associations linking horse managers' on-farm biosecurity practices during an at-risk period in the 2007 outbreak and their perceptions of whether such measures will be effective in a future outbreak. Practice of barrier infection control measures were associated with a heightened perception of preparedness, whereas horse managers that considered their on-farm biosecurity to be more stringent during the outbreak period than normal practices had a heightened perception of the effectiveness of other measures such as controlling access to the premises. Past performance in an outbreak setting may indeed be a reliable predictor of future perceptions, and should be considered when targeting infection control guidance to horse owners and managers.

Keywords: Bayesian graphical network modelling; Biosecurity; Effectiveness; Equine influenza; Perceptions.