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Beyond QMRA: Modelling microbial health risk as a complex system using Bayesian networks
Journal article   Peer reviewed

Beyond QMRA: Modelling microbial health risk as a complex system using Bayesian networks

Denise Beaudequin, F Harden, Anne H Roiko, H Stratton, C Lemckert and K Mengersen
Environment International, Vol.80, pp.8-18
2015
DOI: https://doi.org/10.1016/j.envint.2015.03.013
url
https://doi.org/10.1016/j.envint.2015.03.013View
Published Version

Abstract

bayesian network health risk assessment microbial risk modelling QMRA uncertainty
Background: Quantitative microbial risk assessment (QMRA) is the current method of choice for determining the risk to human health from exposure to microorganisms of concern. However, current approaches are often constrained by the availability of required data, and may not be able to incorporate the many varied factors that influence this risk. Systems models, based on Bayesian networks (BNs), are emerging as an effective complementary approach that overcomes these limitations. Objectives: This article aims to provide a comparative evaluation of the capabilities and challenges of current QMRA methods and BN models, and a scoping review of recent published articles that adopt the latter for microbial risk assessment. Pros and cons of systems approaches in this context are distilled and discussed. Methods: A search of the peer-reviewed literature revealed 15 articles describing BNs used in the context of QMRAs for foodborne and waterborne pathogens. These studies were analysed in terms of their application, uses and benefits in QMRA. Discussion: The applications were notable in their diversity. BNs were used to make predictions, for scenario assessment, risk minimisation, to reduce uncertainty and to separate uncertainty and variability. Most studies focused on a segment of the exposure pathway, indicating the broad potential for the method in other QMRA steps. BNs offer a number of useful features to enhance QMRA, including transparency, and the ability to deal with poor quality data and support causal reasoning. Conclusion: The method has significant untapped potential to describe the complex relationships between microbial environmental exposures and health.

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