Beyond QMRA: Modelling microbial health risk as a complex system using Bayesian networks. (July 2015)

• Record Type: Journal Article
• Authors: Beaudequin, Denise; Harden, Fiona; Roiko, Anne; Stratton, Helen; Lemckert, Charles; Mengersen, Kerrie
• Is Part Of: Environment international. Volume 80(2015:Jul.)
• Page Start: 8
• Page End: 18
• Publication Date: 2015-07
• Subjects: BN Bayesian network -- CFU colony-forming unit -- DAG Directed Acyclic Graph -- FIB faecal indicator bacteria -- MC Monte Carlo -- MCMC Markov chain Monte Carlo -- MPN most probable number -- MPRM modular process risk model -- QMRA quantitative microbial risk assessment; Bayesian network -- Health risk assessment -- Microbial risk -- Modelling -- QMRA -- Uncertainty; Environmental protection -- Periodicals; Environmental health -- Periodicals; Environmental monitoring -- Periodicals; Environmental Monitoring -- Periodicals; Environnement -- Protection -- Périodiques; Hygiène du milieu -- Périodiques; Environnement -- Surveillance -- Périodiques; Environmental health; Environmental monitoring; Environmental protection; Periodicals; 333.705
• DOI: 10.1016/j.envint.2015.03.013
• Languages: English
• ISSNs: 0160-4120

Abstract

Abstract: 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. Highlights: Bayesian networks are emerging as a valuable technique in microbial risk assessment. Fifteen applications in food and water health risk assessment are reviewed. BNs are flexible and have diverse applications. The method overcomes problems where data are sparse, missing or poor in quality. BNs offer significant untapped potential in quantifying microbial exposures.