title: Utility of Bayesian networks in QMRA-based evaluation of risk reduction options for recycled water.
abstract: BACKGROUND
  Quantitative microbial risk assessment (QMRA), the current method of choice for evaluating human health risks associated with disease-causing microorganisms, is often constrained by issues such as availability of required data, and inability to incorporate the multitude of factors influencing risk. Bayesian networks (BNs), with their ability to handle data paucity, combine quantitative and qualitative information including expert opinions, and ability to offer a systems approach to characterisation of complexity, are increasingly recognised as a powerful, flexible tool that overcomes these limitations.
  
  
  OBJECTIVE
  We present a QMRA expressed as a Bayesian network (BN) in a wastewater reuse context, with the objective of demonstrating the utility of the BN method in health risk assessments, particularly for evaluating a range of exposure and risk mitigation scenarios. As a case study, we examine the risk of norovirus infection associated with wastewater-irrigated lettuce.
  
  
  METHODS
  A Bayesian network was developed following a QMRA approach, using published data, and reviewed by domain experts using a participatory process.
  
  
  CONCLUSIONS
  Employment of a BN facilitated rapid scenario evaluations, risk minimisation, and predictive comparisons. The BN supported exploration of conditions required for optimal outcomes, as well as investigation of the effect on the reporting nodes of changes in 'upstream' conditions. A significant finding was the indication that if maximum post-treatment risk mitigation measures were implemented, there was a high probability (0.84) of a low risk of infection regardless of fluctuations in other variables, including norovirus concentration in treated wastewater.
  
  
  CONCLUSIONS
  BNs are useful in situations where insufficient empirical data exist to satisfy QMRA requirements and they are exceptionally suited to the integration of risk assessment and risk management in the QMRA context. They allow a comprehensive visual appraisal of major influences in exposure pathways, and rapid interactive risk assessment in multifaceted water reuse scenarios.
authors.affiliation: Faculty of Health, Queensland University of Technology, Gardens Point Campus, 2 George Street, Brisbane, Queensland 4000, Australia; Institute of Health and Biomedical Innovation (IHBI), Queensland University of Technology, 60 Musk Avenue, Kelvin Grove, Queensland 4059, Australia. Electronic address: d.beaudequin@qut.edu.au.
authors.forename: Denise
authors.initials: D
authors.lastname: Beaudequin
authors.affiliation: Faculty of Health, Queensland University of Technology, Gardens Point Campus, 2 George Street, Brisbane, Queensland 4000, Australia; Institute of Health and Biomedical Innovation (IHBI), Queensland University of Technology, 60 Musk Avenue, Kelvin Grove, Queensland 4059, Australia. Electronic address: fiona.harden@qut.edu.au.
authors.forename: Fiona
authors.initials: F
authors.lastname: Harden
authors.affiliation: School of Medicine, Griffith University, Gold Coast Campus, Parklands Drive, Southport, Queensland 4222, Australia; Smartwater Research Centre, Griffith University, Gold Coast Campus, Edmund Rice Dr, Southport, Queensland 4215, Australia. Electronic address: a.roiko@griffith.edu.au.
authors.forename: Anne
authors.initials: A
authors.lastname: Roiko
authors.affiliation: Science and Engineering Faculty, Queensland University of Technology, Gardens Point Campus, 2 George Street, Brisbane, Queensland 4000, Australia; Institute for Future Environments (IFE), Queensland University of Technology, Gardens Point Campus, 2 George Street, Brisbane, Queensland 4000, Australia. Electronic address: k.mengersen@qut.edu.au.
authors.forename: Kerrie
authors.initials: K
authors.lastname: Mengersen
mesh_terms: D057914:Agricultural Irrigation / Q000379:methods*; D001499:Bayes Theorem; D016571:Neural Networks, Computer; D029322:Norovirus; D059027:Recycling; D018570:Risk Assessment / Q000379:methods; D014865:Waste Disposal, Fluid / Q000379:methods; D062065:Wastewater / Q000382:microbiology* / Q000821:virology; D014871:Water Microbiology; D014876:Water Pollution / Q000706:statistics & numerical data*
chemical_list: D062065:Wastewater
keywords: Bayesian network; Exposure modelling; Health risk assessment; Norovirus; Quantitative microbial risk assessment; Water recycling