Beyond QMRA

Modelling microbial health risk as a complex system using Bayesian networks

Denise Beaudequin, Fiona Harden, Anne Roiko, Helen Stratton, Charles Lemckert, Kerrie Mengersen

Research output: Contribution to journalReview article

15 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)8-18
Number of pages11
JournalEnvironment International
Volume80
DOIs
Publication statusPublished - Jul 2015
Externally publishedYes

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health risk
risk assessment
modeling
assessment method
data quality
transparency
pathogen
microorganism
prediction
method
exposure

Cite this

Beaudequin, Denise ; Harden, Fiona ; Roiko, Anne ; Stratton, Helen ; Lemckert, Charles ; Mengersen, Kerrie. / Beyond QMRA : Modelling microbial health risk as a complex system using Bayesian networks. In: Environment International. 2015 ; Vol. 80. pp. 8-18.
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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.",
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Beyond QMRA : Modelling microbial health risk as a complex system using Bayesian networks. / Beaudequin, Denise; Harden, Fiona; Roiko, Anne; Stratton, Helen; Lemckert, Charles; Mengersen, Kerrie.

In: Environment International, Vol. 80, 07.2015, p. 8-18.

Research output: Contribution to journalReview article

TY - JOUR

T1 - Beyond QMRA

T2 - Modelling microbial health risk as a complex system using Bayesian networks

AU - Beaudequin, Denise

AU - Harden, Fiona

AU - Roiko, Anne

AU - Stratton, Helen

AU - Lemckert, Charles

AU - Mengersen, Kerrie

PY - 2015/7

Y1 - 2015/7

N2 - 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.

AB - 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.

KW - Bayesian network

KW - Health risk assessment

KW - Microbial risk

KW - Modelling

KW - QMRA

KW - Uncertainty

KW - Models, Theoretical

KW - Water Microbiology/standards

KW - Humans

KW - Food Microbiology/standards

KW - Communicable Diseases/epidemiology

KW - Public Health/methods

KW - Bayes Theorem

KW - Risk Assessment/methods

UR - http://www.scopus.com/inward/record.url?scp=84925433422&partnerID=8YFLogxK

U2 - 10.1016/j.envint.2015.03.013

DO - 10.1016/j.envint.2015.03.013

M3 - Review article

VL - 80

SP - 8

EP - 18

JO - Environmental International

JF - Environmental International

SN - 0160-4120

ER -