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

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

doi:10.1016/j.envint.2015.03.013

Cited by 45 publications

(34 citation statements)

References 37 publications

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“…BN approaches offer several features that are useful in hazard ranking of NMs. Firstly, a BN is based on probabilistic relationships that provides insight and an understanding even with partial and limited information (Beaudequin et al, 2015). This is particularly important in the case of NM hazard ranking were the relationships between NMs physicochemical properties and their effects on human health have not been firmly established.…”

Section: Introductionmentioning

confidence: 99%

Application of Bayesian networks for hazard ranking of nanomaterials to support human health risk assessment

et al. 2017

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In this study, a Bayesian Network (BN) was developed for the prediction of the hazard potential and biological effects with the focus on metal-and metal-oxide nanomaterials to support human health risk assessment. The developed BN captures the (inter) relationships between the exposure route, the nanomaterials physicochemical properties and the ultimate biological effects in a holistic manner and was based on international expert consultation and the scientific literature (e.g., in vitro/in vivo data). The BN was validated with independent data extracted from published studies and the accuracy of the prediction of the nanomaterials hazard potential was 72% and for the biological effect 71%, respectively. The application of the BN is shown with scenario studies for TiO 2 , SiO 2 , Ag, CeO 2 , ZnO nanomaterials. It is demonstrated that the BN may be used by different stakeholders at several stages in the risk assessment to predict certain properties of a nanomaterials of which little information is available or to prioritize nanomaterials for further screening.ARTICLE HISTORY

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Section: Introductionmentioning

confidence: 99%

Application of Bayesian networks for hazard ranking of nanomaterials to support human health risk assessment

et al. 2017

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“…However in the future, other areas, like chemical risk assessment, and other modelling approaches, as e.g. Bayesian networks (Beaudequin et al, 2015) or machine learning (Laabei et al, 2014), should be addressed. As this research has been initiated as an open community effort it can take up suggestions on future development goals from the scientific community.…”

Section: Discussionmentioning

confidence: 99%

Harmonized terms, concepts and metadata for microbiological risk assessment models: The basis for knowledge integration and exchange

Haberbeck

Plaza‐Rodríguez

Desvignes

et al. 2018

Microbial Risk Analysis

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Harmonized terms, concepts and metadata for microbiological risk assessment models: the basis for knowledge integration and exchange, Microbial Risk Analysis (2018Analysis ( ), doi: 10.1016Analysis ( /j.mran.2018 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Author names and affiliations Leticia Corresponding authorsLaurent Guillier; Maarten Nauta; Matthias Filter Short Title or Running HeadHarmonization for microbiological risk assessment modelling AbstractIn the last decades the microbial food safety community has developed a variety of valuable knowledge (e.g., mathematical models and data) and resources (e.g., databases and software tools)in the areas of quantitative microbial risk assessment (QMRA) and predictive microbiology.However, the reusability of this knowledge and the exchange of information between resources are currently difficult and time consuming. This problem has increased over time due to the lack of harmonized data format and rules for knowledge annotation. It includes the lack of a common understanding of basic terms and concepts and of a harmonized information exchange format to describe and annotate knowledge. The existence of ambiguities and inconsistencies in the use of terms and concepts in the QMRA and predictive microbial (PM) modelling necessitates a consensus on their refinement, which will allow a harmonized exchange of information within these areas.Therefore, this work aims to harmonize terms and concepts used in QMRA and PM modelling spanning from high level concepts as defined by Codex Alimentarius, Food and Agriculture Organization (FAO) and World Health Organization (WHO), up to terms generally used in statistics or data and software science. As a result, a harmonized schema for metadata that allows consistent annotation of data and models from these two domains is proposed. This metadata schema is also a . This platform will facilitate the sharing and execution of curated QMRA and PM models using the foundation of the proposed harmonized metadata schema and information exchange format. Furthermore, it will also provide access to related open source software libraries, converter tools and software-specific import and export functions that promote the adoption of FSK-ML by the microbial food safety community. In the future, these resources will hopefully promote both the knowledge reusability and the highquality information exchange between stakeholders within the areas of QMRA and PM modelling worldwide.

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“…Not only for analysing omic data but more generally for facing the lack of data inherent to biological phenomena in MRA, the use of Bayesian techniques (Bayesian inference, Bayesian graphical models) is gaining interest [47,48 ]. They have been used to assess microbial prevalence [49] and contamination level [50], inactivation of pathogens in food [51,52], and more generally in microbial exposure or risk assessment [48 ,53,54].…”

Section: Statistical and Probabilistic Techniques To Support Risk Assmentioning

confidence: 99%

Latest developments in foodborne pathogen risk assessment

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Guillou

2016

Current Opinion in Food Science

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

Cited by 45 publications

References 37 publications

Application of Bayesian networks for hazard ranking of nanomaterials to support human health risk assessment

Application of Bayesian networks for hazard ranking of nanomaterials to support human health risk assessment

Harmonized terms, concepts and metadata for microbiological risk assessment models: The basis for knowledge integration and exchange

Latest developments in foodborne pathogen risk assessment

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