Towards transparent and consistent exchange of knowledge for improved microbiological food safety

Plaza‐Rodríguez, Carolina; Haberbeck, Letícia Ungaretti; Desvignes, Virginie; Dalgaard, Paw; Sanaa, Moez; Nauta, Maarten; Filter, Matthias; Guillier, Laurent

doi:10.1016/j.cofs.2017.12.002

Cited by 25 publications

(12 citation statements)

References 49 publications

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“…In order to provide transparency and consistency of the developed model and to facilitate the reuse and exchange of the model script (Plaza‐Rodríguez et al, ), the R code was created following the structure required by the standardized data format called Food Safety Knowledge Markup Language (FSK‐ML) (https://foodrisklabs.bfr.bund.de/fsk-ml-food-safety-knowledge-markup-language/), including two R scripts: (a) Model script and (b) Visualization Script. The entire metadata schema was also generated by using the metadata Excel sheet template (https://foodrisklabs.bfr.bund.de/rakip-harmonization-resources/) in order to describe in detail the complete model range of applicability.…”

Section: Methodsmentioning

confidence: 99%

Probabilistic model for the estimation of the consumer exposure to methicillin‐resistant Staphylococcus aureus due to cross‐contamination and recontamination

2019

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The presence of multidrug‐resistant bacteria like methicillin‐resistant Staphylococcus aureus (MRSA) in retail meat is one of the current concerns of the public health authorities. Bacterial cross‐contamination and recontamination during household food preparation could play an important role in the dissemination of such bacteria, and therefore could contribute to a serious health problem, more specifically for immunocompromised people. In order to evaluate the importance of such events, a probabilistic model was developed to estimate the likelihood and extent of cross‐contamination and recontamination and the burden of MRSA from contaminated raw chicken meat via hands and kitchen utensils in a serving (consisting on a slice of bread and a piece of grilled chicken meat) during a household barbecue in Germany. A modular design was used, taking into account the chronological order of the routines during the barbecue event, and Monte Carlo simulations were applied. Available data on the prevalence and burden of MRSA in chicken meat at retail in Germany were used as starting point and were incorporated in the model as probability distributions. The probabilities and extent of bacterial transfer between food items and kitchen utensils (referred to as “Objects”) and the routines performed during food preparation (referred to as “Actions”) specified by their probabilities of occurrence were incorporated as the main input parameters. The model was set up in R 3.5.0 and converted to a standardized format (FSKX file). Therefore, the code can be easily accessed, evaluated, modified, and reused for different purposes. The present study contributes to the quantification of consumer exposure to MRSA through food consumption once contaminated food has entered the household kitchen. Even when the MRSA prevalence and bacterial load in retail chicken meat in Germany are low, resistant bacteria can reach the consumer due to cross‐contamination and recontamination events. The results show that the probability of one CFU to be transferred from the contaminated raw chicken meat to the final serving and the number of MRSA bacteria transferred due to cross‐contamination and recontamination events are in general low, being the contamination of the final serving more likely to occur via bread, rather than via grilled chicken. The results show that the prevalence of MRSA at retail highly influences the probability of the final serving to be contaminated. However, this study also highlights the importance of keeping good hygiene practices during the household food manipulation for reducing the spread of MRSA. The provision of the model in a standardized data format will allow an easy incorporation of the developed model into a complete quantitative microbial risk assessment model that will greatly help to estimate the risk of consumer exposure to MRSA through the consumption of contaminated food.

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Section: Methodsmentioning

confidence: 99%

Probabilistic model for the estimation of the consumer exposure to methicillin‐resistant Staphylococcus aureus due to cross‐contamination and recontamination

2019

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“…The project has 1) defined terms, concepts and metadata requirements, 2) created and open webbased food safety knowledge repository 11 with a standardised software language and 3) uploaded models (Rodríguez et al, 2017).…”

Section: Rakip: the Risk Assessment Modelling And Knowledge Integratimentioning

confidence: 99%

Annual report of the Scientific Network on Microbiological Risk Assessment 2018

2018

EFSA Supporting Publications

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Among the tasks of EFSA, according to its founding regulation (Regulation (EC) No 178/2002), there is the establishment of a system of Networks of organisations operating in the fields within EFSA's mission, the objective being to facilitate a scientific cooperation framework by the coordination of activities, the exchange of information, the development and implementation of joint projects, the exchange of expertise and best practices. Additionally, the EFSA Science Strategy 2012‐2016 set the objective of developing, together with Member States' competent authorities, multi‐annual work programmes focused on filling data gaps and on setting priorities for data collections. To implement the above provisions various Networks were established. The Scientific Network on Microbiological Risk Assessment (MRA Network) had its first meeting in 2007. Currently, 25 European Union Member States and two observer countries (Switzerland and Norway) are members of the MRA Network. The annual meeting was held on 24‐25 April 2018 in Parma. A wide range of topics related to microbial risk assessment were discussed, including risk assessments on Salmonella, quantitative risk assessments, and antimicrobial resistance.

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“…However, their reusability and the information exchange between the resources may currently be difficult and time consuming (Plaza-Rodríguez et al, 2017). This situation represents an obstacle for the performance of risk assessment using the most up to date domain knowledge.…”

Section: Introductionmentioning

confidence: 99%

“…For example: (a) modellers would be able to share their models in a harmonized way with the scientific community, (b) reviewers of research papers exploiting these models would be able to easily reproduce results from the paper, (c) risk assessors could easier exploit the scientific knowledge generated by the research community by having QMRA and EM models readily available with a harmonized description which consequently would allow risk assessors to perform their work in a shorter time. More details on the end users objectives and on the description of end users cases are given by Plaza-Rodríguez et al (2017).…”

Section: Accepted Manuscript 17mentioning

confidence: 99%

“…A consensus on definitions of QMRA and PM modelling terms, is an indispensable first step in achieving efficient information exchange, and will enhance transparency and confidence in the shared knowledge (Plaza-Rodríguez et al, 2017). The second step would be the creation of a conceptual framework to describe all necessary metadata on a piece of knowledge.…”

Section: Introductionmentioning

confidence: 99%

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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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Towards transparent and consistent exchange of knowledge for improved microbiological food safety

Cited by 25 publications

References 49 publications

Probabilistic model for the estimation of the consumer exposure to methicillin‐resistant Staphylococcus aureus due to cross‐contamination and recontamination

Probabilistic model for the estimation of the consumer exposure to methicillin‐resistant Staphylococcus aureus due to cross‐contamination and recontamination

Annual report of the Scientific Network on Microbiological Risk Assessment 2018

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

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