An Agent-based Simulator for the Gastrointestinal Pathway of Listeria monocytogenes

We developed an agent-based gastric simulator for a human host to illustrate the within host survival mechanisms of Listeria monocytogenes. The simulator incorporates the gastric physiology and digestion processes that are critical for pathogen survival in the stomach. Mathematical formulations for the pH dynamics, stomach emptying time, and survival probability in the presence of gastric acid are integrated in the simulator to evaluate the portion of ingested bacteria that survives in the stomach and reaches the small intestine. The parameters are estimated using in vitro data relevant to the human stomach and L. monocytogenes. The simulator predicts that 5%–29% of ingested bacteria can survive a human stomach and reach the small intestine. In the absence of extensive scientific experiments, which are not feasible on the grounds of ethical and safety concerns, this simulator may provide a supplementary tool to evaluate pathogen survival and subsequent infection, especially with regards to the ingestion of small doses

Integrating Whole-Genome Sequencing Data Into Quantitative Risk Assessment of Foodborne Antimicrobial Resistance: A Review of Opportunities and Challenges

Whole-genome sequencing (WGS) will soon replace traditional phenotypic methods for routine testing of foodborne antimicrobial resistance (AMR). WGS is expected to improve AMR surveillance by providing a greater understanding of the transmission of resistant bacteria and AMR genes throughout the food chain, and therefore support risk assessment activities. At this stage, it is unclear how WGS data can be integrated into quantitative microbial risk assessment (QMRA) models and whether their integration will impact final risk estimates or the assessment of risk mitigation measures. This review explores opportunities and challenges of integrating WGS data into QMRA models that follow the Codex Alimentarius Guidelines for Risk Analysis of Foodborne AMR. We describe how WGS offers an opportunity to enhance the next-generation of foodborne AMR QMRA modeling. Instead of considering all hazard strains as equally likely to cause disease, WGS data can improve hazard identification by focusing on those strains of highest public health relevance. WGS results can be used to stratify hazards into strains with similar genetic profiles that are expected to behave similarly, e.g., in terms of growth, survival, virulence or response to antimicrobial treatment. The QMRA input distributions can be tailored to each strain accordingly, making it possible to capture the variability in the strains of interest while decreasing the uncertainty in the model. WGS also allows for a more meaningful approach to explore genetic similarity among bacterial populations found at successive stages of the food chain, improving the estimation of the probability and magnitude of exposure to AMR hazards at point of consumption. WGS therefore has the potential to substantially improve the utility of foodborne AMR QMRA models. However, some degree of uncertainty remains in relation to the thresholds of genetic similarity to be used, as well as the degree of correlation between genotypic and phenotypic profiles. The latter could be improved using a functional approach based on prediction of microbial behavior from a combination of ‘omics’ techniques (e.g., transcriptomics, proteomics and metabolomics). We strongly recommend that methodologies to incorporate WGS data in risk assessment be included in any future revision of the Codex Alimentarius Guidelines for Risk Analysis of Foodborne AMR

Modeling Cross-Contamination During Poultry Processing: Dynamics in The Chiller Tank

Understanding mechanisms of cross-contamination during poultry processing is vital for effective pathogen control. As an initial step toward this goal, we develop a mathematical model of the chilling process in a typical high speed Canadian processing plant. An important attribute of our model is that it provides quantifiable links between processing control parameters and microbial levels, simplifying the complexity of these relationships for implementation into risk assessment models. We apply our model to generic, non-pathogenic Escherichia coli contamination on broiler carcasses, connecting microbial control with chlorine sanitization, organic load in the water, and pre-chiller E. coli levels on broiler carcasses. In particular, our results suggest that while chlorine control is important for reducing E. coli levels during chilling, it plays a less significant role in the management of cross-contamination issues

A Multi-Factorial Risk Prioritization Framework for Food-borne Pathogens

To lower the incidence of human food-borne disease, experts and stakeholders have urged the development of a science- and risk-based management system in which food-borne hazards are analyzed and prioritized. A literature review shows that most approaches to risk prioritization developed to date are based on measures of health outcomes and do not systematically account for other factors that may be important to decision making. The Multi-Factorial Risk Prioritization Framework developed here considers four factors that may be important to risk managers: public health, consumer risk perceptions and acceptance, market-level impacts, and social sensitivity. The framework is based on the systematic organization and analysis of data on these multiple factors. The basic building block of the information structure is a three-dimensional cube based on pathogen-food-factor relationships. Each cell of the cube has an information card associated with it and data from the cube can be aggregated along different dimensions. The framework is operationalized in three stages, with each stage adding another dimension to decision-making capacity. The first stage is the information cards themselves that provide systematic information that is not pre-processed or aggregated across factors. The second stage maps the information on the various information cards into cobweb diagrams that create a graphical profile of, for example, a food-pathogen combination with respect to each of the four risk prioritization factors. The third stage is formal multi-criteria decision analysis in which decision makers place explicit values on different criteria in order to develop risk priorities. The process outlined above produces a ‘List A’ of priority food-pathogen combinations according to some aggregate of the four risk prioritization factors. This list is further vetted to produce ‘List B’, which brings in feasibility analysis by ranking those combinations where practical actions that have a significant impact are feasible. Food-pathogen combinations where not enough is known to identify any or few feasible interventions are included in ‘List C’. ‘List C’ highlights areas with significant uncertainty where further research may be needed to enhance the precision of the risk prioritization process. The separation of feasibility and uncertainty issues through the use of ‘Lists A, B, and C’ allows risk managers to focus separately on distinct dimensions of the overall prioritization. The Multi-Factorial Risk Prioritization Framework provides a flexible instrument that compares and contrasts risks along four dimensions. Use of the framework is an iterative process. It can be used to establish priorities across pathogens for a particular food, across foods for a particular pathogen and/or across specific food-pathogen combinations. This report provides a comprehensive conceptual paper that forms the basis for a wider process of consultation and for case studies applying the framework

A comparison of risk assessments on Campylobacter in broiler meat

In recent years, several quantitative risk assessments for Campylobacter in broiler meat have been developed to support risk managers in controlling this pathogen. The models encompass some or all of the consecutive stages in the broiler meat production chain: primary production, industrial processing, consumer food preparation, and the dose–response relationship. The modelling approaches vary between the models, and this has supported the progress of risk assessment as a research discipline. The risk assessments are not only used to assess the human incidence of campylobacteriosis due to contaminated broiler meat, but more importantly for analyses of the effects of control measures at different stages in the broiler meat production chain.This review paper provides a comparative overview of models developed in the United Kingdom, Denmark, the Netherlands and Germany, and aims to identify differences and similarities of these existing models. Risk assessments developed for FAO/WHO and in New Zealand are also briefly discussed.Although the dynamics of the existing models may differ substantially, there are some similar conclusions shared between all models. The continuous introduction of Campylobacter in flocks implies that monitoring for Campylobacter at the farm up to one week before slaughter may result in flocks that are falsely tested negative: once Campylobacter is established at the farm, the within-flock prevalence increases dramatically within a week. Consequently, at the point of slaughter, the prevalence is most likely to be either very low ( 95%). In evaluating control strategies, all models find a negligible effect of logistic slaughter, the separate processing of positive and negative flocks. Also, all risk assessments conclude that the most effective intervention measures aim at reducing the Campylobacter concentration, rather than reducing the prevalence. During the stage where the consumer handles the food, cross-contamination is generally considered to be more relevant than undercooking. An important finding, shared by all, is that the tails of the distributions describing the variability in Campylobacter concentrations between meat products and meals determine the risks, not the mean values of those distributions.Although a unified model for risk assessment of Campylobacter in the broiler meat production would be desirable in order to promote a European harmonized approach, it is neither feasible nor desirable to merge the different models into one generic risk assessment model. The purpose of such a generic model has yet to be defined at a European level and the large variety in practices between countries, especially related to consumer food preparation and consumption, complicates a unified approach