Interventions Targeting Deep Tissue Lymph Nodes May Not Effectively Reduce the Risk of Salmonellosis from Ground Pork Consumption: A Quantitative Microbial Risk Assessment

The inclusion of deep tissue lymph nodes (DTLNs) or nonvisceral lymph nodes contaminated with Salmonella in wholesale fresh ground pork (WFGP) production may pose risks to public health. To assess the relative contribution of DTLNs to human salmonellosis occurrence associated with ground pork consumption and to investigate potential critical control points in the slaughter‐to‐table continuum for the control of human salmonellosis in the United States, a quantitative microbial risk assessment (QMRA) model was established. The model predicted an average of 45 cases of salmonellosis (95% CI = [19, 71]) per 100,000 Americans annually due to WFGP consumption. Sensitivity analysis of all stochastic input variables showed that cooking temperature was the most influential parameter for reducing salmonellosis cases associated with WFGP meals, followed by storage temperature and Salmonella concentration on contaminated carcass surface before fabrication. The input variables were grouped to represent three main factors along the slaughter‐to‐table chain influencing Salmonella doses ingested via WFGP meals: DTLN‐related factors, factors at processing other than DTLNs, and consumer‐related factors. The evaluation of the impact of each group of factors by second‐order Monte Carlo simulation showed that DTLN‐related factors had the lowest impact on the risk estimate among the three groups of factors. These findings indicate that interventions to reduce Salmonella contamination in DTLNs or to remove DTLNs from WFGP products may be less critical for reducing human infections attributable to ground pork than improving consumers’ cooking habits or interventions of carcass decontamination at processing.     

Quantitative Risk Assessment of Human Salmonellosis in Canadian Broiler Chicken Breast from Retail to Consumption

The current quantitative risk assessment model followed the framework proposed by the Codex Alimentarius to provide an estimate of the risk of human salmonellosis due to consumption of chicken breasts which were bought from Canadian retail stores and prepared in Canadian domestic kitchens. The model simulated the level of Salmonella contamination on chicken breasts throughout the retail‐to‐table pathway. The model used Canadian input parameter values, where available, to represent risk of salmonellosis. From retail until consumption, changes in the concentration of Salmonella on each chicken breast were modeled using equations for growth and inactivation. The model predicted an average of 318 cases of salmonellosis per 100,000 consumers per year. Potential reasons for this overestimation were discussed. A sensitivity analysis showed that concentration of Salmonella on chicken breasts at retail and food hygienic practices in private kitchens such as cross‐contamination due to not washing cutting boards (or utensils) and hands after handling raw meat along with inadequate cooking contributed most significantly to the risk of human salmonellosis. The outcome from this model emphasizes that responsibility for protection from Salmonella hazard on chicken breasts is a shared responsibility. Data needed for a comprehensive Canadian Salmonella risk assessment were identified for future research.     

A Practical Framework for the Construction of a Biotracing Model: Application to Salmonella in the Pork Slaughter Chain

A novel purpose of the use of mathematical models in quantitative microbial risk assessment (QMRA) is to identify the sources of microbial contamination in a food chain (i.e., biotracing). In this article we propose a framework for the construction of a biotracing model, eventually to be used in industrial food production chains where discrete numbers of products are processed that may be contaminated by a multitude of sources. The framework consists of steps in which a Monte Carlo model, simulating sequential events in the chain following a modular process risk modeling (MPRM) approach, is converted to a Bayesian belief network (BBN). The resulting model provides a probabilistic quantification of concentrations of a pathogen throughout a production chain. A BBN allows for updating the parameters of the model based on observational data, and global parameter sensitivity analysis is readily performed in a BBN. Moreover, a BBN enables “backward reasoning” when downstream data are available and is therefore a natural framework for answering biotracing questions. The proposed framework is illustrated with a biotracing model of Salmonella in the pork slaughter chain, based on a recently published Monte Carlo simulation model. This model, implemented as a BBN, describes the dynamics of Salmonella in a Dutch slaughterhouse and enables finding the source of contamination of specific carcasses at the end of the chain.     

Structured expert elicitation about Listeria monocytogenes cross-contamination in the environment of retail deli operations in the United States

Listeria monocytogenes is among the foodborne pathogens with the highest death toll in the United States. Ready‐to‐eat foods contaminated at retail are an important source of infection. Environmental sites in retail deli operations can be contaminated. However, commonly contaminated sites are unlikely to come into direct contact with food and the public health relevance of environmental contamination has remained unclear. To identify environmental sites that may pose a considerable cross‐contamination risk, to elucidate potential transmission pathways, and to identify knowledge gaps, we performed a structured expert elicitation of 41 experts from state regulatory agencies and the food retail industry with practical experience in retail deli operations. Following the “Delphi” method, the elicitation was performed in three consecutive steps: questionnaire, review and discussion of results, second questionnaire. Hands and gloves were identified as important potential contamination sources. However, bacterial transfers to and from hands or gloves represented a major data gap. Experts agreed about transfer probabilities from cutting boards, scales, deli cases, and deli preparation sinks to product, and about transfer probabilities from floor drains, walk‐in cooler floors, and knife racks to food contact surfaces. Comparison of experts' opinions to observational data revealed a tendency among experts with certain demographic characteristics and professional opinions to overestimate prevalence. Experts’ votes clearly clustered into separate groups not defined by place of employment, even though industry experts may have been somewhat overrepresented in one cluster. Overall, our study demonstrates the value and caveats of expert elicitation to identify data gaps and prioritize research efforts.     

A Risk Assessment of Campylobacteriosis and Salmonellosis Linked to Chicken Meals Prepared in Households in Dakar,Senegal

We used a quantitative microbiological risk assessment model to describe the risk of Campylobacter and Salmonella infection linked to chicken meals prepared in households in Dakar, Senegal. The model uses data collected specifically for this study, such as the prevalence and level of bacteria on the neck skin of chickens bought in Dakar markets, time‐temperature profiles recorded from purchase to consumption, an observational survey of meal preparation in private kitchens, and detection and enumeration of pathogens on kitchenware and cooks’ hands. Thorough heating kills all bacteria present on chicken during cooking, but cross‐contamination of cooked chicken or ready‐to‐eat food prepared for the meal via kitchenware and cooks’ hands leads to a high expected frequency of pathogen ingestion. Additionally, significant growth of Salmonella is predicted during food storage at ambient temperature before and after meal preparation. These high exposures lead to a high estimated risk of campylobacteriosis and/or salmonellosis in Dakar households. The public health consequences could be amplified by the high level of antimicrobial resistance of Salmonella and Campylobacter observed in this setting. A significant decrease in the number of ingested bacteria and in the risk could be achieved through a reduction of the prevalence of chicken contamination at slaughter, and by the use of simple hygienic measures in the kitchen. There is an urgent need to reinforce the hygiene education of food handlers in Senegal.     

Probability of Exporting Infected Carcasses from Vaccinated Pigs Following a Foot‐and‐Mouth Disease Epidemic

Emergency vaccination is an effective control strategy for foot‐and‐mouth disease (FMD) epidemics in densely populated livestock areas, but results in a six‐month waiting period before exports can be resumed, incurring severe economic consequences for pig exporting countries. In the European Union, a one‐month waiting period has been discussed based on negative test results in a final screening. The objective of this study was to analyze the risk of exporting FMD‐infected pig carcasses from a vaccinated area: (1) directly after final screening and (2) after a six‐month waiting period. A risk model has been developed to estimate the probability that a processed carcass was derived from an FMD‐infected pig (P_carc). Key variables were herd prevalence (P_H), within‐herd prevalence (P_A), and the probability of detection at slaughter (P_SL). P_H and P_A were estimated using Bayesian inference under the assumption that, despite all negative test results, ≥1 infected pigs were present. Model calculations indicated that P_carc was on average 2.0 × 10^?5 directly after final screening, and 1.7 × 10^?5 after a six‐month waiting period. Therefore, the additional waiting time did not substantially reduce P_carc. The estimated values were worst‐case scenarios because only viraemic pigs pose a risk for disease transmission, while seropositive pigs do not. The risk of exporting FMD via pig carcasses from a vaccinated area can further be reduced by heat treatment of pork and/or by excluding high‐risk pork products from export.     

A Mathematical Model for Pathogen Cross‐Contamination Dynamics during the Postharvest Processing of Leafy Greens

We developed a probabilistic mathematical model for the postharvest processing of leafy greens focusing on Escherichia coli O157:H7 contamination of fresh‐cut romaine lettuce as the case study. Our model can (i) support the investigation of cross‐contamination scenarios, and (ii) evaluate and compare different risk mitigation options. We used an agent‐based modeling framework to predict the pathogen prevalence and levels in bags of fresh‐cut lettuce and quantify spread of E. coli O157:H7 from contaminated lettuce to surface areas of processing equipment. Using an unbalanced factorial design, we were able to propagate combinations of random values assigned to model inputs through different processing steps and ranked statistically significant inputs with respect to their impacts on selected model outputs. Results indicated that whether contamination originated on incoming lettuce heads or on the surface areas of processing equipment, pathogen prevalence among bags of fresh‐cut lettuce and batches was most significantly impacted by the level of free chlorine in the flume tank and frequency of replacing the wash water inside the tank. Pathogen levels in bags of fresh‐cut lettuce were most significantly influenced by the initial levels of contamination on incoming lettuce heads or surface areas of processing equipment. The influence of surface contamination on pathogen prevalence or levels in fresh‐cut bags depended on the location of that surface relative to the flume tank. This study demonstrates that developing a flexible yet mathematically rigorous modeling tool, a “virtual laboratory,” can provide valuable insights into the effectiveness of individual and combined risk mitigation options.     

Salmonella Prevalence Alone Is Not a Good Indicator of Poultry Food Safety

Salmonella is a leading cause of foodborne illness (i.e., salmonellosis) outbreaks, which on occasion are attributed to ground turkey. The poultry industry uses Salmonella prevalence as an indicator of food safety. However, Salmonella prevalence is only one of several factors that determine risk of salmonellosis. Consequently, a model for predicting risk of salmonellosis from individual lots of ground turkey as a function of Salmonella prevalence and other risk factors was developed. Data for Salmonella contamination (prevalence, number, and serotype) of ground turkey were collected at meal preparation. Scenario analysis was used to evaluate effects of model variables on risk of salmonellosis. Epidemiological data were used to simulate Salmonella serotype virulence in a dose‐response model that was based on human outbreak and feeding trial data. Salmonella prevalence was 26% (n = 100) per 25 g of ground turkey, whereas Salmonella number ranged from 0 to 1.603 with a median of 0.185 log per 25 g. Risk of salmonellosis (total arbitrary units (AU) per lot) was affected (p ≤ 0.05) by Salmonella prevalence, number, and virulence, by incidence and extent of undercooking, and by food consumption behavior and host resistance but was not (p > 0.05) affected by serving size, serving size distribution, or total bacterial load of ground turkey when all other risk factors were held constant. When other risk factors were not held constant, Salmonella prevalence was not correlated (r = ?0.39; p = 0.21) with risk of salmonellosis. Thus, Salmonella prevalence alone was not a good indicator of poultry food safety because other factors were found to alter risk of salmonellosis. In conclusion, a more holistic approach to poultry food safety, such as the process risk model developed in the present study, is needed to better protect public health from foodborne pathogens like Salmonella.     

Modeling Retrospective Attribution of Responsibility to Hazard‐Managing Institutions: An Example Involving a Food Contamination Incident

Perceptions of institutions that manage hazards are important because they can affect how the public responds to hazard events. Antecedents of trust judgments have received far more attention than antecedents of attributions of responsibility for hazard events. We build upon a model of retrospective attribution of responsibility to individuals to examine these relationships regarding five classes of institutions that bear responsibility for food safety: producers (e.g., farmers), processors (e.g., packaging firms), watchdogs (e.g., government agencies), sellers (e.g., supermarkets), and preparers (e.g., restaurants). A nationally representative sample of 1,200 American adults completed an Internet‐based survey in which a hypothetical scenario involving contamination of diverse foods with Salmonella served as the stimulus event. Perceived competence and good intentions of the institution moderately decreased attributions of responsibility. A stronger factor was whether an institution was deemed (potentially) aware of the contamination and free to act to prevent or mitigate it. Responsibility was rated higher the more aware and free the institution. This initial model for attributions of responsibility to impersonal institutions (as opposed to individual responsibility) merits further development.     

Development of a Self‐Regulated Dynamic Model for the Propagation of Salmonella Typhimurium in Pig Farms

A self‐regulated epidemic model was developed to describe the dynamics of Salmonella Typhimurium in pig farms and predict the prevalence of different risk groups at slaughter age. The model was focused at the compartment level of the pig farms and it included two syndromes, a high and a low propagation syndrome. These two syndromes generated two different classes of pigs, the High Infectious and the Low Infectious, respectively, which have different shedding patterns. Given the two different classes and syndromes, the Infectious Equivalent concept was used, which reflected the combination of High and Low Infectious pigs needed for the high propagation syndrome to be triggered. Using the above information a new algorithm was developed that decides, depending on the Infectious Equivalent, which of the two syndromes should be triggered. Results showed that the transmission rate of S. Typhimurium for the low propagation syndrome is around 0.115, pigs in Low Infectious class contribute to the transmission of the infection by 0.61–0.80 of pigs in High Infectious class and that the Infectious Equivalent should be above 10–14% of the population in order for the high propagation syndrome to be triggered. This self‐regulated dynamic model can predict the prevalence of the classes and the risk groups of pigs at slaughter age for different starting conditions of infection.     

Leveraging risk assessment for foodborne outbreak investigations: The Quantitative Risk Assessment-Epidemic Curve Prediction Model

Root cause analysis can be used in foodborne illness outbreak investigations to determine the underlying causes of an outbreak and to help identify actions that could be taken to prevent future outbreaks. We developed a new tool, the Quantitative Risk Assessment-Epidemic Curve Prediction Model (QRA-EC), to assist with these goals and applied it to a case study to investigate and illustrate the utility of leveraging quantitative risk assessment to provide unique insights for foodborne illness outbreak root cause analysis. We used a 2019 Salmonella outbreak linked to melons as a case study to demonstrate the utility of this model (Centers for Disease Control and Prevention [CDC], 2019). The model was used to evaluate the impact of various root cause hypotheses (representing different contamination sources and food safety system failures in the melon supply chain) on the predicted number and timeline of illnesses. The predicted number of illnesses varied by contamination source and was strongly impacted by the prevalence and level of Salmonella contamination on the surface/inside of whole melons and inside contamination niches on equipment surfaces. The timeline of illnesses was most strongly impacted by equipment sanitation efficacy for contamination niches. Evaluations of a wide range of scenarios representing various potential root causes enabled us to identify which hypotheses, were likely to result in an outbreak of similar size and illness timeline to the 2019 Salmonella melon outbreak. The QRA-EC framework can be adapted to accommodate any food–pathogen pairs to provide insights for foodborne outbreak investigations.     

Evaluation of Inhaled Versus Deposited Dose Using the Exponential Dose‐Response Model for Inhalational Anthrax in Nonhuman Primate,Rabbit, and Guinea Pig

The application of the exponential model is extended by the inclusion of new nonhuman primate (NHP), rabbit, and guinea pig dose‐lethality data for inhalation anthrax. Because deposition is a critical step in the initiation of inhalation anthrax, inhaled doses may not provide the most accurate cross‐species comparison. For this reason, species‐specific deposition factors were derived to translate inhaled dose to deposited dose. Four NHP, three rabbit, and two guinea pig data sets were utilized. Results from species‐specific pooling analysis suggested all four NHP data sets could be pooled into a single NHP data set, which was also true for the rabbit and guinea pig data sets. The three species‐specific pooled data sets could not be combined into a single generic mammalian data set. For inhaled dose, NHPs were the most sensitive (relative lowest LD₅₀) species and rabbits the least. Improved inhaled LD₅₀s proposed for use in risk assessment are 50,600, 102,600, and 70,800 inhaled spores for NHP, rabbit, and guinea pig, respectively. Lung deposition factors were estimated for each species using published deposition data from Bacillus spore exposures, particle deposition studies, and computer modeling. Deposition was estimated at 22%, 9%, and 30% of the inhaled dose for NHP, rabbit, and guinea pig, respectively. When the inhaled dose was adjusted to reflect deposited dose, the rabbit animal model appears the most sensitive with the guinea pig the least sensitive species.     

A Risk Assessment Model for Enterotoxigenic Staphylococcus aureus in Pasteurized Milk: A Potential Route to Source‐Level Inference

This article describes a probabilistic model that quantifies hazards that arise from Staphylococcus aureus in milk that is sold as pasteurized in the United Kingdom. The model is centered on coupled dynamics for S. aureus populations, staphylococcal enterotoxins, and the concentration of alkaline phosphatase throughout the milk chain. The chain includes farm collection and storage of pooled milk, further pooling for off‐farm processing, high temperature short time thermal processing, and possible postprocess contamination. The model is implemented as a Bayesian belief network. The results indicate that milk sold as pasteurized is relatively safe with respect to the hazards associated with S. aureus and that most risk is associated with small scale on‐farm processing. An additional analysis of likelihood ratios shows that alkaline phosphatase concentrations in filler tank milk are a good indicator of potential hazards and that these concentrations, in conjunction with other measurements, can be used effectively to discriminate over possible failure modes. The ability to discriminate over potential failure modes can support preemptive actions, such as maintenance or hygiene, which assist with milk chain management and, over extended periods, accumulate to drive improved safety, efficiency, and security.     

Evaluation of Scenarios for Reducing Human Salmonellosis Through Household Consumption of Fresh Minced Pork Meat

Nontyphoidal salmonellosis is the second most frequently reported zoonotic disease in the European Union (EU) and is considered to be a major threat to human health worldwide. The most reported Salmonella serovar in the EU is S. Enteritidis, mainly associated with egg contamination, followed by S. Typhimurium, with the latter being the most predominant serovar isolated from pork. These findings suggest that reducing the Salmonella contamination in the pork production might be a good strategy to prevent and control human salmonellosis in the EU. Recently, a quantitative microbial risk assessment (QMRA) has been developed to assess the risks for human salmonellosis due to home consumption of fresh minced pork meat in Belgium.⁽ 1 ⁾ The newly developed risk model is called the METZOON model. In the current study, the METZOON model was used to evaluate the effectiveness of different hypothetical Salmonella mitigation strategies implemented at different stages of the minced pork production and consumption chain by means of a scenario analysis. To efficiently evaluate the mitigation strategies, model results were obtained by running simulations using the randomized complete block design. The effectiveness of a mitigation strategy is expressed using point and interval estimates of the effect size for dependent observations, expressed as the standardized difference in population means. The results indicate that the most effective strategies are taken during the slaughter processes of polishing, evisceration, and chilling, and during postprocessing, whereas interventions in the primary production and at the beginning of the slaughter process seem to have only a limited effect. Improving consumer awareness is found to be effective as well.     

Variability and Uncertainty Analysis of the Cross‐Contamination Ratios of Salmonella During Pork Cutting

The transfer ratio of bacteria from one surface to another is often estimated from laboratory experiments and quantified by dividing the expected number of bacteria on the recipient surface by the expected number of bacteria on the donor surface. Yet, the expected number of bacteria on each surface is uncertain due to the limited number of colonies that are counted and/or samples that can be analyzed. The expected transfer ratio is, therefore, also uncertain and its estimate may exceed 1 if real transfer is close to 100%. In addition, the transferred fractions vary over experiments but it is unclear, using this approach, how to combine uncertainty and variability into one estimate for the transfer ratio. A Bayesian network model was proposed that allows the combination of uncertainty within one experiment and variability over multiple experiments and prevents inappropriate values for the transfer ratio. Model functionality was shown using data from a laboratory experiment in which the transfer of Salmonella was determined from contaminated pork meat to a butcher's knife, and vice versa. Recovery efficiency of bacteria from both surfaces was also determined and accounted for in the analysis. Transfer ratio probability distributions showed a large variability, with a mean value of 0.19 for the transfer of Salmonella from pork meat to the knife and 0.58 for the transfer of Salmonella from the knife to pork meat. The proposed Bayesian model can be used for analyzing data from similar study designs in which uncertainty should be combined with variability.     

Salmonella Enteritidis Surveillance by Egg Immunology: Impact of the Sampling Scheme on the Release of Contaminated Table Eggs

Design of surveillance programs to detect infections could benefit from more insight into sampling schemes. We address the effect of sampling schemes for Salmonella Enteritidis surveillance in laying hens. Based on experimental estimates for the transmission rate in flocks, and the characteristics of an egg immunological test, we have simulated outbreaks with various sampling schemes, and with the current boot swab program with a 15‐week sampling interval. Declaring a flock infected based on a single positive egg was not possible because test specificity was too low. Thus, a threshold number of positive eggs was defined to declare a flock infected, and, for small sample sizes, eggs from previous samplings had to be included in a cumulative sample to guarantee a minimum flock level specificity. Effectiveness of surveillance was measured by the proportion of outbreaks detected, and by the number of contaminated table eggs brought on the market. The boot swab program detected 90% of the outbreaks, with 75% fewer contaminated eggs compared to no surveillance, whereas the baseline egg program (30 eggs each 15 weeks) detected 86%, with 73% fewer contaminated eggs. We conclude that a larger sample size results in more detected outbreaks, whereas a smaller sampling interval decreases the number of contaminated eggs. Decreasing sample size and interval simultaneously reduces the number of contaminated eggs, but not indefinitely: the advantage of more frequent sampling is counterbalanced by the cumulative sample including less recently laid eggs. Apparently, optimizing surveillance has its limits when test specificity is taken into account.     

Risk of Illness with Salmonella due to Consumption of Raw Unwashed Vegetables Irrigated with Water from the Bogotá River

The Bogotá River receives untreated wastewater from the city of Bogotá and many other towns. Downstream from Bogotá, water from the river is used for irrigation of crops. Concentrations of indicator organisms in the river are high, which is consistent with fecal contamination. To investigate the probability of illness due to exposure to enteric pathogens from the river, specifically Salmonella, we took water samples from the Bogotá River at six sampling locations in an area where untreated water from the river is used for irrigation of lettuce, broccoli, and cabbage. Salmonella concentrations were quantified by direct isolation and qPCR. Concentrations differed, depending on the quantification technique used, ranging between 10^7.7 and 10^9.9 number of copies of gene invA per L and 10^5.3 and 10^8.4 CFU/L, for qPCR and direct isolation, respectively. A quantitative microbial risk assessment model that estimates the daily risk of illness with Salmonella resulting from consuming raw unwashed vegetables irrigated with water from the Bogotá River was constructed using the Salmonella concentration data. The daily probability of illness from eating raw unwashed vegetables ranged between 0.62 and 0.85, 0.64 and 0.86, and 0.64 and 0.85 based on concentrations estimated by qPCR (0.47–0.85, 0.47–0.86, and 0.41–0.85 based on concentrations estimated by direct isolation) for lettuce, cabbage, and broccoli, respectively, which are all above the commonly propounded benchmark of 10^?4 per year. Results obtained in this study highlight the necessity for appropriate wastewater treatment in the region, and emphasize the importance of postharvest practices, such as washing, disinfecting, and cooking.     

Assessing the effect on the public health risk of current and alternative border control of Salmonella Typhimurium and Enteritidis in imported frozen poultry meat in Jordan

The JFDA applies border control for Salmonella Typhimurium and Salmonella Enteritidis in frozen poultry products. A QMRA model was developed to evaluate the effectiveness of this system in controlling the risk for consumers. The model consists of three modules; consumer phase, risk estimation, and risk reduction. The model inputs were the occurrence of Salmonella in different types of imported poultry products, the LOD of the Rapid’Salmonella, the number of tested samples of each batch, and the criteria for rejection. The model outputs were public health impact as the Minimum Relative Residual Risk (MRRR) given the batches’ refusal and the percentage of Batches that are Not-compliant with the Microbiological Criteria (BNMC) of rejection. To estimate the overall MRRR of the border control, the estimated country and product-specific MRRR were summarized and weighted by the total imports of each product from each country. The current border control based on one sample per batch gives an overall MRRR value of 27%. The alternative scenarios based on three and five samples per batch are 12% and 8%, respectively. Overall, the higher the prevalence and/or concentration of Salmonella in imported products, the more the likelihood that batches will be rejected. For products with up-to-date data of occurrence, the estimated BNMC was similar to the observed proportion of rejected batches. The lack of data on the Salmonella concentrations in poultry products from different countries is the major source of the uncertainties in the model. It reduces our opportunities to obtain valid estimates of the absolute risk.     

A Model for Setting Performance Objectives for Salmonella in the Broiler Supply Chain

A stochastic model for setting performance objectives for Salmonella in the broiler supply chain was developed. The goal of this study was to develop a model by which performance objectives for Salmonella prevalence at various points in the production chain can be determined, based on a preset final performance objective at the end of the processing line. The transmission of Salmonella through the broiler production chain was modeled. The prevalence at flock level was calculated from the measured prevalence at sample level. The transmission model is based on data on the occurrence of Salmonella collected in the Dutch broiler production chain during several years. The developed model can be used by policymakers and industry to determine economically and politically acceptable performance objectives for various points of the production chain and to draw conclusions about which interventions are most appropriate.     

Modeling of interventions for reducing external Enterobacteriaceae contamination of broiler carcasses during processing

This article presents a mathematical model for the Enterobacteriaceae count on the surface of broiler chicken during slaughter and how it may be affected by different processing technologies. The model is based on a model originally developed for Campylobacter and has been adapted for Enterobacteriaceae using a Bayesian updating approach and hitherto unpublished data gathered from German abattoirs. The slaughter process in the model consists of five stages: input, scalding, defeathering, evisceration, washing, and chilling. The impact of various processing technologies along the broiler processing line on the Enterobacteriaceae count on the carcasses’ surface has been determined from literature data. The model is implemented in the software R and equipped with a graphical user interface which allows interactively to choose among different processing technologies for each stage along the processing line. Based on the choice of processing technologies the model estimates the Enterobacteriaceae count on the surface of each broiler chicken at each stage of processing. This result is then compared to a so-called baseline model which simulates a processing line with a fixed set of processing technologies. The model calculations showed how even very effective removal of bacteria on the exterior of the carcass in a previous step will be undone by the cross-contamination with leaked feces, if feces contain high concentrations of bacteria.