Food Modelling Journal : FSKX (Food Safety Knowledge)
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FSKX (Food Safety Knowledge)
Quantitative microbial risk assessment for Salmonella in eggs
expand article infoVirginie Desvignes, Tasja Buschhardt§, Laurent Guillier, Moez Sanaa
‡ ANSES - French Agency for Food, Environmental and Occupational Health and Safety, Maisons-Alfort, France
§ BfR- Federal Institute for Risk Assessment, Berlin, Germany
Open Access

Abstract

The scope of this quantitative risk assessment model is to estimate the number of salmonellosis cases per million servings of table egg, as well as the probability of illness when ingesting a random serving of table egg. The model describes the potential egg contamination by Salmonella Enteritidis from farm to fork according to time/temperature storage conditions, as well as consumption practices.

Keywords

QMRA model, eggs, Salmonella Enteritidis, R programming language

Introduction

There were 91,662 human salmonellosis cases reported in Europe in 2017 (19.7 cases per 100,000 population) by the European Food Safety Authority and European Centre for Disease Prevention and Control (EFSA and ECDC 2018). The more prevalent serovar in human cases acquired in the EU during 2017 is Salmonella Enteritidis. The food and animal monitoring data showed that S. Enteritidis was mainly associated with laying hens and next with broiler meat. In Europe, amongst 269 Salmonella food-borne outbreaks reported with strong-evidence on the implicated food vehicle, 99 (37%) were associated with eggs and egg products. S. Enteritidis has been causing large outbreaks associated with international trade in table eggs (Dallman et al. 2016). The number of cases of human salmonellosis in the European Union has been increasing since 2014 (EFSA Panel on Biological Hazards et al. 2019). One of the reasons given could be the increase in the prevalence of Salmonella Enteritidis in laying hens (De Cesare 2018).

This work provides a generic model for assessing the risk of salmonellosis associated with the consumption of table eggs. It estimates the expected number of salmonellosis cases based on the prevalence of egg contamination and the temperature profiles of egg storage. The exposure assessment model comprises six process steps of the egg food chain from lay to consumption: on farm before collection, during grading step, during transport to wholesale, during storage at wholesale, at retail and at household. The model was adapted from “Scientific Opinion on the public health risks of table eggs due to deterioration and development of pathogens” (EFSA BIOHAZ Panel (EFSA Panel on Biological Hazards) 2014). Model parameters used to describe the growth, the rupture of the yolk membrane or for the dose response were taken from the EFSA opinion. The prevalence and the condition storage of the eggs were set to new values for illustrating the output of the model. The consumer phase model considers three cooking scenarios: uncooked, lightly and well-cooked. A beta-Poisson dose response model (Thomas et al. 2006) is then used to estimate the probability of illness per S. Enteritidis-contaminated serving of egg for each of the egg-cooking scenarios. This model is avalaible in Food Safety Knowledge Markup Language (FSK-ML) format to facilitate its reuse. This open information exchange format is based on harmonised terms, metadata and controlled vocabulary to harmonise annotations of risk assessment models (Haberbeck et al. 2018). This format also contained model script, visualisation script and simulation settings (de Alba Aparicio et al. 2018).

Model metadata

General metadata

Source: RISK ASSESSMENTS: Risk assessments models

Identifier: QMRA_Salmonella_egg

Rights: Creative Commons Attribution-NonCommercial 4.0

Software: R

Product/matrix

Product Name: Eggs Chicken

Product Unit: Piece

Hazard

Hazard Name: Salmonella

Hazard Description: Salmonella Enteritidis

Data background

Study Title: QMRA for Salmonella Enteritidis in eggs

Study Description: This QMRA assesses the risk to consumers posed by Salmonella Enteritidis associated with the consumption of table eggs. The model estimates (a) the number of illnesses per million servings of egg (uncooked, lightly or well-cooked) and (b) the probability of illness when ingesting a random serving of egg.

Material and methods

Fig. 1 describes the global method of risk assessment of salmonellosis due to S. Enteritidis related to egg consumption. This model includes an exposure model, that considers six steps along the farm to household and a dose-reponse model for risk characterisation.

Figure 1.  

Global method of risk assessment.

Exposure model

The exposure assessment model is composed of six steps. The evolution of the contamination along theses steps is assessed with the model equations described below. It takes into account the time and temperature conditions described in default simulation settings. Three scenarios of egg cooking are defined to assess the probability of illness: uncooked, lightly and well-cooked. The reduction of concentrations is applied to the initial concentration of S. Enteritidis in egg. Risk is evaluated when a random serving is ingested (i.e. either lightly or well-cooked egg).

Hazard characterisation

The beta-Poisson dose response model was used to characterise the relationship between the ingested dose and the probability of salmonellosis. In this model, the variability in host-pathogen interaction is assumed to be beta distributed. The parameterisation of Thomas et al. (2006) was chosen.

Risk characterisation

Risk was estimated through different metrics (risk per serving or number of illnesses per millions of serving) for the different cooking modes of the eggs. The default number of simulations to explore variability was set to 2,000.

Model equations

At each step, the yolk membrane breakdown time and the expected growth of S. Enteritidis following the membrane breakdown are estimated. These two parameters depend on the internal temperature of egg which varies at each step of the process. At the end of the six steps, the number of ingested bacteria is calculated and the dose-response model estimates the probability of illness. After that, risk is evaluated.

The yolk membrane breakdown time

In this model, we only considered the internal contamination of egg with S. Enteritidis. Growth models of S. Enteritidis are different in egg yolk and albumen. While the yolk membrane does not break down, we consider the growth of S. Enteritidis albumen as insignificant. However, the resistance of the yolk membrane can decrease, for example, with the vibrations during the transport phase. At the storage phase, the resistance of the membrane depends on the time and temperature. At ambient temperature, the yolk membrane is rapidly damaged, leading to the yolk diffusion in egg albumen and contamination with S. Enteritidis. After the breakdown of the yolk membrane, the distribution of S. Enteritidis is considered to be uniform in the egg.

\((log_{10} TRMV=a+b \times T)\)

where \(TRMV\) is the time of the yolk membrane breakdown (day), \(a\) is the intercept of the equation (\(a\)= 2.0872 hours) (Thomas et al. 2006), \(b\) is the slope of the equation (\(b\) = -0.042579 hours/°C) (Thomas et al. 2006) and \(T\) is the temperature (°C).

The estimation of the time required for the yolk membrane breakdown becomes more complicated because the temperature is no longer constant and changes over time. The calculation of the breakdown time is, therefore, done step by step at each temperature change. In order to determine whether or not there is a rupture, the \(TRMV\) is calculated hourly as a function of temperature and the cumulative sum of the inverse of the different previous \(TRMVs\). If the sum of the inverses of the \(TRMVs\) is less than 1, then the membrane is considered still intact, otherwise (cumulative sum greater than or equal to 1), the yolk membrane is no longer intact and Salmonella growth then becomes possible.

Expected growth of S. Enteritidis following membrane breakdown

Using the Rosso growth rate equation (Delignette-Muller and Rosso 2000, Rosso et al. 1993, Rosso et al. 1995, Whiting et al. 2000), in particular \(\mu_{opt}\) also called kopt in the model script:

\((\mu_{max}=\mu_{opt} \times \gamma(T))\)

with \(\gamma(T) = \begin{cases}0 & \quad T < T_{min}\\\frac{(T-T_{max})(T-T_{min})^{2} }{(T_{opt}-T_{min})[(T_{opt}-T_{min})(T-T_{opt})-(T_{opt}-T_{max})(T_{opt}+T_{min}-2T)]} & \quad T_{min} \leq T \leq T_{max}\\0 & \quad T > T_{max}\end{cases}\)

where \(\mu_{max}\) is the growth rate (log10CFU/g per hour) at temperature \(T\) (°C), \(\mu_{opt}\) is the optimal growth rate (1.6) and \(\gamma(T)\) is the growth mitigation factor, \(T_{min}\) is the minimal growth temperature (6.29°C), \(T_{opt}\) is the optimum growth temperature (40.11°C) and \(T_{max}\) is the maximum growth temperature (43.46°C) (EFSA BIOHAZ Panel (EFSA Panel on Biological Hazards) 2014).

The decrease of internal egg temperature

The temperature of a laid egg is 41.2°C (Thomas et al. 2006) and then this temperature decreases progressively, according to the environmental conditions of the different process steps like production, transport and storage. The formula of the temperature decrease (EFSA BIOHAZ Panel (EFSA Panel on Biological Hazards) 2014) is:

\(T = T_{s} +(T_{i}-T_{s})\space exp(-kr_{s}t_{s})\)

where \(T\) is th egg temperature (°C), \(T_{s}\) is the storage temperature in stage \(s\), \(T_{i}\) is the internal temperature at the start of the time interval, \(kr_{s}\) is the cooling rate constant in stage \(s\) and \(t_{s}\) is the time spent in stage \(s\). \(T_{s}\) follows a Pert distribution with \(minT\), \(modeT\) and \(maxT\) as parameters, which vary at each step of the process (Tables 1, 3). The rapidity of internal tempearture decreasing depends on the storage place, in particular, on the ventilation systems and air humidity. Pert distributions were used to describe this variability of the cooling rate due to the conditions of storage. The values of Pert distributions differ according to the process step (Table 2). \(t_{s}\) follows a Pert distribtuion with \(minD\), \(modeD\) and \(maxD\) as parmeters, which vary at each process step (Tables 1, 3)

Parameters used to describe the variability of times and temperatures for the steps from farm to consumer kitchen.

Process step Minimum time period Mode time period Maximum time period Minimum temperature Mode temperature Maximum temperature
On farm, before collection MinD1 ModeD1 MaxD1 MinT1 ModeT1 MaxT1
Grading MinD4 ModeD4 MaxD4 MinT4 ModeT4 MaxT4
Transport to wholesale MinD5 ModeD5 MaxD5 MinT5 ModeT5 MaxT5
Storage at wholesale MinD6 ModeD6 MaxD6 MinT6 ModeT6 MaxT6
Retail MinD8 ModeD8 MaxD8 MinT8 ModeT8 MaxT8
Household MinD10 ModeD10 MaxD10 MinT10 ModeT10 MaxT10

Parameter values of the Pert distribution describing the cooling rate, according to the process step (Thomas et al. 2006)

Process step Minimum value Mode value Maximum value
On farm before collection 0.8 0.9 1
Grading 0.0528 0.08 0.1072
Transport to wholesale, Storage at wholesale, Retail, Household 0.066 0.1 0.134

Description of the model parameters of the quantitative microbial risk assessment for Salmonella Enteritidis in eggs, according to metadata schema of FSK-ML

minD1
parameterID minD1
parameterClassification input
parameterName minD1
parameterDescription minimum time period at farm (hours)
parameterUnit h
parameterUnitCategory time
parameterDataType integer
parameterValue 2
modeD1
parameterID modeD1
parameterClassification input
parameterName modeD1
parameterDescription mode time period at farm (hours)
parameterUnit h
parameterUnitCategory time
parameterDataType integer
parameterValue 6
maxD1
parameterID maxD1
parameterClassification input
parameterName maxD1
parameterDescription maximum time period at farm (hours)
parameterUnit h
parameterUnitCategory time
parameterDataType integer
parameterValue 13
minD4
parameterID minD4
parameterClassification input
parameterName minD4
parameterDescription minimum time period at grading (hours)
parameterUnit h
parameterUnitCategory time
parameterDataType integer
parameterValue 1
modeD4
parameterID modeD4
parameterClassification input
parameterName modeD4
parameterDescription mode time period at grading (hours)
parameterUnit h
parameterUnitCategory time
parameterDataType integer
parameterValue 2
maxD4
parameterID maxD4
parameterClassification input
parameterName maxD4
parameterDescription maximum time period at grading (hours)
parameterUnit h
parameterUnitCategory time
parameterDataType integer
parameterValue 4
minD5
parameterID minD5
parameterClassification input
parameterName minD5
parameterDescription minimum time period at transport (hours)
parameterUnit h
parameterUnitCategory time
parameterDataType integer
parameterValue 7
modeD5
parameterID modeD5
parameterClassification input
parameterName modeD5
parameterDescription mode time period at transport (hours)
parameterUnit h
parameterUnitCategory time
parameterDataType integer
parameterValue 48
maxD5
parameterID maxD5
parameterClassification input
parameterName maxD5
parameterDescription maximum time period at transport (hours)
parameterUnit h
parameterUnitCategory time
parameterDataType integer
parameterValue 72
minD6
parameterID minD6
parameterClassification input
parameterName minD6
parameterDescription minimum time period at storage/wholesale (hours)
parameterUnit h
parameterUnitCategory time
parameterDataType integer
parameterValue 1
modeD6
parameterID modeD6
parameterClassification input
parameterName modeD6
parameterDescription mode time period at storage/wholesale (hours)
parameterUnit h
parameterUnitCategory time
parameterDataType integer
parameterValue 5
maxD6
parameterID maxD6
parameterClassification input
parameterName maxD6
parameterDescription maximum time period at storage/wholesale (hours)
parameterUnit h
parameterUnitCategory time
parameterDataType integer
parameterValue 24
minD8
parameterID minD8
parameterClassification input
parameterName minD8
parameterDescription minimum time period at retail (hours)
parameterUnit h
parameterUnitCategory time
parameterDataType integer
parameterValue 1
modeD8
parameterID modeD8
parameterClassification input
parameterName modeD8
parameterDescription mode time period at retail (hours)
parameterUnit h
parameterUnitCategory time
parameterDataType integer
parameterValue 24
maxD8
parameterID maxD8
parameterClassification input
parameterName maxD8
parameterDescription maximum time period at retail (hours)
parameterUnit h
parameterUnitCategory time
parameterDataType integer
parameterValue 72
minD10
parameterID minD10
parameterClassification input
parameterName minD10
parameterDescription minimum time period at household (hours)
parameterUnit h
parameterUnitCategory time
parameterDataType integer
parameterValue 1
modeD10
parameterID modeD10
parameterClassification input
parameterName modeD10
parameterDescription mode time period at household (hours)
parameterUnit h
parameterUnitCategory time
parameterDataType integer
parameterValue 24
maxD10
parameterID maxD10
parameterClassification input
parameterName maxD10
parameterDescription maximum time period at household (hours)
parameterUnit h
parameterUnitCategory time
parameterDataType integer
parameterValue 336
minT1
parameterID minT1
parameterClassification input
parameterName minT1
parameterDescription minimum temperature at farm
parameterUnit °C
parameterUnitCategory temperature
parameterDataType integer
parameterValue 29
modeT1
parameterID modeT1
parameterClassification input
parameterName modeT1
parameterDescription mode temperature at farm
parameterUnit °C
parameterUnitCategory temperature
parameterDataType integer
parameterValue 30
maxT1
parameterID maxT1
parameterClassification input
parameterName maxT1
parameterDescription maximum temperature at farm
parameterUnit °C
parameterUnitCategory temperature
parameterDataType integer
parameterValue 35
minT4
parameterID minT4
parameterClassification input
parameterName minT4
parameterDescription minimum temperature at grading
parameterUnit °C
parameterUnitCategory temperature
parameterDataType integer
parameterValue 25
modeT4
parameterID modeT4
parameterClassification input
parameterName modeT4
parameterDescription mode temperature at grading
parameterUnit °C
parameterUnitCategory temperature
parameterDataType integer
parameterValue 27
maxT4
parameterID maxT4
parameterClassification input
parameterName maxT4
parameterDescription maximum temperature at grading
parameterUnit °C
parameterUnitCategory temperature
parameterDataType integer
parameterValue 30
minT5
parameterID minT5
parameterClassification input
parameterName minT5
parameterDescription minimum temperature at transport
parameterUnit °C
parameterUnitCategory temperature
parameterDataType integer
parameterValue 28
modeT5
parameterID modeT5
parameterClassification input
parameterName modeT5
parameterDescription mode temperature at transport
parameterUnit °C
parameterUnitCategory temperature
parameterDataType integer
parameterValue 30
maxT5
parameterID maxT5
parameterClassification input
parameterName maxT5
parameterDescription maximum temperature at transport
parameterUnit °C
parameterUnitCategory temperature
parameterDataType integer
parameterValue 33
minT6
parameterID minT6
parameterClassification input
parameterName minT6
parameterDescription minimum temperature at storage/wholesale
parameterUnit °C
parameterUnitCategory temperature
parameterDataType integer
parameterValue 25
modeT6
parameterID modeT6
parameterClassification input
parameterName modeT6
parameterDescription mode temperature at storage/wholesale
parameterUnit °C
parameterUnitCategory temperature
parameterDataType integer
parameterValue 27
maxT6
parameterID maxT6
parameterClassification input
parameterName maxT6
parameterDescription maximum temperature at storage/wholesale
parameterUnit °C
parameterUnitCategory temperature
parameterDataType integer
parameterValue 30
minT8
parameterID minT8
parameterClassification input
parameterName minT8
parameterDescription minimum temperature at retail
parameterUnit °C
parameterUnitCategory temperature
parameterDataType integer
parameterValue 20
modeT8
parameterID modeT8
parameterClassification input
parameterName modeT8
parameterDescription mode temperature at retail
parameterUnit °C
parameterUnitCategory temperature
parameterDataType integer
parameterValue 25
maxT8
parameterID maxT8
parameterClassification input
parameterName maxT8
parameterDescription maximum temperature at retail
parameterUnit °C
parameterUnitCategory temperature
parameterDataType integer
parameterValue 30
minT10
parameterID minT10
parameterClassification input
parameterName minT10
parameterDescription minimum temperature at household
parameterUnit °C
parameterUnitCategory temperature
parameterDataType integer
parameterValue 6
modeT10
parameterID modeT10
parameterClassification input
parameterName modeT10
parameterDescription mode temperature at household
parameterUnit °C
parameterUnitCategory temperature
parameterDataType integer
parameterValue 15
maxT10
parameterID maxT10
parameterClassification input
parameterName maxT10
parameterDescription maximum temperature at household
parameterUnit °C
parameterUnitCategory temperature
parameterDataType integer
parameterValue 30
sim
parameterID sim
parameterClassification input
parameterName sim
parameterDescription simulation parameter: iteration number
parameterUnit others
parameterUnitCategory other
parameterDataType integer
parameterValue 2000
parameterValueMin 1000.0
parameterValueMax 10000.0
prevalence
parameterID prevalence
parameterClassification input
parameterName prevalence
parameterDescription simulation parameter: prevalence of egg contamination
parameterUnit others
parameterUnitCategory other
parameterDataType double
parameterValue 0.1
parameterValueMin 0.0
parameterValueMax 1.0
freqcuisson
parameterID freqcuisson
parameterClassification input
parameterName freqcuisson
parameterDescription simulation parameter: frequency of well-cooked eggs
parameterUnit others
parameterUnitCategory other
parameterDataType double
parameterValue 0.9
parameterValueMin 0.0
parameterValueMax 1.0
Rlcuit
parameterID Rlcuit
parameterClassification output
parameterName Rlcuit
parameterDescription number of illnesses per million servings of lightly-cooked egg
parameterUnit others
parameterUnitCategory other
parameterDataType integer
Rbcuit
parameterID Rbcuit
parameterClassification output
parameterName Rbcuit
parameterDescription number of illnesses per million servings of well-cooked egg
parameterUnit others
parameterUnitCategory other
parameterDataType integer
Rlcuit_1
parameterID Rlcuit_1
parameterClassification output
parameterName Rlcuit_1
parameterDescription probability of illness when ingesting a serving of lightly-cooked egg
parameterUnit %
parameterUnitCategory arbitrary Fraction
parameterDataType double
Rbcuit_1
parameterID Rbcuit_1
parameterClassification output
parameterName Rbcuit_1
parameterDescription probability of illness when ingesting a serving of well-cooked egg
parameterUnit %
parameterUnitCategory arbitrary Fraction
parameterDataType double
Risque
parameterID Risque
parameterClassification output
parameterName risque
parameterDescription probability of illness when ingesting a random serving of egg
parameterUnit %
parameterUnitCategory arbitrary Fraction
parameterDataType double

Estimated number of Salmonella in one egg (CFU)

From the moment that growth becomes possible, the growth rate is re-assessed hourly according to the \(\gamma\) model already presented. The growth rates calculated hourly until the end of egg storage are then cumulated. At the end, a number of ten-fold increase (\(NCD\)) is obtained for each egg. In order to determine the concentration before the egg's preparation, data on the initial concentration, i.e. at the time of laying, must be available.

The number of bacteria at the end of the storage is estimated by:

\(NSS=C_{0} \times 10^{NCD}\),

where \(NSS\) is the number of Salmonella per egg (CFU), \(C_{0}\) is the initial concentration which follows a Poisson law with a mean value at 7 and a minimum value at 1 cell per egg and \(NCD\) is the number of ten-fold increase; after that, the different process steps (log10 CFU/h). \(NCD\) are estimated by:

\(NCD = \sum_{s = 1}^6 \mu_{max,s}.t_{TRMV}\),

where \(t_{TRMV}\) is the duration of the step after the complete deterioration of the yolk membrane is observed (TRMV).

The number of bacteria ingested is calculated as follows:

\(NSC = NSS \times 10^{-NRD}\)

where \(NSC\) is the number of Salmonella per egg after cooking and \(NRD\) is the number of log10 reductions, according to the conditions of cooking. \(NRD\) follows a normal distribution with a mean value at 2 and a standard deviation at 0.5 when eggs are lightly-cooked and a normal distribution with a mean value at 12 and a standard deviation at 1 when eggs are well-cooked (Thomas et al. 2006).

Dose-response model

The beta-Poisson dose-response model from (FAO/WHO 2002) was used:

\(P_{ill \space | \space cont \space serving} = 1-(1+ \frac {NSC}{\beta})^{- \alpha}\)

\(P_{ill | \text{cont serving} | \text{w}}\) and \(P_{ill | \text{cont serving} | \text{l}}\) are the probability of illness per S. Enteritidis-contaminated serving for well or lightly cooked eggs, respectively. The coefficients of the dose-response model are \(\alpha\) (0.1345) and \(\beta\) (53.33) which were taken from (Thomas et al. 2006).

Risk estimation

The potential risk for humans of becoming infected via egg consumption is estimated by:

\(R_{lightly-cooked} = (1-fq_{cooking}) \times prevalence \times P_{ill \space | \space cont \space serving | \space l} \times 10^{6}\)

\(R_{well-cooked} = fq_{cooking} \times prevalence \times P_{ill \space | \space cont \space serving | \space w} \times 10^{6}\)

where \(R_{lightly-cooked}\) is the number of illnesses per million servings of cooked egg, \(fq_{cooking}\) is the percentage of people who well cook eggs (set to 0.9), \(prevalence\) is the prevalence (set to 10% for illustration purposes) and \(P_{ill \space | \space cont \space serving}\), the probability of illness per S. Enteritidis-contaminated serving of egg.

Simulations

All model parameters are presented in Table 3. The default simulation values of these parameters take account of the conditions of production, transport and storage of eggs and they are summarised in the supplementary material (Suppl. material 2).

Executable model

Execute with default simulation parameters: http://data.d4science.org/analytics/get/FMJ_Lab?dim=https://data.d4science.org/shub/E_MklUWkdmZmp6eW56LzhDSXRxRk9QeXl2YXA1a0tNMUorbXhEVUMvcFJMVzNFRS83ZHBqSDZ6TW1pZ1hmbmprdA==

Execute with new simulation parameters: http://data.d4science.org/knime/get/FMJ_Lab?pm:file_ID=E_VEtmWmx2VTRVc2hHSXFSUElaWi9BK1VVUnpRUXVQcDd6RWNORG42Z3kxL3RidUpVNVNMSlNyOFlwWW9CbVBVWQ==

Results

Fig. 2 is generated thanks to the visualisation script and shows the number of salmonellosis cases per million eggs consumed dependent on the cooking method. According to the parameters entered for the time-temperature conditions and for 10% prevalence, the number of cases would reach at 186 salmonellosis per million serving of lightly-cooked eggs. The risk levels of well-cooked eggs are 1690 lower than lightly-cooked eggs. The risk associated raw egg cunsumption is two times more important than for lighly-cooked eggs.

Figure 2.  

Predicted number of salmonellosis per million servings of eggs, according to the cooking method.

The output shown in Fig. 3 is also generated by the visualisation script. It illustrates the evolution of the risk of salmonellosis per egg consumed as a function of the age of the egg at consumption. The probability of illness becomes high, especially after 9 days of storage.

Figure 3.  

Probability of illness according to storage duration.

Fig. 4 displays the cumulative distribution of the log10 of the probability of illness when ingesting a random contaminated serving of egg. It takes into account the proportion of lightly and well-cooked consumption practice.

Figure 4.  

Probability of salmonellosis for a random serving of egg.

Conclusion

This quantitative risk assessment model allows the estimation of the number of salmonellosis cases per million servings of table egg, as well as the probability of illness when ingesting a random serving of table egg. The model can be adapted to different situations in setting the parameters values especially time/temperature storage conditions (Suppl. materials 1, 2). Prevalence, set at 10% in the simulation settings, could also be adapted to more realistic values (EFSA BIOHAZ Panel (EFSA Panel on Biological Hazards) 2014). In the same way, the frequency of consumption of well-cooked eggs could be adapted to consumer practices, based on national consumption survey (Dubuisson et al. 2019).

Author contributions

Moez Sanaa developed the model code in a R Shiny application at first (https://qram.shinyapps.io/Eggs/). This model was recoded and prepared by Virginie Desvignes who drafted the manuscript. Laurent Guillier and Moez Sanaa reviewed the manuscript. Tasja Buschhardt filled in the metadata sheet of the model and the final FSK file was compiled by Virginie Desvignes.

References

Supplementary materials

Suppl. material 1: QMRA_Salmonella_egg_Virginie.fskx 
Authors:  Virginie Desvignes
Data type:  fskx model
Suppl. material 2: Parameter settings 
Authors:  Virginie Desvignes
Data type:  Model parameters