Thermotolerant Campylobacter is the most commonly reported zoonotic disease in the EU (European Food Safety Authority and European Centre for Disease Prevention Control 2019) and a leading cause of zoonotic enteric infections worldwide (World Health Organization 2013). To support the control of Campylobacter, quantitative microbiological risk assessments (QMRAs) have been performed in various countries as well as in an international context (World Health Organization and Food Agriculture Organization of the United Nations 2009, Nauta et al. 2009, EFSA Panel on Biological Hazards 2011, Chapman et al. 2016, EFSA Panel on Biological Hazards et al. 2020). Several foods are indicated as potential sources of Campylobacter (Silva et al. 2011), but QMRAs have predominantly focused on what is generally considered the main route of transmission: the broiler meat production chain and human exposure associated with the consumption of broiler meat. Published models vary extensively in the way the exposure assessment is performed (Nauta et al. 2009, Chapman et al. 2016), but, until recently, the vast majority of QMRAs have applied a dose-response model (DRM) based on the challenge study performed by Black et al. (1988) (Teunis and Havelaar 2000, Teunis et al. 2005). This challenge study involved only a small group of healthy male volunteers who were exposed to a specific Campylobacter strain.  However, it has for a long time been considered the best basis for Campylobacter dose-response modelling in QMRA. It has been referred to as the "classic" DRM (EFSA Panel on Biological Hazards 2011).

After an earlier reconsideration of the "classic" DRM (Teunis et al. 2005), Teunis et al. (2018) published an analysis including newly published dose-response data, including various challenge studies on humans and primates as well as four studies based on outbreaks associated with the consumption of raw milk. The DRM applied to these data suggested a different dose-response curve and higher infectivity of Campylobacter. This led to the statement of Teunis et al. (2018) that the "classic" DRM has been “an unfortunate choice” as the default DRM used for Campylobacter. Teunis et al. (2018) have described their analyses in detail and provide the software code for their model. However, application of their model in a QMRA of Campylobacter remains challenging, as a ready-to-use model is not provided and developing one requires considerable expertise in modelling and programming.

In this paper, we provide the DRM developed by Teunis et al. (2018) in the Food Safety Knowledge Exchange (FSKX) Format, which is a standardised exchange format for mathematical models and simulation results. This open format contains model script, visualisation script, and simulation settings (de Alba Aparicio et al. 2018) as well as a harmonised way to annotate the model by providing a metadata schema that uses, whenever possible, controlled vocabularies (Haberbeck et al. 2018). Thus, it facilitates re-usability, the application of the model in a QMRA, and an efficient knowledge exchange. The research project "Risk Assessment Modelling and Knowledge Integration Platforms (RAKIP)" initiated the development of the FSKX format and included the metadata schema to annotate knowledge. The RAKIP community, with partners from the French Agency for Food, Environmental and Occupational Health & Safety (ANSES), the Technical University of Denmark, National Food Institute (DTU Food), and the German Federal Institute for Risk Assessment (BfR), continues to improve and extend the community-driven metadata schema (Haberbeck et al. 2018, German Federal Institute for Risk Assessment 2019). We illustrate how the user can explore the performance of the Campylobacter DRM published by Teunis et al. (2018) and apply them to user-defined inputs.

The model metadata are a schema to annotate the model in a harmonised way. It is part of the FSKX-file (see Suppl. material 1 for the FSKX-file). For details about the metadata schema and the used definitions, see Haberbeck et al. (2018) and https://foodrisklabs.bfr.bund.de/rakip-harmonization-resources/ (the used metadata schema version is 1.04).

General metadata

Product/matrix

Hazard

Population

Scope

Data background

Model description

Parameter estimation

All model parameters and their descriptions are presented in Table 1. The default simulation represents values derived from challenge studies; the values are summarised in Table 2. Another simulation setting, which considers values derived from outbreaks, is also available (see Table 2). This simulation differs from the default simulation only by the parameter "challenge". This parameter is "true" and "false" for the default and the outbreak simulation, respectively. In these two scenarios considered, a dose of one CFU is set, hence outcomes represent the single hit probabilities of illness and infection. These scenarios are interesting since they are lower bounds of infection or illness risk, and secondly since they allow us to compare the results to single hit infection probabilities of other pathogens. Two more scenarios are presented Table 2, again for both the challenge and outbreak situation, but, instead of a constant dose, we set the dose to \(\log_{10}(D) \sim N(-1, 1.5)\). This distribution models the concentration distribution in poultry meat, based on data presented in Swart and Havelaar (2012). In the following section, the model can be executed with default or customised parameters. It is also possible to work with the model on the local computer, for example, using the KNIME extension FSK-Lab (see https://foodrisklabs.bfr.bund.de/fsk-lab/ and de Alba Aparicio et al. 2018).

Executable model

Results are visualised as boxplots that show the probability of illness and infection for the human population that consumes Campylobacter jejuni-contaminated food. The dose-response model is applied using the challenge studies dataset and the outbreak studies dataset separately (Teunis et al. 2018). All figures (Figs 1, 2, 3, 4) provide detailed information about the uncertainty of the probability of infection and illness; the white box represents the 25 % and 75 % quantile with the median indicated as the black bar inside of the box. The whiskers correspond to the maximum and minimum of the distribution, when not more than -/+ 1.5 times the inter-quartile range. The dots represent extreme values. The high values found for the probability of infection and the probability of illness for outbreak strains, are in agreement with the results obtained by Teunis et al. (2018).

Figure 1.  

The probability of illness and infection for the human population after consumption of a mean dose of 1 CFU of Campylobacter jejuni contaminated food. The probabilities are calculated based on the challenge studies dataset (the so-called default simulation).

Figure 2.  

The probability of illness and infection for the human population after consumption of a mean dose of 1 CFU of Campylobacter jejuni-contaminated food. The probabilities are calculated based on the outbreak dataset (the so-called outbreak simulation).

Figure 3.  

The probability of illness and infection for the human population after consumption of Campylobacter jejuni-contaminated food. The probabilities are calculated based on the challenge studies dataset with 1000 various mean doses (the so-called ChallengeVarMeanDoses simulation).

Figure 4.  

The probability of illness and infection for the human population after consumption of Campylobacter jejuni-contaminated food. The probabilities are calculated based on the outbreak dataset with 1000 various mean doses (the so-called OutbreakVarMeanDoses simulation).

The effect of a mean dose of 1 CFU of Campylobacter on human health

The effect of various mean doses of Campylobacter on human health

Dose-response models, as part of the hazard characterisation, are an indispensable ingredient of any QMRA model (Codex Alimentarius Commission 1999). Each microbial pathogen requires its own dose-response model, since infectivities of different species may differ substantially. In addition, pathogenicity may differ between bacterial strains of a taxonomical unit and even susceptibilities between individuals vary, due to individual host characteristics or acquired immunity (Havelaar and Swart 2014). However, scarcity of data to characterise the variability between bacterial strains and hosts precludes dose-response models specified at this level of detail.

In the current study, we focus on a recently published dose-response model for Campylobacter jejuni (Teunis et al. 2018). This model is based on a variety of dose-response studies and offers an alternative to the "classic" dose-response model of Teunis and Havelaar (2000). The "classic" model has been most frequently used in Campylobacter risk assessments so far (Nauta et al. 2009) and is based on the study of Black et al. (1988). As shown by Teunis et al. (2018), the recent model suggests that Campylobacter is more virulent than previously assumed.

We present the recent model of Teunis et al. (2018) annotated with all metadata and implemented in the ready-to-use FSKX-format. This allows easy model integration and simulation result interpretation in future QMRA studies. On the one hand, the model in the FSKX-file is available as an R programming language code that can be integrated into a fully fledged QMRA model. On the other hand, for a swift assessment of infection and illness probabilities at given mean doses, the current "executable paper" provides a convenient resource. Moreover, by using software like FSK-Lab, the user is able to join the model with an exposure assessment and perform a complete QMRA. In conclusion, we provide an annotated, ready-to-use implementation of the Campylobacter jejuni dose-response model.

We wish to acknowledge the original work of Peter F.M. Teunis, Axel Bonačić Marinović, David R. Tribble, Chad K. Porter, and Stylianos Georgiadis.  

EMS is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 731001 and the JIP MATRIX within the One Health EJP. One Health EJP has received funding from the European Union Horizon 2020 research and innovation programme under grant agreement No 773830. 

Esther M. Sundermann: Conceptualisation, Software (creation of the fskx-model), Project administration, Visualisation, Data Curation, Writing - Original Draft, Writing - Review & Editing, Maarten Nauta: Writing - Original Draft, Writing - Review & Editing, Arno Swart: Methodology, Software (development of the model), Writing - Original Draft, Writing - Review & Editing