The qualified presumption of safety (QPS) was developed to provide a safety pre‐assessment within EFSA for microorganisms. Strains belonging to QPS taxonomic units (TUs) still require an assessment based on a specific data package, but QPS status facilitates fast track evaluation. QPS TUs are unambiguously defined biological agents assessed for the body of knowledge, their safety and their end use. Safety concerns are, where possible, to be confirmed at strain or product level, and reflected as ‘qualifications’. Qualifications need to be evaluated at strain level by the respective EFSA units. The lowest QPS TU is the species level for bacteria, yeasts and protists/algae, and the family for viruses. The QPS concept is also applicable to genetically modified microorganisms used for production purposes if the recipient strain qualifies for the QPS status, and if the genetic modification does not indicate a concern. Based on the actual body of knowledge and/or an ambiguous taxonomic position, the following TUs were excluded from the QPS assessment: filamentous fungi, oomycetes, streptomycetes, Enterococcus faecium, Escherichia coli and bacteriophages. The list of QPS‐recommended biological agents was reviewed and updated in the current opinion and therefore now becomes the valid list. For this update, reports on the safety of previously assessed microorganisms, including bacteria, yeasts and viruses (the latter only when used for plant protection purposes) were reviewed, following an Extensive Literature Search strategy. All TUs previously recommended for 2016 QPS list had their status reconfirmed as well as their qualifications. The TUs related to the new notifications received since the 2016 QPS opinion was periodically evaluated for QPS status in the Statements of the BIOHAZ Panel, and the QPS list was also periodically updated. In total, 14 new TUs received a QPS status between 2017 and 2019: three yeasts, eight bacteria and three algae/protists.

A quantitative microbiological risk assessment model describes the transmission of Campylobacter through the broiler meat production chain and at home, from entering the processing plant until consumption of a chicken breast fillet meal. The exposure model is linked to a dose-response model to allow estimation of the incidence of human campylobacteriosis. The ultimate objective of the model is to serve as a tool to assess the effects of interventions to reduce campylobacteriosis in the Netherlands. The model describes some basic mechanistics of processing, including the nonlinear effects of cross-contamination between carcasses and their leaking feces. Model input is based on the output of an accompanying farm model and Dutch count data of Campylobacters on the birds' exterior and in the feces. When processing data are lacking, expert judgment is used for model parameter estimation. The model shows that to accurately assess of the effects of interventions, numbers of Campylobacter have to be explicitly incorporated in the model in addition to the prevalence of contamination. Also, as count data usually vary by several orders of magnitude, variability in numbers within and especially between flocks has to be accounted for. Flocks with high concentrations of Campylobacter in the feces that leak from the carcasses during industrial processing seem to have a dominant impact on the human incidence. The uncertainty in the final risk estimate is large, due to a large uncertainty at several stages of the chain. Among others, more quantitative count data at several stages of the production chain are needed to decrease this uncertainty. However, this uncertainty is smaller when relative risks of interventions are calculated with the model. Hence, the model can be effectively used by risk management in deciding on strategies to reduce human campylobacteriosis.

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We estimated the true incidence of campylobacteriosis and salmonellosis in the European Union (EU) in 2009. The estimate was based on disease risks of returning Swedish travellers, averaged over the years 2005-2009, and anchored to a Dutch population-based study on incidence and aetiology of gastroenteritis. For the 27 EU member states the incidence of campylobacteriosis was about 9·2 (95% CI 2·8-23) million cases, while the incidence of salmonellosis was 6·2 (95% CI 1·0-19) million cases. Only 1/47 (95% CI 14-117) cases of campylobacteriosis and one 1/58 (95% CI 9-172) cases of salmonellosis were reported in the EU. The incidence rate of campylobacteriosis in EU member states varied between 30 and 13 500/100 000 population and was significantly correlated with the prevalence of Campylobacter spp. in broiler chickens. The incidence rate of salmonellosis in EU member states varied between 16 and 11 800/100 000 population and was significantly correlated with the prevalence of Salmonella Enteritidis in laying hens.

As a major foodborne pathogen, Campylobacter jejuni receives much attention in quantitative risk assessment. To date, all dose-response assessments have been based on a single human feeding study which unfortunately provides incomplete and possibly biased information on the dose-response relation. An incident at a dairy farm, where several children from a school class became ill as a result of drinking raw milk contaminated with C. jejuni, appeared to show a very clear dose-response relation between the amount of milk consumed and the attack rate. This relation was very nearly exponentially shaped and, therefore, seemed to conflict with the rather slowly rising dose-response relation established in the feeding study. Here we show that both datasets can be reconciled when illness and infection are considered separately. This not only provides new information on the illness dose-response relation for Campylobacter, but also amends the infection dose-response relation because of their conditional dependence.

Quantitative risk assessment (QRA) modelling is increasingly used in food microbiology as a tool to evaluate health risks and to support the management of safe food production. Depending on the hazard and the process analysed, a QRA model may involve complex calculations: probability distributions are derived for the model parameters and the model is evaluated using specific risk analysis software. Second-order modelling, involving the separation of uncertainty and variability of model parameters, is considered of increasing importance in several fields of risk analysis. However, it is commonly neglected in microbial risk assessment studies. In this paper the relevance of second-order modelling in microbial risk assessment is illustrated by a simple example of a risk assessment of growth of B. cereus in pasteurised milk. It shows that the prediction of the outbreak size may depend on the way that uncertainty and variability are separated, and that a major outbreak may be overlooked if the distinction between uncertainty and variability is neglected.

The provisional molecular approach, proposed by EFSA in 2013, for the pathogenicity assessment of Shiga toxin-producing Escherichia coli (STEC) has been reviewed. Analysis of the confirmed reported human STEC infections in the EU/EEA (2012-2017) demonstrated that isolates positive for any of the reported Shiga toxin (Stx) subtypes (and encoding stx gene subtypes) may be associated with severe illness (defined as bloody diarrhoea (BD), haemolytic uraemic syndrome (HUS) and/or hospitalisation). Although strains positive for stx2a gene showed the highest rates, strains with all other stx subtypes, or combinations thereof, were also associated with at least one human case with a severe clinical outcome. Serogroup cannot be used as a predictor of clinical outcome and the presence of the intimin gene (eae) is not essential for severe illness. These findings are supported by the published literature, a review of which suggested there was no single or combination of virulence markers associated exclusively with severe illness. Based on available evidence, it was concluded that all STEC strains are pathogenic in humans, capable of causing at least diarrhoea and that all STEC subtypes may be associated with severe illness. Source attribution analysis, based on 'strong evidence' outbreak data in the EU/EEA (2012-2017), suggests that 'bovine meat and products thereof', 'milk and dairy products', 'tap water including well water' and 'vegetables, fruit and products thereof' are the main sources of STEC infections in the EU/EEA, but a ranking between these categories cannot be made as the data are insufficient. Other food commodities are also potentially associated with STEC infections but rank lower. Data gaps are identified, and are primarily caused by the lack of harmonisation in sampling strategies, sampling methods, detection and characterisation methods, data collation and reporting within the EU. (VTEC). All these institutions provided data for this scientific output in the context of two consultations developed via the EU survey tool (Appendices I and J).

Aims:  To determine the effect of hygiene measures on cross‐contamination of Campylobacter jejuni at home and to select a safe tracer organism for C. jejuni. Methods and Results:  Comparative tests were conducted with nonpathogenic Escherichia coli and Lactobacillus casei and L. casei was chosen as the safe tracer organism. Salads containing chicken breast fillet contaminated with a known number of C. jejuni and L. casei were prepared according to different cross‐contamination scenarios and contamination levels of salads were determined. Cross‐contamination could be strongly reduced when cleaning cutting board and cutlery with hot water (68°C), but generally was not prevented using consumer‐style cleaning methods for hands and cutting board. Conclusions:  Dish‐washing does not sufficiently prevent cross‐contamination, thus different cutting boards for raw meat and other ingredients should be used and meat–hand contact should be avoided or hands should be thoroughly cleaned with soap. Lactobacillus casei can be used as a safe tracer organism for C. jejuni in consumer observational studies. Significance and Impact of the Study:  Cross‐contamination plays an important role in the transmission of food‐borne illness, especially for C. jejuni. This study delivers suitable data to quantitatively assess the risk of campylobacteriosis caused by cross‐contamination and it shows the effect of different preventive hygiene measures.