Extended-spectrum beta-lactamase (ESBL)-producing Escherichia (E.) coli have been widely described as the cause of treatment failures in humans around the world. The origin of human infections with these microorganisms is discussed controversially and in most cases hard to identify. Since they pose a relevant risk to human health, it becomes crucial to understand their sources and the transmission pathways. In this study, we analyzed data from different studies in Germany and grouped ESBL-producing E. coli from different sources and human cases into subtypes based on their phenotypic and genotypic characteristics (ESBL-genotype, E. coli phylogenetic group and phenotypic antimicrobial resistance pattern). Then, a source attribution model was developed in order to attribute the human cases to the considered sources. The sources were from different animal species (cattle, pig, chicken, dog and horse) and also from patients with nosocomial infections. The human isolates were gathered from community cases which showed to be colonized with ESBL-producing E. coli. We used the attribution model first with only the animal sources (Approach A) and then additionally with the nosocomial infections (Approach B). We observed that all sources contributed to the human cases, nevertheless, isolates from nosocomial infections were more related to those from human cases than any of the other sources. We identified subtypes that were only detected in the considered animal species and others that were observed only in the human population. Some subtypes from the human cases could not be allocated to any of the sources from this study and were attributed to an unknown source. Our study emphasizes the importance of human-to-human transmission of ESBL-producing E. coli and the different role that pets, livestock and healthcare facilities may play in the transmission of these resistant bacteria. The developed source attribution model can be further used to monitor future trends. A One Health approach is necessary to develop source attribution models further to integrate also wildlife, environmental as well as food sources in addition to human and animal data.

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Direct contact between humans and live broilers, as well as the consumption of chicken meat, have been suggested as pathways for transmission of extended-spectrum-β-lactamase (ESBL) and AmpC-β-lactamase (AmpC)-producing Escherichia coli. One approach to design intervention strategies to control the transmission of such bacteria between animals and humans is to study the transmission pathways of such bacteria between the animals themselves. The rationale is that controlling the process of the underlying source, here transmission between animals, can provide hints on how to control a higher-level process, here the transmission between animals and humans. The focus of this article is the transmission of the above-mentioned bacteria between broilers and broiler flocks in meat production with regards to the establishment of possible intervention strategies to reduce the transfer of these bacteria between animals. The objective of this work is to design a mathematical transmission model describing the effects of vertical and horizontal bacterial transmission in the broiler production chain, from the parent generation to the slaughterhouse level. To achieve this objective, an existing transmission model for Campylobacter was adapted for the case of E. coli. The model keeps track of prevalence among flocks (flock prevalence) and of prevalence among animals within one flock (animal prevalence). Flock and animal prevalences show different dynamics in the model. While flock prevalence increases mainly through horizontal transmission in hatcheries, animal prevalence increases mainly at the broiler-fattening farm. Transports have rather small effects just as the vertical transmission from parents to chicks.

Source attribution methods attribute human cases of a zoonotic disease to a certain source putatively responsible for this disease. Identifying and quantifying the contribution of different sources to human infections is important for taking appropriate actions for reducing the exposure of the consumer to zoonotic pathogens. One widely used method is the microbial subtyping approach, whose principle is to compare the frequency of pathogen subtypes from different sources (e.g. animals or food) with the frequency of these subtypes in human cases. This paper studies the relationship between a Bayesian microbial subtyping approach described by Hald and coworkers subsequently modified by David and coworkers, here called the Hald model, and a frequentist approach known as the “Dutch model.” The comparison between the Bayesian and frequentist model is done for two data sets on salmonellosis in Germany from different time periods (year 2004–2007 and 2010–2011). The results of both approaches are in good agreement with each other for the used data. It is shown here mathematically that a certain parameterization can be used to transform the probabilistic Hald model into a deterministic form, which is equivalent to the Dutch model. That certain parameterization secures independence of the model outcomes from the choice of so‐called unique subtypes (which are unique in the sense that they are found exclusively in one of the sources). It is shown that deviating from that certain parameterization leads variations in the model outcome dependent on which unique subtypes are chosen in the process of modelling.

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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.

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