Source Attribution of Foodborne Diseases: Potentialities, Hurdles, and Future Expectations

Foodborne Illnesses from Leafy Greens in the United States: Attribution, Burden, and Cost

Leafy green vegetables are a major source of foodborne illnesses. Nevertheless, few studies have attempted to estimate attribution and burden of illness estimates for leafy greens. This study combines results from three outbreak-based attribution models with illness incidence and economic cost models to develop comprehensive pathogen-specific burden estimates for leafy greens and their subcategories in the United States. We find that up to 9.18% (90% CI: 5.81%-15.18%) of foodborne illnesses linked to identified pathogens are attributed to leafy greens. Including 'Unknown' illnesses not linked to specific pathogens, leafy greens account for as many as 2,307,558 (90% CI: 1,077,815-4,075,642) illnesses annually in the United States. The economic cost of these illnesses is estimated to be up to $5.278 billion (90% CI: $3.230-$8.221 billion) annually. Excluding the pathogens with small outbreak sizes, Norovirus, Shiga toxin-producingEscherichia coli (both non-O157 and O157:H7), Campylobacter spp., and nontyphoidal Salmonella, are associated with the highest number of illnesses and greatest costs from leafy greens. While lettuce (romaine, iceberg, "other lettuce") takes 60.8% of leafy green outbreaks, it accounts for up to 75.7% of leafy green foodborne illnesses and 70% of costs. Finally, we highlighted that 19.8% of Shiga toxin-producingEscherichia coli O157:H7 illnesses are associated with romaine among all food commodities, resulting in 12,496 estimated illnesses and $324.64 million annually in the United States.

Development and validation of a random forest algorithm for source attribution of animal and human Salmonella Typhimurium and monophasic variants of S. Typhimurium isolates in England and Wales utilising whole genome sequencing data

Source attribution has traditionally involved combining epidemiological data with different pathogen characterisation methods, including 7-gene multi locus sequence typing (MLST) or serotyping, however, these approaches have limited resolution. In contrast, whole genome sequencing data provide an overview of the whole genome that can be used by attribution algorithms. Here, we applied a random forest (RF) algorithm to predict the primary sources of human clinical Salmonella Typhimurium (S. Typhimurium) and monophasic variants (monophasic S. Typhimurium) isolates. To this end, we utilised single nucleotide polymorphism diversity in the core genome MLST alleles obtained from 1,061 laboratory-confirmed human and animal S. Typhimurium and monophasic S. Typhimurium isolates as inputs into a RF model. The algorithm was used for supervised learning to classify 399 animal S. Typhimurium and monophasic S. Typhimurium isolates into one of eight distinct primary source classes comprising common livestock and pet animal species: cattle, pigs, sheep, other mammals (pets: mostly dogs and horses), broilers, layers, turkeys, and game birds (pheasants, quail, and pigeons). When applied to the training set animal isolates, model accuracy was 0.929 and kappa 0.905, whereas for the test set animal isolates, for which the primary source class information was withheld from the model, the accuracy was 0.779 and kappa 0.700. Subsequently, the model was applied to assign 662 human clinical cases to the eight primary source classes. In the dataset, 60/399 (15.0%) of the animal and 141/662 (21.3%) of the human isolates were associated with a known outbreak of S. Typhimurium definitive type (DT) 104. All but two of the 141 DT104 outbreak linked human isolates were correctly attributed by the model to the primary source classes identified as the origin of the DT104 outbreak. A model that was run without the clonal DT104 animal isolates produced largely congruent outputs (training set accuracy 0.989 and kappa 0.985; test set accuracy 0.781 and kappa 0.663). Overall, our results show that RF offers considerable promise as a suitable methodology for epidemiological tracking and source attribution for foodborne pathogens.

A Review on Microbiological Source Attribution Methods of Human Salmonellosis: From Subtyping to Whole-Genome Sequencing

Salmonella is one of the main causes of human foodborne illness. It is endemic worldwide, with different animals and animal-based food products as reservoirs and vehicles of infection. Identifying animal reservoirs and potential transmission pathways of Salmonella is essential for prevention and control. There are many approaches for source attribution, each using different statistical models and data streams. Some aim to identify the animal reservoir, while others aim to determine the point at which exposure occurred. With the advance of whole-genome sequencing (WGS) technologies, new source attribution models will greatly benefit from the discriminating power gained with WGS. This review discusses some key source attribution methods and their mathematical and statistical tools. We also highlight recent studies utilizing WGS for source attribution and discuss open questions and challenges in developing new WGS methods. We aim to provide a better understanding of the current state of these methodologies with application to Salmonella and other foodborne pathogens that are common sources of illness in the poultry and human sectors.

Source attribution of campylobacteriosis in Australia, 2017-2019

Campylobacter jejuni and Campylobacter coli infections are the leading cause of foodborne gastroenteritis in high-income countries. Campylobacter colonizes a variety of warm-blooded hosts that are reservoirs for human campylobacteriosis. The proportions of Australian cases attributable to different animal reservoirs are unknown but can be estimated by comparing the frequency of different sequence types in cases and reservoirs. Campylobacter isolates were obtained from notified human cases and raw meat and offal from the major livestock in Australia between 2017 and 2019. Isolates were typed using multi-locus sequence genotyping. We used Bayesian source attribution models including the asymmetric island model, the modified Hald model, and their generalizations. Some models included an "unsampled" source to estimate the proportion of cases attributable to wild, feral, or domestic animal reservoirs not sampled in our study. Model fits were compared using the Watanabe-Akaike information criterion. We included 612 food and 710 human case isolates. The best fitting models attributed >80% of Campylobacter cases to chickens, with a greater proportion of C. coli (>84%) than C. jejuni (>77%). The best fitting model that included an unsampled source attributed 14% (95% credible interval [CrI]: 0.3%-32%) to the unsampled source and only 2% to ruminants (95% CrI: 0.3%-12%) and 2% to pigs (95% CrI: 0.2%-11%) The best fitting model that did not include an unsampled source attributed 12% to ruminants (95% CrI: 1.3%-33%) and 6% to pigs (95% CrI: 1.1%-19%). Chickens were the leading source of human Campylobacter infections in Australia in 2017-2019 and should remain the focus of interventions to reduce burden.

Harmonization of supervised machine learning practices for efficient source attribution of Listeria monocytogenes based on genomic data

BACKGROUND

Genomic data-based machine learning tools are promising for real-time surveillance activities performing source attribution of foodborne bacteria such as Listeria monocytogenes. Given the heterogeneity of machine learning practices, our aim was to identify those influencing the source prediction performance of the usual holdout method combined with the repeated k-fold cross-validation method.

METHODS

A large collection of 1 100 L. monocytogenes genomes with known sources was built according to several genomic metrics to ensure authenticity and completeness of genomic profiles. Based on these genomic profiles (i.e. 7-locus alleles, core alleles, accessory genes, core SNPs and pan kmers), we developed a versatile workflow assessing prediction performance of different combinations of training dataset splitting (i.e. 50, 60, 70, 80 and 90%), data preprocessing (i.e. with or without near-zero variance removal), and learning models (i.e. BLR, ERT, RF, SGB, SVM and XGB). The performance metrics included accuracy, Cohen's kappa, F1-score, area under the curves from receiver operating characteristic curve, precision recall curve or precision recall gain curve, and execution time.

RESULTS

The testing average accuracies from accessory genes and pan kmers were significantly higher than accuracies from core alleles or SNPs. While the accuracies from 70 and 80% of training dataset splitting were not significantly different, those from 80% were significantly higher than the other tested proportions. The near-zero variance removal did not allow to produce results for 7-locus alleles, did not impact significantly the accuracy for core alleles, accessory genes and pan kmers, and decreased significantly accuracy for core SNPs. The SVM and XGB models did not present significant differences in accuracy between each other and reached significantly higher accuracies than BLR, SGB, ERT and RF, in this order of magnitude. However, the SVM model required more computing power than the XGB model, especially for high amount of descriptors such like core SNPs and pan kmers.

CONCLUSIONS

In addition to recommendations about machine learning practices for L. monocytogenes source attribution based on genomic data, the present study also provides a freely available workflow to solve other balanced or unbalanced multiclass phenotypes from binary and categorical genomic profiles of other microorganisms without source code modifications.

Utilization of Algae Extracts as Natural Antibacterial and Antioxidants for Controlling Foodborne Bacteria in Meat Products

Padina pavonica, Hormophysa cuneiformis, and Corallina officinalis are three types of algae that are assumed to be used as antibacterial agents. Our study's goal was to look into algal extracts' potential to be used as food preservative agents and to evaluate their ability to inhibit pathogenic bacteria in several meat products (pastirma, beef burger, luncheon, minced meat, and kofta) from the local markets in Alexandria, Egypt. By testing their antibacterial activity, results demonstrated that Padina pavonica showed the highest antibacterial activity towards Bacillus cereus, Staphylococcus aureus, Escherichia coli, Streptococcus pyogenes, Salmonella spp., and Klebsiella pneumoniae. Padina pavonica extract also possesses most phenolic and flavonoid content overall. It has 24 mg gallic acid equivalent/g and 7.04 mg catechol equivalent/g, respectively. Moreover, the algae extracts were tested for their antioxidant activity, and the findings were measured using ascorbic acid as a benchmark. The IC50 of ascorbic acid was found to be 25.09 μg/mL, while Padina pavonica exhibited an IC50 value of 267.49 μg/mL, Corallina officinalis 305.01 μg/mL, and Hormophysa cuneiformis 325.23 μg/mL. In this study, Padina pavonica extract was utilized in three different concentrations (Treatment 1 g/100 g, Treatment 2 g/100 g, and Treatment 3 g/100 g) on beef burger as a model. The results showed that as the concentration of the extract increased, the bacterial inhibition increased over time. Bacillus cereus was found to be the most susceptible to the extract, while Streptococcus pyogenes was the least. In addition, Padina pavonica was confirmed to be a safe compound through cytotoxicity testing. After conducting a sensory evaluation test, it was confirmed that Padina pavonica in meat products proved to be a satisfactory product.

Transforming Shiga toxin-producing Escherichia coli surveillance through whole genome sequencing in food safety practices

Introduction

Shiga toxin-producing Escherichia coli (STEC) is a gastrointestinal pathogen causing foodborne outbreaks. Whole Genome Sequencing (WGS) in STEC surveillance holds promise in outbreak prevention and confinement, in broadening STEC epidemiology and in contributing to risk assessment and source attribution. However, despite international recommendations, WGS is often restricted to assist outbreak investigation and is not yet fully implemented in food safety surveillance across all European countries, in contrast to for example in the United States.

Methods

In this study, WGS was retrospectively applied to isolates collected within the context of Belgian food safety surveillance and combined with data from clinical isolates to evaluate its benefits. A cross-sector WGS-based collection of 754 strains from 1998 to 2020 was analyzed.

Results

We confirmed that WGS in food safety surveillance allows accurate detection of genomic relationships between human cases and strains isolated from food samples, including those dispersed over time and geographical locations. Identifying these links can reveal new insights into outbreaks and direct epidemiological investigations to facilitate outbreak management. Complete WGS-based isolate characterization enabled expanding epidemiological insights related to circulating serotypes, virulence genes and antimicrobial resistance across different reservoirs. Moreover, associations between virulence genes and severe disease were determined by incorporating human metadata into the data analysis. Gaps in the surveillance system were identified and suggestions for optimization related to sample centralization, harmonizing isolation methods, and expanding sampling strategies were formulated.

Discussion

This study contributes to developing a representative WGS-based collection of circulating STEC strains and by illustrating its benefits, it aims to incite policymakers to support WGS uptake in food safety surveillance.

16S microbiome analysis of microbial communities in distribution centers handling fresh produce

Little is known about the microbial communities found in distribution centers (DCs), especially in those storing and handling food. As many foodborne bacteria are known to establish residence in food facilities, it is reasonable to assume that DCs handling foods are also susceptible to pathogen colonization. To investigate the microbial communities within DCs, 16S amplicon sequencing was completed on 317 environmental surface sponge swabs collected in DCs (n = 18) across the United States. An additional 317 swabs were collected in parallel to determine if any viable Listeria species were also present at each sampling site. There were significant differences in median diversity measures (observed, Shannon, and Chao1) across individual DCs, and top genera across all reads were Carnobacterium_A, Psychrobacter, Pseudomonas_E, Leaf454, and Staphylococcus based on taxonomic classifications using the Genome Taxonomy Database. Of the 39 16S samples containing Listeria ASVs, four of these samples had corresponding Listeria positive microbiological samples. Data indicated a predominance of ASVs identified as cold-tolerant bacteria in environmental samples collected in DCs. Differential abundance analysis identified Carnobacterium_A, Psychrobacter, and Pseudomonas_E present at a significantly greater abundance in Listeria positive microbiological compared to those negative for Listeria. Additionally, microbiome composition varied significantly across groupings within variables (e.g., DC, season, general sampling location).

Different Roles of Wild Boars and Livestock in Salmonella Transmission to Humans in Italy

Wild boar (Sus scrofa) is the most widely distributed large wildlife mammal worldwide. To investigate the transmission of Salmonella enterica amongst wild boars (Sus scrofa), humans, and livestock, we compared via pulsed-field gel electrophoresis and whole genome sequences the isolates of S. enterica serovar Typhimurium (biphasic and monophasic variants) and Enteritidis collected from wild boars, food-producing animals, and human patients in Emilia-Romagna region (Northern Italy) between 2017 and 2020. Specifically, we analysed 2175 isolates originated from human (1832), swine (117), bovine (128), poultry (76), and wild boar (22). The genomic analyses showed that wild boars shared most of their lineages of biphasic Typhimurium with bovines and most of Enteritidis with poultry, whilst we did not find any lineage shared with swine. Moreover, almost 17% of human biphasic Typhimurium and Enteritidis belonged to genomic clusters including wild boar isolates, but the inclusion of bovine and poultry isolates in the same clusters and the peculiar spatial distribution of the isolates suggested that human cases (and wild boar infections) likely originated from bovines and poultry. Consequently, wild boars appear not to play a significant role in infecting humans with these serovars, but seem to get infected themselves from livestock, probably through the environment.

Review of major meat-borne zoonotic bacterial pathogens

The importance of meat-borne pathogens to global disease transmission and food safety is significant for public health. These pathogens, which can cause a variety of diseases, include bacteria, viruses, fungi, and parasites. The consumption of pathogen-contaminated meat or meat products causes a variety of diseases, including gastrointestinal ailments. Humans are susceptible to several diseases caused by zoonotic bacterial pathogens transmitted through meat consumption, most of which damage the digestive system. These illnesses are widespread worldwide, with the majority of the burden borne by developing countries. Various production, processing, transportation, and food preparation stages can expose meat and meat products to bacterial infections and/or toxins. Worldwide, bacterial meat-borne diseases are caused by strains of Escherichia coli, Salmonella, Listeria monocytogenes, Shigella, Campylobacter, Brucella, Mycobacterium bovis, and toxins produced by Staphylococcus aureus, Clostridium species, and Bacillus cereus. Additionally, consuming contaminated meat or meat products with drug-resistant bacteria is a severe public health hazard. Controlling zoonotic bacterial pathogens demands intervention at the interface between humans, animals, and their environments. This review aimed to highlight the significance of meat-borne bacterial zoonotic pathogens while adhering to the One Health approach for creating efficient control measures.

A Machine Learning Model for Food Source Attribution of Listeria monocytogenes

Despite its low morbidity, listeriosis has a high mortality rate due to the severity of its clinical manifestations. The source of human listeriosis is often unclear. In this study, we investigate the ability of machine learning to predict the food source from which clinical Listeria monocytogenes isolates originated. Four machine learning classification algorithms were trained on core genome multilocus sequence typing data of 1212 L. monocytogenes isolates from various food sources. The average accuracies of random forest, support vector machine radial kernel, stochastic gradient boosting, and logit boost were found to be 0.72, 0.61, 0.7, and 0.73, respectively. Logit boost showed the best performance and was used in model testing on 154 L. monocytogenes clinical isolates. The model attributed 17.5 % of human clinical cases to dairy, 32.5% to fruits, 14.3% to leafy greens, 9.7% to meat, 4.6% to poultry, and 18.8% to vegetables. The final model also provided us with genetic features that were predictive of specific sources. Thus, this combination of genomic data and machine learning-based models can greatly enhance our ability to track L. monocytogenes from different food sources.

Source attribution of salmonellosis by time and geography in New South Wales, Australia

BACKGROUND

Salmonella is a major cause of zoonotic illness around the world, arising from direct or indirect contact with a range of animal reservoirs. In the Australian state of New South Wales (NSW), salmonellosis is believed to be primarily foodborne, but the relative contribution of animal reservoirs is unknown.

METHODS

The analysis included 4543 serotyped isolates from animal reservoirs and 30,073 serotyped isolates from domestically acquired human cases in NSW between January 2008 and August 2019. We used a Bayesian source attribution methodology to estimate the proportion of foodborne Salmonella infections attributable to broiler chickens, layer chickens, ruminants, pigs, and an unknown or unsampled source. Additional analyses included covariates for four time periods and five levels of rurality.

RESULTS

A single serotype, S. Typhimurium, accounted for 65-75% of included cases during 2008-2014 but < 50% during 2017-2019. Attribution to layer chickens was highest during 2008-2010 (48.7%, 95% CrI 24.2-70.3%) but halved by 2017-2019 (23.1%, 95% CrI 5.7-38.9%) and was lower in the rural and remote populations than in the majority urban population. The proportion of cases attributed to the unsampled source was 11.3% (95% CrI 1.2%-22.1%) overall, but higher in rural and remote populations. The proportion of cases attributed to pork increased from approximately 20% in 2009-2016 to approximately 40% in 2017-2019, coinciding with a rise in cases due to Salmonella ser. 4,5,12:i:-.

CONCLUSION

Layer chickens were likely the primary reservoir of domestically acquired Salmonella infections in NSW circa 2010, but attribution to the source declined contemporaneously with increased vaccination of layer flocks and tighter food safety regulations for the handling of eggs.

Digging into Toxoplasma gondii infections via soil: A quantitative microbial risk assessment approach

Soil has been identified as an important source of exposure to a variety of chemical and biological contaminants. Toxoplasma gondii is one of those potential biological contaminants associated with serious health effects in pregnant women and immunocompromised patients. Gardening or consumption of homegrown vegetables may present an important route of T. gondii infection via accidental ingestion of soil. In the Netherlands, there is quantitative information on the risk of T. gondii infection via meat products, but not on the risk of infection through soil. The objective of this study was to develop a quantitative microbial risk assessment (QMRA) model for estimating the risk associated with T. gondii exposure via accidental soil ingestion in the Netherlands. In order to obtain the needed information, a magnetic capture method for detection of T. gondii oocysts in soil samples was developed, and T. gondii DNA was detected using qPCR targeting the 529 bp repeat element. The method was shown to provide 95% probability of detection (95% CI: 88-100%) when at least 34 oocysts are present in 25 g of soil. T. gondii DNA was detected in 5 of 148 soil samples with interpretable results (3%, 95% CI: 1.5-7.7%). Results for 18 samples were not interpretable due to PCR inhibition. The estimated amount of oocysts presented in qPCR positive samples was quantified by a linear model, and the amount varied from 8 to 478 in 25 g of soil. The estimated incidence rate of T. gondii infection from the QMRA model via soil varied from 0.3 to 1.8 per 1000 individuals per day. Several data gaps (e.g., soil contamination/ingestion and oocysts viability) have been identified in this study, the structure of the model can be applied to obtain more accurate estimates of the risk of T. gondii infection via soil when data become available.

The Data Behind Risk Analysis of Campylobacter Jejuni and Campylobacter Coli Infections

Campylobacter jejuni and Campylobacter coli are major causes of food-borne enteritis in humans. Poultry meat is known to be responsible for a large proportion of cases of human campylobacteriosis. However, other food-borne, environmental and animal sources are frequently associated with the disease in humans as well. Human campylobacteriosis causes gastroenteritis that in most cases is self-limiting. Nevertheless, the burden of the disease is relatively large compared with other food-borne diseases, which is mostly due to rare but long-lasting symptoms related to immunological sequelae. In order to pave the way to improved surveillance and control of human campylobacteriosis, we review here the data that is typically used for risk analysis to quantify the risk and disease burden, identify specific surveillance strategies and assist in choosing the most effective control strategies. Such data are mostly collected from the literature, and their nature is discussed here, for each of the three processes that are essential for a complete risk analysis procedure: risk assessment, risk management and risk communication. Of these, the first, risk assessment, is most dependent on data, and this process is subdivided into the steps of hazard identification, hazard characterization, exposure assessment and risk characterization. For each of these steps of risk assessment, information from published material that is typically collected will be summarized here. In addition, surveillance data are highly valuable for risk assessments. Different surveillance systems are employed in different countries, which can make international comparison of data challenging. Risk analysis typically results in targeted control strategies, and these again differ between countries. The applied control strategies are as yet not sufficient to eradicate human campylobacteriosis. The surveillance tools of Campylobacter in humans and exposure sources in place in different countries are briefly reviewed to better understand the Campylobacter dynamics and guide control strategies. Finally, the available control measures on different risk factors and exposure sources are presented.

Mining whole genome sequence data to efficiently attribute individuals to source populations

Whole genome sequence (WGS) data could transform our ability to attribute individuals to source populations. However, methods that efficiently mine these data are yet to be developed. We present a minimal multilocus distance (MMD) method which rapidly deals with these large data sets as well as methods for optimally selecting loci. This was applied on WGS data to determine the source of human campylobacteriosis, the geographical origin of diverse biological species including humans and proteomic data to classify breast cancer tumours. The MMD method provides a highly accurate attribution which is computationally efficient for extended genotypes. These methods are generic, easy to implement for WGS and proteomic data and have wide application.

Source Attribution Study of Sporadic Salmonella Derby Cases in France

Salmonella enterica subsp. enterica serovar Derby is one of the most frequent causes of gastroenteritis in humans. In Europe, this pathogen is one of the top five most commonly reported serovars in human cases. In France, S. Derby has been among the ten most frequently isolated serovars in humans since the year 2000. The main animal hosts of this serovar are pigs and poultry, and white meat is the main source of human contamination. We have previously shown that this serovar is polyphyletic and that three distinct genetic lineages of S. Derby cohabit in France. Two of them are associated with pork and one with poultry. In this study, we conducted a source attribution study based on single nucleotide polymorphism analysis of a large collection of 440 S. Derby human and non-human isolates collected in 2014-2015, to determine the contribution of each lineage to human contamination. In France, the two lineages associated with pork strains, and corresponding to the multilocus sequence typing (MLST) profiles ST39-ST40 and ST682 were responsible for 94% of human contaminations. Interestingly, the ST40 profile is responsible for the majority of human cases (71%). An analysis of epidemiologic data and the structure of the pork sector in France allowed us to explain the spread and the sporadic pattern of human cases that occurred in the studied period.

Attribution of Listeria monocytogenes human infections to food and animal sources in Northern Italy

Listeriosis is a foodborne illness characterized by a relatively low morbidity, but a large disease burden due to the severity of clinical manifestations and the high case fatality rate. Increased listeriosis notifications have been observed in Europe since the 2000s. However, the reasons for this increase are largely unknown, with the sources of sporadic human listerioris often remaining elusive. Here we inferred the relative contributions of several putative sources of Listeria monocytogenes strains from listerioris patients in Northern Italy (Piedmont and Lombardy regions), using two established source attribution models (i.e. 'Dutch' and 'STRUCTURE') in comparative fashion. We compared the Multi-Locus Sequence Typing and Multi-Virulence-Locus Sequence Typing profiles of strains collected from beef, dairy, fish, game, mixed foods, mixed meat, pork, and poultry. Overall, 634 L. monocytogenes isolates were collected from 2005 to 2016. In total, 40 clonal complexes and 51 virulence types were identified, with 36% of the isolates belonging to possible epidemic clones (i.e. genetically related strains from unrelated outbreaks). Source attribution analysis showed that 50% of human listerioris cases (95% Confidence Interval 44-55%) could be attributed to dairy products, followed by poultry and pork (15% each), and mixed foods (15%). Since the contamination of dairy, poultry and pork products are closely linked to primary production, expanding actions currently limited to ready-to-eat products to the reservoir level may help reducing the risk of cross-contamination at the consumer level.

One Health: the global challenge of Clostridium difficile infection

The One Health concept recognises that the health of humans is interconnected to the health of animals and the environment. It encourages multidisciplinary communication and collaboration with the aim of enhancing surveillance and research and developing integrative policy frameworks. Clostridium difficile (also known as Clostridioides difficile) infection (CDI) has long been viewed as a hospital-associated (HA) enteric disease mainly linked to the use of broad-spectrum antimicrobials that cause dysbiosis in the gut and loss of ‘colonisation resistance'. However, since the early 2000s, the rate of community-associated CDI (CA-CDI) has increased to ~15% in Europe, ~30% in Australia and ~40% in the USA in populations often without obvious risk factors. Since the 1990s, it has become apparent that food animals are now a major reservoir and amplification host for C.difficile, including lineages of clinical importance. Cephalosporin antimicrobials, to which C. difficile is intrinsically resistant, were licensed for animal use in North America in 1990. By the second decade of the 21st century, there were reports of C. difficile contamination of food and the environment in general. Using whole-genome sequencing (WGS) and high-resolution typing, C. difficile isolates from humans, animals, food and the environment were proven to be genetically closely related and, in some cases, indistinguishable. This suggests possible zoonoses and/or anthroponoses, with contaminated food and the environment acting as the conduit for transmission between animals and humans. This paper summarises the key evidence that demonstrates the One Health importance of C. difficile.

Whole genome sequencing and metagenomics for outbreak investigation, source attribution and risk assessment of food-borne microorganisms

This Opinion considers the application of whole genome sequencing (WGS) and metagenomics for outbreak investigation, source attribution and risk assessment of food‐borne pathogens. WGS offers the highest level of bacterial strain discrimination for food‐borne outbreak investigation and source‐attribution as well as potential for more precise hazard identification, thereby facilitating more targeted risk assessment and risk management. WGS improves linking of sporadic cases associated with different food products and geographical regions to a point source outbreak and can facilitate epidemiological investigations, allowing also the use of previously sequenced genomes. Source attribution may be favoured by improved identification of transmission pathways, through the integration of spatial‐temporal factors and the detection of multidirectional transmission and pathogen–host interactions. Metagenomics has potential, especially in relation to the detection and characterisation of non‐culturable, difficult‐to‐culture or slow‐growing microorganisms, for tracking of hazard‐related genetic determinants and the dynamic evaluation of the composition and functionality of complex microbial communities. A SWOT analysis is provided on the use of WGS and metagenomics for Salmonella and Shigatoxin‐producing Escherichia coli (STEC) serotyping and the identification of antimicrobial resistance determinants in bacteria. Close agreement between phenotypic and WGS‐based genotyping data has been observed. WGS provides additional information on the nature and localisation of antimicrobial resistance determinants and on their dissemination potential by horizontal gene transfer, as well as on genes relating to virulence and biological fitness. Interoperable data will play a major role in the future use of WGS and metagenomic data. Capacity building based on harmonised, quality controlled operational systems within European laboratories and worldwide is essential for the investigation of cross‐border outbreaks and for the development of international standardised risk assessments of food‐borne microorganisms.

A Modular Bayesian Salmonella Source Attribution Model for Sparse Data

Several statistical models for salmonella source attribution have been presented in the literature. However, these models have often been found to be sensitive to the model parameterization, as well as the specifics of the data set used. The Bayesian salmonella source attribution model presented here was developed to be generally applicable with small and sparse annual data sets obtained over several years. The full Bayesian model was modularized into three parts (an exposure model, a subtype distribution model, and an epidemiological model) in order to separately estimate unknown parameters in each module. The proposed model takes advantage of the consumption and overall salmonella prevalence of the studied sources, as well as bacteria typing results from adjacent years. The latter were used for a smoothed estimation of the annual relative proportions of different salmonella subtypes in each of the sources. The source-specific effects and the salmonella subtype-specific effects were included in the epidemiological model to describe the differences between sources and between subtypes in their ability to infect humans. The estimation of these parameters was based on data from multiple years. Finally, the model combines the total evidence from different modules to proportion human salmonellosis cases according to their sources. The model was applied to allocate reported human salmonellosis cases from the years 2008 to 2015 to eight food sources.

Extending statistical models for source attribution of zoonotic diseases: a study of campylobacteriosis

Preventing and controlling zoonoses through the design and implementation of public health policies requires a thorough understanding of transmission pathways. Modelling jointly the epidemiological data and genetic information of microbial isolates derived from cases provides a methodology for tracing back the source of infection. In this paper, the attribution probability for human cases of campylobacteriosis for each source, conditional on the extent to which each case resides in a rural compared to urban environment, is estimated. A model that incorporates genetic data and evolutionary processes is applied alongside a newly developed genetic-free model. We show that inference from each model is comparable except for rare microbial genotypes. Further, the effect of 'rurality' may be modelled linearly on the logit scale, with increasing rurality leading to the increasing likelihood of ruminant-sourced campylobacteriosis.