One Health Consensus Report Annotation Checklist (OH-CRAC): A cross-sector checklist to support harmonized annotation of surveillance data in reports.

To facilitate cross-sector integration of surveillance data it is necessary to improve and harmonize the meta-information provided in surveillance data reports. Cross-sector integration of surveillance results in sector-specific reports is frequently difficult as reports with a focus on a single sector often lack aspects of the relevant meta-information necessary to clarify the surveillance context. Such reporting deficiencies reduce the value of surveillance reports to the One Health community. The One Health Consensus Report Annotation Checklist (OH-CRAC), described in this paper along with potential application scenarios, was developed to improve the current practice of annotating data presented in surveillance data reports. It aims to provide guidance to researchers and reporting officers on what meta-information should be collected and provided to improve the completeness and transparency of surveillance data reports. The OH-CRAC can be adopted by all One Health-related sectors and due to its cross-sector design, it supports the mutual mapping of surveillance meta-information from sector-specific surveillance reports on federal, national and international levels. To facilitate the checklist completion, OH-CRAC is also available as an online resource that allows the collection of surveillance meta-information in an easy and user-friendly manner. Completed OH-CRAC checklists can be attached as annexes to the corresponding surveillance data reports or even to individual data files regardless of the data source. In this way, reports and data become better interpretable, usable and comparable to information from other sectors, improving their value for all surveillance actors and providing a better foundation for advice to risk managers.

A one health glossary to support communication and information exchange between the human health, animal health and food safety sectors.

Collaboration across sectors, disciplines and countries is a key concept to achieve the overarching One Health (OH) objective for better human, animal and environmental health. Differences in terminology and interpretation of terms are still a significant hurdle for cross-sectoral information exchange and collaboration within the area of OH including One Health Surveillance (OHS). The development of the here described glossary is a collaborative effort of three projects funded within the One Health European Joint Programme (OHEJP). We describe the infrastructure of the OHEJP Glossary, as well as the methodology to create such a cross-sectoral web resource in a collaborative manner. The new OHEJP Glossary allows OH actors to identify terms with different or shared interpretation across sectors. Being aware of such differences in terminology will help overcome communication hurdles in the future and consequently support collaboration and a more inclusive development of OHS. The OHEJP Glossary was implemented as a web-based, user-friendly and searchable infrastructure that complies with the Findable, Accessible, Interoperable, Reusable (FAIR) data principles. Maintenance, enrichment and quality control of the OHEJP Glossary is supported through a flexible and updatable curation infrastructure. This increases the uptake potential and exploitation of the OHEJP Glossary by other OH initiatives or tools and services.

One Health Surveillance Codex: promoting the adoption of One Health solutions within and across European countries.

Cross-sector communication, collaboration and knowledge exchange are still significant challenges for practical adoption of the One Health paradigm. To address these needs the "One Health Surveillance Codex" (OHS Codex) was established to provide a framework for the One Health community to continuously share practical solutions (e.g. tools, technical resources, guidance documents and experiences) applicable for national and international stakeholders from different One Health Surveillance sectors. Currently, the OHS Codex provides a number of resources that support the adoption of the OH paradigm in areas linked to the harmonization and interpretation of surveillance data. The OHS Codex framework comprises four high-level "action" principles, which respectively support collaboration, knowledge exchange, data interoperability, and dissemination. These principles match well with priority areas identified in the "Tripartite Guide to Addressing Zoonotic Diseases in Countries" published by WHO, FAO and OIE. Within each of the four principles, the OHS Codex provides a collection of useful resources as well as pointers to success stories for the application of these resources. As the OHS Codex is designed as an open community framework, it will continuously evolve and adapt to the needs of the OH community in the future.

Minimum Information Required to Annotate Food Safety Risk Assessment Models (MIRARAM).

In the last decades, mathematical models and model-based simulations became important elements not only in the area of risk assessment concerning microbiological and chemical hazards but also in modelling biological phenomena in general. Unfortunately, many of the developed models are published in non-standardized ways, which hinders efficient exchange, re-use and continuous improvement of models within the risk assessment domain. The establishment of guidelines for model annotation is an important pre-condition to overcome these obstacles. Additionally, implementation of annotation guidelines can improve transparency, quality control and even aid the clarification of intellectual property rights. Here, we address the question of "What is the minimum set of metadata that should be provided for a model in the risk assessment domain?". The proposed guideline focuses on food safety risk assessment models and is called "Minimum Information Required to Annotate food safety Risk Assessment Models (MIRARAM)". MIRARAM supports the model creator during the model documentation step and could also be used as a checklist by scientific journal editors or database curators. Software developers could take up MIRARAM and develop easy-to-use software tools or new features in existing programs that can help model creators to provide proposed model annotations in harmonized file formats. Based on experiences from similar guidelines in related scientific disciplines (like systems biology), it is expected that MIRARAM could contribute to the promotion of application and re-use of models as well as to implementing more standardized quality control in the food safety modelling domain.

Bacterial community analysis for investigating bacterial transfer from tonsils to the pig carcass.

Tonsils in the oral cavity are an important source of contamination during pig slaughter, but have not received as much attention as faecal contamination. In the present study, ten pigs were sampled from tonsils, faeces and three different areas on each carcass. The samples were analysed by both culturing of Escherichia coli and Yersinia enterocolitica and by 16S rRNA gene sequencing to characterize the bacterial communities. Comparing culture data from deep tonsil tissue and tonsil surface showed similar numbers of E. coli but significantly higher numbers of Y. enterocolitica in the deep tissue samples. Microbiota analysis showed similar bacterial communities in the two sample types at phylum level, while comparison at genus level showed significant differences between the relative abundance of several genera in the two sample types. The finding of a significantly higher relative abundance of Yersinia in tonsil tissue compared to tonsil surface supported the culture analysis. The microbiota analysis also investigated characteristics of the bacterial community that could discriminate bacterial transfer from tonsils and faeces to the carcass during slaughter. The microbiota analyses demonstrated that Fusobacteria and Proteobacteria are the most abundant phyla in tonsils, while Firmicutes showed the highest relative abundance in faeces. The dominating phylum on carcasses was Proteobacteria. Besides Proteobacteria, the swabbing area on the forepart of the carcass, showed a higher relative abundance of Firmicutes and Fusobacteria compared to swabbing areas on the rear part and mid-section of the carcass. Principal coordinate analysis showed clear clustering of samples based on sample source (tonsils, faeces and carcass). Carcass swab samples from the forepart tended to cluster closer to the tonsil samples compared to carcass swab samples from the rear part and mid-section. Identification of the genera Fusobacterium, Moraxella, Actinobacillus and non-E. coli genera of the family Enterobacteriaceae in carcass swabs could indicate tonsil contamination, while faecal contamination would more likely include higher prevalence of bacteria belonging to the class of Clostridia. The present study supports that it is possible to identify bacterial groups that are indicative for either tonsil or faecal carcass contamination. The level and composition of Enterobacteriaceae on the carcasses did, however, indicate that other sources of meat contamination than tonsils and faeces may be important, such as the process environment.