Table 3 Potential approaches for one health genomic data integration
Reference | Overview | Approach |
|---|---|---|
Describes a shared secure surveillance platform between human and veterinary medicine in Switzerland | • Includes human, animal, environmental, and food isolates • Includes sequence data and associated metadata • Features controlled data access • Allow complex dynamic queries • Features dashboards • Automated data sharing with international repositories • Incoming data are quality- checked, curated, standardized where needed and processed/annotated with dedicated bioinformatics pipelines | |
Describes a shared platform for multisectoral data collection and bioinformatic analysis in Italy | • Includes isolates from food, environment, human and non-human and associated metadata • Bioinformatics tools sharing a common workflow system • Process for quality control | |
Outlines best practices for One Health contributions to open access databases | • Store the sequence with metadata • Inclusion of specimens from human, animals, food, and environment • Thresholds for QC • Contact information for submitters • Process for updating data, responding to requests, and correcting submissions | |
Describes a federated ecosystem as a possible solution for sharing genomic data | • Encourages use of federated databases • Connections of distributed databases through APIs | |
Describes a One Health system for hepatitis E virus surveillance in Europe | • Inclusion of human, animal, food, and environmental samples • Secure online environment • All sequence data with a restricted set of associated metadata become publicly available at a time specified by the data provider | |
Describes 10 recommendations for an informatic ecosystem to support pathogen genomic analysis in public health agencies | • Consistent data model (pairing sequence data with metadata) • Strengthen APIs to automate querying and analysis • Data management and stewardship • Bioinformatics pipelines open-source and accessible • Develop modular pipelines for data visualization and exploration • Improve the reproducibility of bioinformatics analysis • Utilize cloud computing • Expand the technical workforce • Improve the integration of genomic epidemiology with traditional epidemiology • Best practices to support open data sharing | |
Describes 5 objectives for genomic surveillance strengthening | • Improve access to tools for better geographic representation • Strengthen the workforce to deliver at speed, scale, and quality • Enhance data sharing and utility for public health decision-making and action • Maximize connectivity • Maintain a readiness posture for emergencies |