MATRIX: Connecting dimensions in One-Health surveillance
|Start:||1 January 2020|
|Key Words:||One-Health Surveillance, Cross-disciplinary, Multi-sectorial, Foodborne pathogens, Emerging threats|
|Contact:||Guido Benedetti (SSI)|
The Project #MATRIX
MATRIX aims to advance the implementation of One Health Surveillance (OHS) in practice, by building on existing resources, adding value to them and creating synergies among the sectors. The previous OHEJP integrative projects strengthened collaboration and communication at the end of the surveillance continuum in each sector. MATRIX wants to strengthen OHS along the whole surveillance pathway.
This work has two fundamental premises: i) the need for a problem-oriented approach using real-life cases; ii) the understanding that different countries have different realities.
MATRIX creates solutions for European countries to support and to advance the implementation of OHS:
- OH-EpiCap Tool – This is an interactive tool to evaluate the capacities and capabilities for the OHS of a specific sector and/or pathogen of choice. Additionally, the tool allows the benchmarking of surveillance capacities and capabilities for comparison. More information is available here: OH-EpiCap tool flyer and OH-EpiCap tool user guide.
- Roadmap to develop national OHS – This is a guideline that countries can use to develop OHS according to their needs and resources. The roadmap expands the work of the OHEJP COHESIVE project (https://www.ohras.eu/page/home).
- Manual for OHS Dashboards – This is an online dashboard inventory and practical manual to facilitate the design and implementation of OHS dashboards using open source tools. More information is available here (https://sva-se.github.io/MATRIX-dashboards/).
- Guidelines and checklists:
- An interactive guide to adapt existing animal health, public health and food safety surveillance frameworks into multi-sectorial systems.
- Best-practices of cross-sectorial collaboration for data collection, analysis, and dissemination of surveillance results. A checklist to assess the need for collaboration among the animal health, public health and food safety sectors according to different surveillance objectives.
- A guide to design, implement, and evaluate official controls within the food sector.
MATRIX promotes and expands:
- Codex: The Knowledge Integration Platform – this is a platform to establish a high-level framework to support the mutual understanding and information exchange between OHS sectors. More information is available here (https://oh-surveillance-codex.readthedocs.io/en/latest/index.html).
- Food Safety Knowledge Exchange (FSKX) Format – this is a format that supports the One Health community in sharing and re-using mathematical models as well as data analysis procedures. More information is available here (https://foodrisklabs.bfr.bund.de/fskx-food-safety-knowledge-exchange-format/).
The problem-oriented approach of the project is reflected in the creation of hazard-specific tracks to ensure that the solutions that MATRIX develops are relevant to specific pathogens. MATRIX actually means “a frame of solutions and hazards”. The hazards were chosen based on the operational priorities of MATRIX partner institutes and their One Health relevance. They are Listeria, Salmonella, Campylobacter and emerging threats, including antimicrobial resistance.
The Solutions for One Health Surveillance in Europe webinars
In 2022, MATRIX organizes a series of webinars to present the solutions that are being developed to support and advance the implementation of OHS in European countries.
For full information, download the MATRIX Webinar Series 2022 flyer.
Sundermann, EM., Nauta, M., Swart, A. (2021). A ready-to-use dose-response model of Campylobacter jejuni implemented in the FSKX-standard. Food Modelling Journal 2: e63309. DOI: https://doi.org/10.3897/fmj.2.63309
Zenodo link: https://zenodo.org/record/4923018
Sundermann EM, Correia Carreira G, Käsbohrer A (2021). A FSKX compliant source attribution model for salmonellosis and a look at its major hidden pitfalls. Food Modelling Journal 2: e70008. DOI: https://doi.org/10.3897/fmj.2.70008
Zenodo link: https://zenodo.org/record/5674165#.YZ5rANDMJPY