Animal health and welfare data collection (SIGMA)

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The SIGMA approach for data collection was developed in the context of the SIGMA project (2018-2021) to improve the collection of animal health data from European countries.

SIGMA project: a new way of collecting animal health data

Before the introduction of the SIGMA approach, the collection of animal health data was driven by the unique goal of addressing specific questions received by the different requestors, such as the European Commission and the European Parliament. Each data collection activity was performed only once, for specific needs and purposes, and without necessarily ensuring data consistency over time.

Over the past years, the need for performing a continuous monitoring of important diseases like African Swine Fever and Avian Influenza has become more stringent. The number of data providers has increased, requests for data submission have become more frequent, and the information requested more detailed and sophisticated.

It is in this context that the SIGMA project was born with the aim of:

  • setting new standards for the collection of essential data, i.e. the size of the domestic animal population Community of humans, animals or plants from the same species.;
  • increasing the quality and comparability of the data received across Member States and over time;
  • moving from manual data submission to an automated process;
  • shortening the time needed to retrieve up-to-date data for risk assessment  A specialised field of applied science that involves reviewing scientific data and studies in order to evaluate risks associated with certain hazards. It involves four steps: hazard identification, hazard characterisation, exposure assessment and risk characterisation.;
  • providing Members States with instruments to automatically produce national reports on animal health and surveillance in a protected environment;
  • avoiding double reporting to EFSA and, possibly, to other data repositories.

The project is being implemented by a consortium led by the Istituto Zooprofilattico Sperimentale (IZS) Abruzzo e Molise “G. Caporale” in partnership with the Friedrich Loeffler Institut (FLI), the Swedish National Veterinary Institute (SVA), the Bulgarian Food Safety Agency and the Institute of Veterinary Medicine and Animal Sciences at the Estonian University of Life Sciences.

Animal population data model

Main facts:

  • designed to be adapted to all domestic animal species A subdivision of the genus, a species is a group of closely related and similar-looking organisms; for example, in the case of Homo sapiens (humans), the second part of the name (sapiens) represents the species. in Europe, granting integrity of the data collected over time and across different countries;
  • the level of detail is at the farm level;
  • standards are mainly taken from the animal health law and, only when missing, from other relevant legislation or agreed by the SIGMA expert working group;
  • interoperable with test result data stored in the EFSA Scientific Data Warehouse.

Laboratory data on animal disease testing data model

Main facts:

  • built on EFSA SSD2 standards;
  • tailored to animal health needs (e.g. “animal-based” samples)
  • linked with animal population data by means of the farm ID number;
  • when joined with animal population data, they provide insights on:
    • how many farms were sampled,
    • how many animals were at each farm,
    • how many of them were sampled,
    • which of them tested positive…
    • …over time;
  • include information collected on other potential risk factors (e.g. outdoor/indoor housing).

Country cards

Main facts:

  • open access website providing overview and insights on data ownership in each European country;
  • developed and maintained by EFSA with the contribution of the EFSA Focal Points.

Reports on data flows

EFSA, with the support of the SIGMA Consortium and the contribution of the Focal Points, has published a series of technical reports describing the flows of national data, from their generation to their storage in national repositories. These reports will be particularly important for the goals set in the SIGMA 2.0 project, which aims at further automating the data submission process.

SIGMA EST mapping tool

Main facts:

  • web application for the automated transformation of national data (regardless of language, alphabet or standards) into an EFSA standard database;
  • requires an initial (unique) configuration, with no extra effort needed for the following (usually yearly) translations;
  • access is granted upon creation of credentials.

SIGMA validation tool

Main facts:

  • set of maps, tables and plots for an informed validation of data;
  • all outputs can be downloaded and used for other purposes (e.g. national reporting activities)
  • developed with EFSA’s current Business Intelligence tool (MicroStrategy).

Q&A

1. What is SIGMA data collection?

SIGMA can be seen as an integrated approach to data collection, offering a set of tools to make the process easier and quicker. Only the standards (i.e. the animal population data model and the laboratory data model) are compulsory. Therefore, a “SIGMA data collection”, ultimately, is the collection of data following the SIGMA standards.

2. What is new about SIGMA?

The main novelty introduced with SIGMA are its standards, in particular the animal population data model.

Previously, EFSA collected data on European livestock with a much lower level of detail, and the information gathered was not centralised anywhere: only the originating countries kept records of the farms in their territory. However, good knowledge of the domestic animal population is crucial for a proper risk assessment. For instance, a basic epidemiological parameter like the prevalence The proportion of a population found to have a condition.* of a disease cannot be calculated if the population is not known.

For this reason, it was decided to introduce a dedicated module for the collection of livestock data, at farm level. These will eventually be joined with laboratory data at EFSA to have a full picture. At the end of the process, EFSA will be able to assess:

  • how many farms are present in a given area;
  • how many of them were tested;
  • how many animals were at each farm;
  • how many animals were tested;
  • how many tested animals were positive.

With the additional information collected (potential risk factors) and the information about the entire population, EFSA will be able to improve the precision of the estimations of the relevant epidemiological parameters.

* Prevalence is the ratio between the number of (randomly chosen) animals testing positive and the population to which they belong to.

3. Has the SIGMA approach been validated?

Yes. In 2021 the African Swine Fever data collection was performed using the SIGMA approach. Despite the introduction of new elements (a complete new set of data on domestic farming, a new mapping tool) the data were submitted without additional delay as compared to previous years. It is therefore expected that, starting from 2022, data will be received in a shorter time, as the learning curve has reached its plateau.

4. What are the challenges?

One of the main goals of SIGMA is to further improve the experience of data providers (Member States) in submitting data: the aim is to make the process so easy that at any time, e.g. in the case of an urgent risk assessment request, data providers will be able to send the required data within few days.

A second important aim is to avoid any double reporting, in particular to EFSA. It appears that the SIGMA animal population data model enables the collection of data at such a level of detail that they can be aggregated in any possible way, thus also addressing the needs of other activities, e.g. the annual zoonoses report. The goal is to ask data providers to submit their data only once and in a unique format.

5. Which animal diseases are targeted by SIGMA?

At present, SIGMA fully covers the African Swine Fever data collection. The next animal disease SIGMA will cover is Avian Influenza. In 2023, a pilot will be run with volunteering Member States, and in March 2024, the first official data submission on Avian Influenza in SIGMA will take place.

Other diseases and related target animal populations will also be covered by SIGMA, with the priority given to those identified by the EFSA Working Group on the One Health surveillance mandate.

6. What kind of data are collected?

Generally speaking, two types of data are collected following the SIGMA approach:

  • data on animal populations (e.g. poultry, pigs and bovines), including farm location, farm size, animal species, and type of production;
  • data on laboratory test results, including sample ID, establishment ID, type of sample, date of sampling and testing, diagnostic test used, and results.
7. What granularity of data is required?

Animal population data must be provided at establishment level (farm or even more defined locations within the farm, if these are officially registered at national level, e.g. houses in the context of a poultry farm). The data model can record geolocation data (latitude and longitude) and, if needed, data providers may also use lower levels of resolution.

Surveillance data (laboratory test results) must be provided at result level. Here, a variable of crucial importance is the Establishment ID, to be reported when the sample is collected from a farmed animal. The ID enables EFSA to connect the test results with existing information about the farm where the animal was bred and, eventually, to perform a proper risk assessment and risk factor analysis. Failure to submit this information will considerably limit the analytical options of EFSA to better understand the behaviour of the disease – and to help the farmers.

8. What happens with SIGMA data once submitted to EFSA?

All data submitted to EFSA, once validated by the Member State, are stored in the EFSA Scientific Data Warehouse. The data are then used by EFSA to perform risk assessments and produce scientific outputs. In line with relevant transparency requirements, the data are proactively published in the EFSA Knowledge Junction (Zenodo).

9. How protected are the data once submitted to EFSA?

Before the publication of a scientific output, the data it is based on are not accessible to anyone but EFSA. After publication, the data are aggregated and made publicly available in the EFSA Knowledge Junction (Zenodo).

All data covered by the GDPR (e.g. farm ID, geolocation, etc.) are protected and will not be included in the set of data published in the EFSA Knowledge Junction.

Should data providers consider some of their data shared with EFSA as confidential, they can claim confidentiality, following the official procedure.

10. What are the conditions to take advantage of the SIGMA framework?

The minimum requirements are:

  • data to be submitted need to be in a digital format;
  • if a data provider intends to use the SIGMA EST mapping tool, an initial one-time investment needs to be made for the configuration.

What’s next?

The SIGMA 2.0 project (2022-2025) has five different work packages (WPs):

  • WP01 – SIGMA Generalisation: additional animal diseases and related target populations will be covered by the SIGMA framework.
  • WP02 – SIGMA Extension: a new data model will be designed for the collection of animal welfare data, interoperable with the animal population and the laboratory test results data.
  • WP03 – SIGMA EST enhancement: the mapping tool will be enhanced to improve the experience of data providers in submitting data to EFSA, and new features will be added (e.g. a validation step before submission to EFSA to check compliance with business rules).
  • WP04 – SIGMA M2M: the data submission process will become completely automated (machine to machine).
  • WP05 – SIGMA Kaleidoscope: a unique interface for the visualisation of data submitted in the SIGMA framework will enable users to explore, visualise, and create reports based on available data submitted to EFSA.

Toolbox

SIGMA data models

SIGMA country cards

SIGMA national data flows

SIGMA EST

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