- Research Project
Pig Health Info System Recording, analysing and improving animal health in Swiss pig herds
The ‘Pig Health Info System’ project involved the development of a system for monitoring the health status of pig herds in Switzerland. The system comprises various apps for data collection, a dashboard for the visual portrayal of evaluations of aggregated data, and an alert system that automatically raises the alarm in case of critical events.
- Lead school(s) School of Engineering and Computer Science
- Institute Institute for Cybersecurity and Engineering ICE
- Funding organisation Federal Food Safety and Veterinary Office FSVO
- Duration 01.05.2019 - 30.04.2022
- Project management Prof. Heiko Nathues, Vetsuisse Faculty, University of Bern
- Head of project Prof. Heiko Nathues, Vetsuisse Faculty, University of Bern
Prof. Ulrich Fiedler, BFH-TI
Prof. Xaver Sidler, Vetsuisse Faculty, University of Zurich
Dr Claudia Egle, Vetsuisse Faculty, University of Bern
Hugues Michel, BFH-TI
Francesco Romeo, BFH-TI
Pig Health Service of SUISAG
Swiss Association of Pig Medicine (SVSM)
Swiss Association of Veterinary Laboratory Diagnostics (SVVLD)
Bern University of Applied Sciences
University of Bern
University of Zurich
- Keywords Animal health, app development, pig, monitoring, data analysis, health database, anonymisation, data protection
Timely monitoring of animal health in Swiss pig herds and the early detection of outbreaks of disease has always been difficult, as the records of vets on clinical observations in pig herds have generally been too brief and unstructured and were often only stored locally at the veterinary practice.
To enable the monitoring of Swiss pig herds, the idea is for animal health data to be logged in the piggery using smartphones or tablets as part of the ‘Pig Health Info System’ (PHIS) project, then analysed and displayed in anonymised form on a partially publicly accessible website.
The vets enter the findings of their inspection directly from the piggery in standardised form using the app on their smartphones. When complete, the data is automatically compiled into a report and sent electronically to the livestock farmer and the vet. The app seeks to make documentation easier for vets and improve their service for livestock farmers
Data evaluation and display
The standardised recording of the animal health data means that it can be used for further processing. The data is processed and evaluated. The results are then displayed in diagrams and charts. The results from the subsequent analysis of the health data serve to improve the monitoring and early identification of diseases. This allows problems to be identified and addressed at an early stage. It also promotes animal health and animal welfare.
The project was carried out at the Vetsuisse Faculty in Bern in conjunction with the Institute for Cybersecurity and Engineering ICE at Bern University of Applied Sciences BFH. While the Vetsuisse Faculty defined the veterinary requirements and monitored their implementation, BFH was responsible for the technical implementation. The project followed the SCRUM process model.
First, an app was developed to record animal health data using configurable questionnaires. Data security was given high priority in order to boost the acceptance of the app. The recorded health data of the herds are saved to a database and evaluated in aggregated form. These evaluations are displayed in visual form in a dashboard, enabling the health of the animals to be monitored and the spread of diseases to be detected at an early stage. It is backed by an alert system.
Results & Outlook
The development of the app, database and dashboard is complete. The follow-on project will support the rollout across the whole of Switzerland. The industry and in particular the veterinary profession are informed through different channels. It will be some time before the PHIS dashboard is open to the public: before that happens, sufficient data must be collected constantly over a certain period of time so that meaningful evaluations are possible.