The increasing threat of disease spread in a globalized world has promoted the development of electronic systems capable of screening large amounts of health data in real-time, in order to provide decision-making support around infectious disease control. While the quantity and variety of health data sources is increasing rapidly both in animal and public health, the automated interpretation of these data by computers remains a challenge.

The step of data interpretation remains the most time-consuming step in the development of electronic systems for early detection of diseases. Furthermore, when such systems are developed in a country using one specific data source, results are not comparable among systems using different data sources, and are especially hard to compare among countries.

An ontology defines a common vocabulary for researchers who need to share information in a domain. It includes machine-interpretable definitions of basic concepts in the domain and relations among them.

A project was launched to develop an Animal Health Surveillance Ontology (AHSO) to facilitate the development of smart systems for data-driven disease surveillance and early disease detection. It will allow the development of systems across different standard coding practices at source.

Project sources:

• Open book with information about the project and background material on ontologies
• GitHub page
• Current codes (owl file)
• Submit a request

If you are interested on resources that help you understand more about ontologies, please visit the first chapter in the "AHSO open book". For information, please contact us at ahso@datadrivensurveillance.org.