Drivers for the development of an Animal Health Surveillance Ontology (AHSO)

Fernanda C Dórea; Flavie Vial; Karl Hammar; Ann Lindberg; Patrick Lambrix; Eva Blomqvist; Crawford W Revie

doi:10.1016/j.prevetmed.2019.03.002

Drivers for the development of an Animal Health Surveillance Ontology (AHSO)

Prev Vet Med. 2019 May 1:166:39-48. doi: 10.1016/j.prevetmed.2019.03.002. Epub 2019 Mar 9.

Authors

Fernanda C Dórea¹, Flavie Vial², Karl Hammar³, Ann Lindberg⁴, Patrick Lambrix⁵, Eva Blomqvist⁶, Crawford W Revie⁷

Affiliations

¹ Department of Disease Control and Epidemiology, National Veterinary Institute, Sweden. Electronic address: fernanda.dorea@sva.se.
² Epi-Connect, Skogås, Sweden.
³ Department of Computer Science and Informatics, Jönköping University, Sweden; Department of Computer and Information Science, Linköping University, Sweden.
⁴ Department of Disease Control and Epidemiology, National Veterinary Institute, Sweden.
⁵ Department of Computer and Information Science, Linköping University, Sweden; Swedish e-Science Centre, Linköping University, Sweden.
⁶ Department of Computer and Information Science, Linköping University, Sweden.
⁷ Atlantic Veterinary College, University of Prince Edward Island, Canada.

PMID: 30935504
DOI: 10.1016/j.prevetmed.2019.03.002

Abstract

Comprehensive reviews of syndromic surveillance in animal health have highlighted the hindrances to integration and interoperability among systems when data emerge from different sources. Discussions with syndromic surveillance experts in the fields of animal and public health, as well as computer scientists from the field of information management, have led to the conclusion that a major component of any solution will involve the adoption of ontologies. Here we describe the advantages of such an approach, and the steps taken to set up the Animal Health Surveillance Ontological (AHSO) framework. The AHSO framework is modelled in OWL, the W3C standard Semantic Web language for representing rich and complex knowledge. We illustrate how the framework can incorporate knowledge directly from domain experts or from data-driven sources, as well as by integrating existing mature ontological components from related disciplines. The development and extent of AHSO will be community driven and the final products in the framework will be open-access.

Keywords: Classification; Standards; Syndromic surveillance; Terminology; Vocabulary.

MeSH terms

Animals
Biological Ontologies*
Population Surveillance / methods
Sentinel Surveillance / veterinary*