Applied Ontologies for Global Health Surveillance and Pandemic Intelligence

Abstract

AbstractGlobal health surveillance and pandemic intelligence rely on the systematic collection and integration of data from diverse distributed and heterogeneous sources at various levels of granularity. These sources include data from multiple disciplines represented in different formats, languages, and structures posing significant integration challenges This article provides an overview of challenges in data driven surveillance. Using Malaria surveillance as a use case we highlight the contribution made by emerging semantic data federation technologies that offer enhanced interoperability, interpretability and explainability through the adoption of ontologies. The paper concludes with a focus on the relevance of these technologies for ongoing pandemic preparedness initiatives.