Scalable Data Integration for Disease Surveillance (SDIDS)
Data for global disease surveillance are fragmented across diseases, countries, funders, and a wide range of clinical settings. This fragmentation of data makes it challenging to assess population health, target disease control activities, and evaluate the effect of interventions. We are developing the SDIDS software platform to integrate surveillance data and make them available to support global health decision making. A proof-of-concept version of the system has been to integrate malaria surveillance data for Uganda. SDIDS makes extensive use of ontologies, including an ontology of data sources and an ontology of global health. Raw data sources are mapped to these ontologies and then automatically translated into a common format, where the integrated data can then be accessed by software to calculate and visualize a variety of indicators. We are now in the process of scaling-up SDIDS to include data for the main causes of under-five mortality in Africa.
In Collaboration with the Uganda Malaria Surveillance Project and the National Malaria Control Programme.