Abstract
The Scalable Data Integration for Disease Surveillance project (SDIDS) is developing tools to integrate and present surveillance data from multiple sources, with an initial focus on malaria. Consideration of data quality is particularly important when integrating data from diverse clinical, population-based, and other sources. We used a hierarchical system to organize data quality properties by capturing metadata elements relevant to provenance and generate a framework with which to assess the quality of the surveillance indicators. The resulting framework enables diverse decision makers to consistently and confidently interpret available surveillance data, indicators, and the analyses based on them.