Statistical methods for disease surveillance have focused mainly on the performance of outbreak detection algorithms and have not paid sufficient attention to the data quality and representativeness, two factors that are especially important in developing countries. Whether the final endpoint of surveillance is outbreak detection, situational awareness, or estimation of trends, these aims cannot be accomplished without adequate intermediate outcomes such as reporting coverage, data quality and completeness. We advocate the use of a more holistic approach to statistical analyses in which indicators relate to the entire surveillance process. Assessment of data quality using a diverse mix of data sources and analytical methods is key during each stage of its implementation. Careful, close monitoring of selected indicators is also crucial to evaluate whether systems are reaching their proposed goals at each stage. A more balanced, diverse analysis of Surveillance Systems data is essential in the current context, as new Surveillance Systems are implemented in response to pandemic threats and the recently updated international health regulations.
