Evaluation of Syndromic Surveillance Systems; Data Analysis


The goal of the series of modules is to provide an introductory knowledge of syndromic surveillance for interested practitioners and to stimulate healthcare provider cooperation and involvement with syndromic surveillance.

• State the purposes of evaluation of syndromic surveillance systems.
• Identify major aspects of data quality, including timeliness, completeness, accuracy, and representativeness.
• Describe the pros and cons of the analysis of real events and simulation studies as evaluation tools.
• Apply performance measures such as sensitivity, false positive rate, and timeliness.
• Characterize the utility of syndromic surveillance systems.
• Describe the role of analyzing population data in routine health monitoring.
• Identify the common approaches for detection of temporal anomalies and significant spatial clusters in surveillance data.
• List adaptive methods for background data estimation to enable recognition of anomalies at controlled alert rates.
• Explain the use of algorithm specificity and sensitivity in the context of daily syndromic surveillance.
• List underlying principles of scan statistics for cluster detection.