Correlation-based Discovery of Disease Patterns for Syndromic Surveillance
This work addresses the problem of early outbreak detection for epidemiologists by providing a tool to define disease patterns, though it appears incremental as it builds on existing syndromic surveillance methods with a new correlation-based technique.
The authors tackled the challenge of defining reliable disease patterns for syndromic surveillance by developing a data-driven approach that uses correlations between health indicators and reported infections to discover patterns from historical data. In an evaluation using emergency department data for three infectious diseases, the approach found patterns that correlated with reported infections and often identified disease-related indicators.
Early outbreak detection is a key aspect in the containment of infectious diseases, as it enables the identification and isolation of infected individuals before the disease can spread to a larger population. Instead of detecting unexpected increases of infections by monitoring confirmed cases, syndromic surveillance aims at the detection of cases with early symptoms, which allows a more timely disclosure of outbreaks. However, the definition of these disease patterns is often challenging, as early symptoms are usually shared among many diseases and a particular disease can have several clinical pictures in the early phase of an infection. To support epidemiologists in the process of defining reliable disease patterns, we present a novel, data-driven approach to discover such patterns in historic data. The key idea is to take into account the correlation between indicators in a health-related data source and the reported number of infections in the respective geographic region. In an experimental evaluation, we use data from several emergency departments to discover disease patterns for three infectious diseases. Our results suggest that the proposed approach is able to find patterns that correlate with the reported infections and often identifies indicators that are related to the respective diseases.