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Correlation-Based Discovery of Disease Patterns for Syndromic Surveillance
Michael Rapp1, Moritz Kulessa1, Eneldo Loza Mencía1
1Knowledge Engineering Group, Technical University of Darmstadt, Darmstadt, Germany.
Frontiers in Big Data
|January 31, 2022
Summary
This study introduces a data-driven method for early infectious disease outbreak detection by correlating health indicators with infection rates. The approach helps identify disease patterns for timely public health interventions.
Area of Science:
- Epidemiology
- Public Health
- Data Science
Background:
- Early detection of infectious disease outbreaks is crucial for containment.
- Syndromic surveillance aims for timely outbreak detection using early symptoms, but defining these patterns is challenging due to symptom overlap and variability.
- Existing methods often rely on confirmed cases, which can delay outbreak disclosure.
Purpose of the Study:
- To develop a novel, data-driven approach to discover reliable disease patterns in historical health data.
- To support epidemiologists in identifying early indicators of infectious disease outbreaks.
- To correlate health-related data indicators with reported infection numbers in specific regions.
Main Methods:
- A data-driven approach analyzing correlations between health indicators and reported infection numbers.
- Utilizing historical data from emergency departments for pattern discovery.
- Experimental study focusing on three infectious diseases.
Main Results:
- The proposed approach successfully identified patterns correlating with reported infections.
- The method effectively pinpointed indicators related to specific infectious diseases.
- Demonstrated the potential for timely outbreak detection through pattern recognition.
Conclusions:
- The data-driven approach shows promise for discovering infectious disease patterns and relevant indicators.
- Highlights the need for strategies to handle noisy and unbalanced data.
- Emphasizes the importance of integrating domain expert feedback for improved accuracy and reliability.
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