Correlation-Based Discovery of Disease Patterns for Syndromic Surveillance

Michael Rapp, Moritz Kulessa, Eneldo Loza Mencía, Johannes Fürnkranz

Research output: Contribution to journalArticlepeer-review

Abstract

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. As a first step toward the goal 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 preliminary experimental study, we use data from several emergency departments to discover disease patterns for three infectious diseases. Our results show the potential of the proposed approach to find patterns that correlate with the reported infections and to identify indicators that are related to the respective diseases. It also motivates the need for additional measures to overcome practical limitations, such as the requirement to deal with noisy and unbalanced data, and demonstrates the importance of incorporating feedback of domain experts into the learning procedure.
Original languageEnglish
Article number784159
Number of pages128
JournalFrontiers in Big Data
Volume4
Issue number784159
DOIs
Publication statusPublished - 2021

Fields of science

  • 303007 Epidemiology
  • 102001 Artificial intelligence
  • 102019 Machine learning
  • 102033 Data mining

JKU Focus areas

  • Digital Transformation

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