Correlation-based Discovery of Disease Patterns for Syndromic Surveillance

TL;DR

This paper introduces a data-driven, correlation-based method for discovering disease patterns in health data to improve early outbreak detection in syndromic surveillance.

Contribution

It presents a novel approach that leverages correlations in historic health data to identify disease patterns, aiding epidemiologists in outbreak detection.

Findings

The approach successfully finds patterns correlating with reported infections.

It identifies indicators related to specific infectious diseases.

The method demonstrates effectiveness across multiple emergency department datasets.

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. 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.