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Correlation-Based Discovery of Disease Patterns for Syndromic Surveillance

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, synd...

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Autores principales: Rapp, Michael, Kulessa, Moritz, Loza Mencía, Eneldo, Fürnkranz, Johannes
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8793623/
https://www.ncbi.nlm.nih.gov/pubmed/35098113
http://dx.doi.org/10.3389/fdata.2021.784159
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author Rapp, Michael
Kulessa, Moritz
Loza Mencía, Eneldo
Fürnkranz, Johannes
author_facet Rapp, Michael
Kulessa, Moritz
Loza Mencía, Eneldo
Fürnkranz, Johannes
author_sort Rapp, Michael
collection PubMed
description 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.
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spelling pubmed-87936232022-01-28 Correlation-Based Discovery of Disease Patterns for Syndromic Surveillance Rapp, Michael Kulessa, Moritz Loza Mencía, Eneldo Fürnkranz, Johannes Front Big Data Big Data 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. Frontiers Media S.A. 2022-01-13 /pmc/articles/PMC8793623/ /pubmed/35098113 http://dx.doi.org/10.3389/fdata.2021.784159 Text en Copyright © 2022 Rapp, Kulessa, Loza Mencía and Fürnkranz. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Big Data
Rapp, Michael
Kulessa, Moritz
Loza Mencía, Eneldo
Fürnkranz, Johannes
Correlation-Based Discovery of Disease Patterns for Syndromic Surveillance
title Correlation-Based Discovery of Disease Patterns for Syndromic Surveillance
title_full Correlation-Based Discovery of Disease Patterns for Syndromic Surveillance
title_fullStr Correlation-Based Discovery of Disease Patterns for Syndromic Surveillance
title_full_unstemmed Correlation-Based Discovery of Disease Patterns for Syndromic Surveillance
title_short Correlation-Based Discovery of Disease Patterns for Syndromic Surveillance
title_sort correlation-based discovery of disease patterns for syndromic surveillance
topic Big Data
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8793623/
https://www.ncbi.nlm.nih.gov/pubmed/35098113
http://dx.doi.org/10.3389/fdata.2021.784159
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