short-paper

Detection of Infectious Disease Outbreaks in Search Engine Time Series Using Non-Specific Syndromic Surveillance with Effect-Size Filtering

Authors:

Elad Yom-TovAuthors Info & Claims

WWW '22: Companion Proceedings of the Web Conference 2022

Pages 924 - 929

https://doi.org/10.1145/3487553.3524672

Published: 16 August 2022 Publication History

Abstract

Novel infectious disease outbreaks, including most recently that of the COVID-19 pandemic, could be detected by non-specific syndromic surveillance systems. Such systems, utilizing a variety of data sources ranging from Electronic Health Records to internet data such as aggregated search engine queries, create alerts when unusually high rates of symptom reports occur. This is especially important for the detection of novel diseases, where their manifested symptoms are unknown.

Here we improve upon a set of previously-proposed non-specific syndromic surveillance methods by taking into account both how unusual a preponderance of symptoms is and their effect size.

We demonstrate that our method is as accurate as previously-proposed methods for low dimensional data and show its effectiveness for high-dimensional aggregated data by applying it to aggregated time-series health-related search engine queries. We find that in 2019 the method would have raised alerts related to several disease outbreaks earlier than health authorities did. During the COVID-19 pandemic the system identified the beginning of pandemic waves quickly, through combinations of symptoms which varied from wave to wave.

Thus, the proposed method could be used as a practical tool for decision makers to detect new disease outbreaks using time series derived from search engine data even in the absence of specific information on the diseases of interest and their symptoms.

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Cited By

Wang ZPei CMa MWang XLi ZPei DRajmohan SZhang DLin QZhang HLi JXie GChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency PerspectiveProceedings of the ACM Web Conference 202410.1145/3589334.3645710(3096-3105)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645710
Spaniol MBaeza-Yates RAlonso O(2023)Report on the 12th Temporal Web Analytics Workshop (TempWeb 2022) at WWW 2022ACM SIGIR Forum10.1145/3582900.358290956:2(1-6)Online publication date: 31-Jan-2023
https://dl.acm.org/doi/10.1145/3582900.3582909

Index Terms

Detection of Infectious Disease Outbreaks in Search Engine Time Series Using Non-Specific Syndromic Surveillance with Effect-Size Filtering
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems
    2. Health informatics
2. Mathematics of computing
  1. Probability and statistics
    1. Statistical paradigms
      1. Contingency table analysis
      2. Time series analysis

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Published In

cover image ACM Conferences

WWW '22: Companion Proceedings of the Web Conference 2022

April 2022

1338 pages

ISBN:9781450391306

DOI:10.1145/3487553

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Lionel Médini
Université Lyon 1, France
,
Ivan Herman
W3C / retired

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Published: 16 August 2022

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Cited By

Wang ZPei CMa MWang XLi ZPei DRajmohan SZhang DLin QZhang HLi JXie GChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency PerspectiveProceedings of the ACM Web Conference 202410.1145/3589334.3645710(3096-3105)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645710
Spaniol MBaeza-Yates RAlonso O(2023)Report on the 12th Temporal Web Analytics Workshop (TempWeb 2022) at WWW 2022ACM SIGIR Forum10.1145/3582900.358290956:2(1-6)Online publication date: 31-Jan-2023
https://dl.acm.org/doi/10.1145/3582900.3582909

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