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Photo of Mattias Ohlsson

Mattias Ohlsson

Professor

Photo of Mattias Ohlsson

Surveillance of Disease Outbreaks Using Unsupervised Uni-Multivariate Anomaly Detection of Time-Series Symptoms

Author

  • Atiye Sadat Hashemi
  • Mirfarid Musavian Ghazani
  • Mattias Ohlsson
  • Jonas Björk
  • Dominik Dietler

Editor

  • John Mantas
  • Arie Hasman
  • George Demiris
  • Kaija Saranto
  • Michael Marschollek
  • Theodoros N. Arvanitis
  • Ivana Ognjanovic
  • Arriel Benis
  • Parisis Gallos
  • Emmanouil Zoulias
  • Elisavet Andrikopoulou

Summary, in English

Effectively identifying deviations in real-world medical time-series data is a critical endeavor, essential for early surveillance of disease outbreaks. This paper demonstrates the integration of time-series anomaly detection techniques to develop surveillance systems for disease outbreaks. Utilizing data from Sweden's telephone counseling service (1177), we first illustrate the trends in physical and mental symptoms recorded as contact reasons, offering valuable insights for outbreak detection. Subsequently, an advanced anomaly detection technique is applied incrementally to these time-series symptoms as univariate and multivariate approaches to assess the effectiveness of a machine learning-based method on early detection of the COVID-19 outbreak.

Department/s

  • EPI@LUND
  • Centre for Environmental and Climate Science (CEC)
  • eSSENCE: The e-Science Collaboration
  • LU Profile Area: Natural and Artificial Cognition
  • Division of Occupational and Environmental Medicine, Lund University
  • EpiHealth: Epidemiology for Health

Publishing year

2024-08

Language

English

Pages

1916-1920

Publication/Series

Studies in Health Technology and Informatics

Volume

316

Document type

Conference paper

Publisher

IOS Press

Topic

  • Computer Science

Keywords

  • Anomaly detection
  • Anomaly transformer
  • COVID-19 pandemic
  • Incremental learning
  • Public health surveillance

Conference name

34th Medical Informatics Europe Conference, MIE 2024

Conference date

2024-08-25 - 2024-08-29

Conference place

Athens, Greece

Status

Published

Project

  • Improved preparedness for future pandemics and other health crises through large-scale disease surveillance
  • Pandemic preparedness in the era of big data: Disease surveillance tools using individual-level register data and novel mobility data
  • eSSENCE@LU 10:6 - Pandemic preparedness in the era of big data: Disease surveillance tools using individual-level register data and novel mobility data

Research group

  • EPI@LUND

ISBN/ISSN/Other

  • ISSN: 0926-9630
  • ISSN: 1879-8365
  • ISBN: 9781643685335