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

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

doi:10.3233/SHTI240807

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

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

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

Abstract

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.

Original language	English
Title of host publication	Digital Health and Informatics Innovations for Sustainable Health Care Systems - Proceedings of MIE 2024
Editors	John Mantas, Arie Hasman, George Demiris, Kaija Saranto, Michael Marschollek, Theodoros N. Arvanitis, Ivana Ognjanovic, Arriel Benis, Parisis Gallos, Emmanouil Zoulias, Elisavet Andrikopoulou
Publisher	IOS Press
Pages	1916-1920
Number of pages	5
ISBN (Electronic)	9781643685335
DOIs	https://doi.org/10.3233/SHTI240807
Publication status	Published - 2024 Aug
Event	34th Medical Informatics Europe Conference, MIE 2024 - Athens, Greece Duration: 2024 Aug 25 → 2024 Aug 29

Publication series

Name	Studies in Health Technology and Informatics
Volume	316
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	34th Medical Informatics Europe Conference, MIE 2024
Country/Territory	Greece
City	Athens
Period	2024/08/25 → 2024/08/29

Subject classification (UKÄ)

Computer Sciences

Free keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.3233/SHTI240807

Cite this

Hashemi, A. S., Ghazani, M. M., Ohlsson, M., Björk, J., & Dietler, D. (2024). Surveillance of Disease Outbreaks Using Unsupervised Uni-Multivariate Anomaly Detection of Time-Series Symptoms. In J. Mantas, A. Hasman, G. Demiris, K. Saranto, M. Marschollek, T. N. Arvanitis, I. Ognjanovic, A. Benis, P. Gallos, E. Zoulias, & E. Andrikopoulou (Eds.), Digital Health and Informatics Innovations for Sustainable Health Care Systems - Proceedings of MIE 2024 (pp. 1916-1920). (Studies in Health Technology and Informatics; Vol. 316). IOS Press. https://doi.org/10.3233/SHTI240807

Hashemi, Atiye Sadat ; Ghazani, Mirfarid Musavian ; Ohlsson, Mattias et al. / Surveillance of Disease Outbreaks Using Unsupervised Uni-Multivariate Anomaly Detection of Time-Series Symptoms. Digital Health and Informatics Innovations for Sustainable Health Care Systems - Proceedings of MIE 2024. editor / John Mantas ; Arie Hasman ; George Demiris ; Kaija Saranto ; Michael Marschollek ; Theodoros N. Arvanitis ; Ivana Ognjanovic ; Arriel Benis ; Parisis Gallos ; Emmanouil Zoulias ; Elisavet Andrikopoulou. IOS Press, 2024. pp. 1916-1920 (Studies in Health Technology and Informatics).

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title = "Surveillance of Disease Outbreaks Using Unsupervised Uni-Multivariate Anomaly Detection of Time-Series Symptoms",

abstract = "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.",

keywords = "Anomaly detection, Anomaly transformer, COVID-19 pandemic, Incremental learning, Public health surveillance",

author = "Hashemi, \{Atiye Sadat\} and Ghazani, \{Mirfarid Musavian\} and Mattias Ohlsson and Jonas Bj{\"o}rk and Dominik Dietler",

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doi = "10.3233/SHTI240807",

language = "English",

series = "Studies in Health Technology and Informatics",

publisher = "IOS Press",

pages = "1916--1920",

editor = "John Mantas and Arie Hasman and George Demiris and Kaija Saranto and Michael Marschollek and Arvanitis, \{Theodoros N.\} and Ivana Ognjanovic and Arriel Benis and Parisis Gallos and Emmanouil Zoulias and Elisavet Andrikopoulou",

booktitle = "Digital Health and Informatics Innovations for Sustainable Health Care Systems - Proceedings of MIE 2024",

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note = "34th Medical Informatics Europe Conference, MIE 2024 ; Conference date: 25-08-2024 Through 29-08-2024",

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Hashemi, AS, Ghazani, MM, Ohlsson, M , Björk, J & Dietler, D 2024, Surveillance of Disease Outbreaks Using Unsupervised Uni-Multivariate Anomaly Detection of Time-Series Symptoms. in J Mantas, A Hasman, G Demiris, K Saranto, M Marschollek, TN Arvanitis, I Ognjanovic, A Benis, P Gallos, E Zoulias & E Andrikopoulou (eds), Digital Health and Informatics Innovations for Sustainable Health Care Systems - Proceedings of MIE 2024. Studies in Health Technology and Informatics, vol. 316, IOS Press, pp. 1916-1920, 34th Medical Informatics Europe Conference, MIE 2024, Athens, Greece, 2024/08/25. https://doi.org/10.3233/SHTI240807

Surveillance of Disease Outbreaks Using Unsupervised Uni-Multivariate Anomaly Detection of Time-Series Symptoms. / Hashemi, Atiye Sadat; Ghazani, Mirfarid Musavian; Ohlsson, Mattias et al.
Digital Health and Informatics Innovations for Sustainable Health Care Systems - Proceedings of MIE 2024. ed. / John Mantas; Arie Hasman; George Demiris; Kaija Saranto; Michael Marschollek; Theodoros N. Arvanitis; Ivana Ognjanovic; Arriel Benis; Parisis Gallos; Emmanouil Zoulias; Elisavet Andrikopoulou. IOS Press, 2024. p. 1916-1920 (Studies in Health Technology and Informatics; Vol. 316).

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

TY - GEN

T1 - Surveillance of Disease Outbreaks Using Unsupervised Uni-Multivariate Anomaly Detection of Time-Series Symptoms

AU - Hashemi, Atiye Sadat

AU - Ghazani, Mirfarid Musavian

AU - Ohlsson, Mattias

AU - Björk, Jonas

AU - Dietler, Dominik

PY - 2024/8

Y1 - 2024/8

N2 - 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.

AB - 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.

KW - Anomaly detection

KW - Anomaly transformer

KW - COVID-19 pandemic

KW - Incremental learning

KW - Public health surveillance

UR - https://www.scopus.com/pages/publications/85202005899

U2 - 10.3233/SHTI240807

DO - 10.3233/SHTI240807

M3 - Paper in conference proceeding

C2 - 39176866

AN - SCOPUS:85202005899

T3 - Studies in Health Technology and Informatics

SP - 1916

EP - 1920

BT - Digital Health and Informatics Innovations for Sustainable Health Care Systems - Proceedings of MIE 2024

A2 - Mantas, John

A2 - Hasman, Arie

A2 - Demiris, George

A2 - Saranto, Kaija

A2 - Marschollek, Michael

A2 - Arvanitis, Theodoros N.

A2 - Ognjanovic, Ivana

A2 - Benis, Arriel

A2 - Gallos, Parisis

A2 - Zoulias, Emmanouil

A2 - Andrikopoulou, Elisavet

PB - IOS Press

T2 - 34th Medical Informatics Europe Conference, MIE 2024

Y2 - 25 August 2024 through 29 August 2024

ER -

Hashemi AS, Ghazani MM, Ohlsson M , Björk J , Dietler D. Surveillance of Disease Outbreaks Using Unsupervised Uni-Multivariate Anomaly Detection of Time-Series Symptoms. In Mantas J, Hasman A, Demiris G, Saranto K, Marschollek M, Arvanitis TN, Ognjanovic I, Benis A, Gallos P, Zoulias E, Andrikopoulou E, editors, Digital Health and Informatics Innovations for Sustainable Health Care Systems - Proceedings of MIE 2024. IOS Press. 2024. p. 1916-1920. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI240807

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

Abstract

Publication series

Conference

Subject classification (UKÄ)

Free keywords

UN SDGs

Access to Document

Other files and links

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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

PREPARE: Improved preparedness for future pandemics and other health crises through large-scale disease surveillance

Cite this

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

Abstract

Publication series

Conference

Subject classification (UKÄ)

Free keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Projects

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

PREPARE: Improved preparedness for future pandemics and other health crises through large-scale disease surveillance

Cite this