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Foto på Mattias Ohlsson

Mattias Ohlsson

Professor

Foto på Mattias Ohlsson

Time series anomaly detection in helpline call trends for early detection of COVID-19 spread across Sweden, 2020

Författare

  • Atiye Sadat Hashemi
  • Dominik Dietler
  • Tove Fall
  • Malin Inghammar
  • Anders F Johansson
  • Carl Bonander
  • Mattias Ohlsson
  • Jonas Björk

Summary, in English

Timely detection and surveillance of disease community spread is a potent tool for implementing effective public health interventions. This study investigates the National Telehealth Service (1177 helpline) across 18 regions in Sweden in 2020 to identify early signals of community transmission of COVID-19 at the beginning of the pandemic. Focusing on calls related to key COVID-19 symptoms (cough, fever, and breathing difficulties in adults), we analyze their frequency and distribution across referral categories, comparing them to 2019 data. We employ an explainable time series anomaly detection algorithm using daily call data to identify the first collective anomalies across regions. The results show that anomalies in call data were correlated with, but preceded, the first confirmed case infected in Sweden by a median of 7 days (IQR: 2.5–10.5) and the first hospitalized case infected in Sweden by a median of 13 days (IQR: 7.25–16). They also preceded the estimated onset of community spread, indicated by the absolute confirmed cases (median: 24.5, IQR: 18.25-32.5), and severe outcomes defined by hospitalizations (median: 33, IQR: 27.25-44). These findings showcase how helpline call monitoring, using time series anomaly detection, can aid early outbreak detection.

Avdelning/ar

  • Epidemiologi och befolkningsstudier (EPI@Lund)
  • EpiHealth: Epidemiology for Health
  • eSSENCE: The e-Science Collaboration
  • Infektionsmedicin
  • Infect@LU
  • Beräkningsvetenskap för hälsa och miljö
  • LU profilområde: Naturlig och artificiell kognition
  • Artificiell intelligens och thoraxkirurgisk vetenskap (AICTS)
  • LU profilområde: Proaktivt åldrande

Publiceringsår

2025-12

Språk

Engelska

Publikation/Tidskrift/Serie

Scientific Reports

Volym

15

Avvikelse

1

Dokumenttyp

Artikel i tidskrift

Förlag

Nature Publishing Group

Ämne

  • Infectious Medicine

Nyckelord

  • Humans
  • Sweden/epidemiology
  • COVID-19/epidemiology
  • Early Diagnosis
  • SARS-CoV-2/isolation & purification
  • Telemedicine
  • Hotlines
  • Adult
  • Algorithms
  • Pandemics

Aktiv

Published

Projekt

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

Forskningsgrupp

  • Epidemiology and population studies (EPI@Lund)
  • Computational Science for Health and Environment
  • Artificial Intelligence in CardioThoracic Sciences (AICTS)

ISBN/ISSN/Övrigt

  • ISSN: 2045-2322