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Related Experiment Videos

Using community syndromic surveillance to anticipate enterovirus-related school class suspensions: A real-time LSTM

Chinmayee Rayguru1, Hong-Lian Jian2, Yi-Fan Peng2

  • 1Center for Environmental Change Research, Academia Sinica, Taipei City, Taiwan.

International Journal of Infectious Diseases : IJID : Official Publication of the International Society for Infectious Diseases
|June 24, 2026
PubMed
Summary

A new early warning system using a long short-term memory (LSTM) model accurately detects enterovirus-like (EV-like) syndrome outbreaks. This system provides timely alerts for schools and families, enabling faster responses to potential epidemics.

Keywords:
Class suspensionEnterovirusLSTMPediatric outpatientSyndromic surveillance

Related Experiment Videos

Area of Science:

  • Epidemiology
  • Machine Learning
  • Public Health Surveillance

Background:

  • Enterovirus-like (EV-like) syndrome poses a significant public health challenge, particularly in school settings.
  • Early detection of EV-like syndrome outbreaks is crucial for timely intervention and prevention of widespread transmission.

Purpose of the Study:

  • To develop and evaluate a district-level early warning framework for EV-like syndrome activity.
  • To utilize a long short-term memory (LSTM) model with adaptive thresholds for enhanced surveillance.
  • To assess the framework's ability to detect disease aberrations earlier than school class suspension events.

Main Methods:

  • Analysis of daily EV-like syndrome cases and school class suspension records at the district level.
  • Forecasting EV-like syndrome activity 14 days in advance using LSTM models with a 30-day lookback window.
  • Employing residual-based anomaly detection with district-specific adaptive thresholds for signal identification.

Main Results:

  • The LSTM model demonstrated high accuracy (0.94 for preschool, 0.96 for primary school) in Taipei City.
  • The framework effectively captured temporal disease activity trends.
  • Alarms were generally generated earlier than abnormal class suspension days, indicating improved early detection.

Conclusions:

  • A district-level LSTM model with adaptive thresholds provides a scalable solution for early EV-like syndrome warning.
  • This framework supports timely local responses and targeted prevention strategies.
  • The system enhances preparedness for schools and families against infectious disease outbreaks.