International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2026
Using community syndromic surveillance to anticipate enterovirus-related school class suspensions: A real-time LSTM approach.
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Rayguru Chinmayee, Jian Hong-Lian, Peng Yi-Fan, Chen Kevin J, Chiang Po-Huang, Chan Ta-Chien
Аннотация
OBJECTIVES: This study aimed to develop a district-level early warning framework using a long short-term memory (LSTM) model with adaptive thresholds to detect abnormal enterovirus-like (EV-like) syndrome activity from clinic-based surveillance data and assess its ability to identify aberrations earlier than the onset of class suspension events in Taipei.
METHODS: Daily counts of EV-like syndrome cases and preschool and primary school class suspension records (2022-2025) were analyzed at the district level. LSTM models forecasted EV-like syndrome activity 14 days in advance using a 30-day lookback window. Abnormal signals were identified using residual-based anomaly detection with district-specific adaptive thresholds. The model performance was evaluated using mean absolute error, root mean square error, sensitivity, specificity, and precision.
RESULTS: The LSTM model achieved accuracies of 0.94 for preschool data and 0.96 for primary school data in Taipei City, with consistent performance across districts. The proposed LSTM-based framework effectively captured temporal changes in disease activity and generated alarms generally earlier than abnormal class suspension days.
CONCLUSION: A district-level LSTM model with adaptive threshold detection offers a scalable approach for early warning of EV-like syndrome activity, supporting timely local responses, targeted prevention strategies, and improved preparedness for schools and families.
METHODS: Daily counts of EV-like syndrome cases and preschool and primary school class suspension records (2022-2025) were analyzed at the district level. LSTM models forecasted EV-like syndrome activity 14 days in advance using a 30-day lookback window. Abnormal signals were identified using residual-based anomaly detection with district-specific adaptive thresholds. The model performance was evaluated using mean absolute error, root mean square error, sensitivity, specificity, and precision.
RESULTS: The LSTM model achieved accuracies of 0.94 for preschool data and 0.96 for primary school data in Taipei City, with consistent performance across districts. The proposed LSTM-based framework effectively captured temporal changes in disease activity and generated alarms generally earlier than abnormal class suspension days.
CONCLUSION: A district-level LSTM model with adaptive threshold detection offers a scalable approach for early warning of EV-like syndrome activity, supporting timely local responses, targeted prevention strategies, and improved preparedness for schools and families.