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Long short-term memory-based forecasting of influenza epidemics using surveillance and meteorological data in Tokyo, Japan

BackgroundInfluenza remains a significant public health challenge worldwide, necessitating robust forecasting models to facilitate timely interventions and resource allocation. The aim of this study was to develop a long short-term memory (LSTM)-based short-term forecasting model to accurately predi...

Ausführliche Beschreibung

Gespeichert in:
Hauptverfasser: Daiki Koge, Keita Wagatsuma
Format: Artigo
Sprache:Inglês
Veröffentlicht: Frontiers Media S.A. 2025-08-01
Schriftenreihe:Frontiers in Public Health
Schlagworte:
influenza
meteorological factor
forecasting
epidemiology
climate change
Japan
Online-Zugang:https://www.frontiersin.org/articles/10.3389/fpubh.2025.1618508/full
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