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

ABSTRACT:

Background

Influenza 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 predict weekly influenza case counts in Tokyo, Japan.

Method

By using weekly time-series data on influenza incidence in Tokyo from 2000 to 2019, along with meteorological variables, we developed four distinct models to evaluate the impact of the external variables of mean temperature, relative humidity, and national public holidays. After model training, we assessed the predictive performance on an independent test dataset, using mean square error (MSE), root mean square error (RMSE),

SUBMITTER: Koge D 

PROVIDER: S-EPMC12411156 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature