Forecasting influenza with the long short-term memory model: results from the 2023-2024 influenza season.
Year: 2025
Type: article
Abstract: This report assesses the performance of the long short-term memory (LSTM) model, a machine-learning method with potential to improve forecasting accuracy for respiratory disease surveillance, for possible inclusion in future U.S. Department of Defense influenza forecasting analyses. LSTM is a recurrent neural network model that can be used in almost all modeling fields. The LSTM model had the lowest median log-transformed weighted interval score (WIS) for all forecasting horizons: 1 week (0.3), 2 weeks (0.4), and combined 1-2 weeks (0.4). Further research is recommended to determine the performance of the LSTM model for other respiratory infections, including COVID-19.
Source: PubMed
Authors: Sneha P Cherukuri, Mark L Bova, Shaylee P Mehta, Christian T Bautista
Institution: Armed Forces Health Surveillance Center
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4410019113