Forecasting influenza with the long short-term memory model: results from the 2023-2024 influenza season.

Sneha P Cherukuri, Mark L Bova, Shaylee P Mehta, Christian T Bautista

Abstract read

In one paragraph

Article in MSMR, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Epidemiological characteristics and incidence prediction of varicella from 2014 to 2023 in Chongqing, China.Frontiers in public health · 2026
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Sneha P CherukuriArmed Forces Health Surveillance Division, Integrated Biosurveillance Branch, Silver Spring, MD.

Mark L BovaArmed Forces Health Surveillance Division, Integrated Biosurveillance Branch, Silver Spring, MD.

Shaylee P MehtaArmed Forces Health Surveillance Division, Integrated Biosurveillance Branch, Silver Spring, MD.

Christian T BautistaArmed Forces Health Surveillance Division, Integrated Biosurveillance Branch, Silver Spring, MD.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Armed ForcescasesforecastinginfectioninfluenzaLong Short-Term MemoryLSTMmachine learningmilitarymodelUnited States

Identifiers

PMID40305838

PMCPMC12091954

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.