Evidence map›Paper›PMID 42571518›Full record

ArticleInfectious Disease Modelling2027

Stage-aware deep learning with adversarial data synthesis enables prospective season-scale A/H1N1 influenza forecasting in China.

Lirong Zhang, Haocheng Zhou, Yuxin Zhu, Panpan Ma, Jiaxin Yuan, Yunxiao Qiu, Huayu Qiao, Jiaxiong Zheng, Suyang Liu, Liping Li and 1 more

Abstract read
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In one paragraph

Article in Infectious Disease Modelling, 2027. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Lirong ZhangShantou University Medical College, Shantou, 515041, PR China.
Haocheng ZhouShantou University Medical College, Shantou, 515041, PR China.
Yuxin ZhuShantou University Medical College, Shantou, 515041, PR China.
Panpan MaShantou University Medical College, Shantou, 515041, PR China.
Jiaxin YuanShantou University Medical College, Shantou, 515041, PR China.
Yunxiao QiuShantou University Medical College, Shantou, 515041, PR China.
Huayu QiaoShantou University Medical College, Shantou, 515041, PR China.
Jiaxiong ZhengShantou University Medical College, Shantou, 515041, PR China.
Suyang LiuShantou University Medical College, Shantou, 515041, PR China.
Liping LiShantou University Medical College, Shantou, 515041, PR China.
Zicheng CaoShantou University Medical College, Shantou, 515041, PR China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate influenza forecasting is essential for public health preparedness, yet many models require future covariates, provide limited interpretability, and degrade under post-pandemic regime shifts. We propose a Stage-Aware Multimodal Neural Network (SAMNN) that integrates multimodal temporal features using configurations adapted to heterogeneous transmission regimes and supports season-ahead scenario forecasting using adversarially synthesized covariates. Using 13 years of national A/H1N1 surveillance data from mainland China (2011∼2024), SAMNN was evaluated across three epidemiologically distinct seasons spanning ∼20-fold differences in peak intensity. SAMNN achieved

Indexed as

COVID-19 impactInfluenza forecastingMultimodal neural networkStage-aware learningSurveillance

Identifiers

PMID42571518
PMCPMC13452303

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.