ArticleScientific reports2025
A systems approach for anticipating post-pandemic forecast challenges for pediatric influenza and hospital burden.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Temporal association between influenza A and B positivity and fever-clinic visit burden in Shanghai, China, 2022-2025: a retrospective time-series study.Frontiers in public health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The COVID-19 pandemic has significantly disrupted influenza forecasting, making it challenging for hospitals to anticipate the severity of upcoming flu seasons relative to typical annual respiratory virus patterns. Even for influenza, health facilities often lack precise information on potential influenza surges, which hinders hospital management's ability to anticipate necessary changes with respect to hospital staffing and resource allocation for an influx of patients. This study addresses this critical gap by developing an enhanced predictive model for pediatric influenza hospitalizations in Massachusetts. By integrating data from the Health and Human Services (HHS) Protect Public Data Hub, Centers for Disease Control and Prevention (CDC) FluSurv-NET, U.S. Department of Transportation (DOT) mobility data, and regional hospitalization rates, we demonstrate how recent improvements in data analytics and population tracking can amplify disease forecasts, and more precisely anticipate hospital burden relative to historical patient intake and hospital utilization trends.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.