ArticleFrontiers in public health2026
Protocol-aware epidemic forecasting across heterogeneous public health surveillance systems.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- γ-Tocotrienol Sensitises Colorectal Cancer to PD-1 Blockade by Enhancing MHC-I-Associated Tumour Immune Visibility and CD8Biomolecules · 2026Article
- Bidirectional cross-modal fusion with tensor interaction for drug-target binding prediction.Scientific reports · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Public-health forecasting is central to epidemic intelligence and operational decision support. In practice, surveillance data are affected by reporting delays, revisions, and backfill, as well as abrupt regime shifts, which often reduce model reliability across regions and systems. Methods: We developed EpiMap-LLM, a protocol-aware forecasting approach that links epidemic dynamics with surveillance context using a frozen language-model backbone and lightweight trainable components. Results: Across daily and weekly surveillance settings (JHU CSSE COVID-19 and CDC influenza hospitalization surveillance), EpiMap-LLM consistently improves MAE and RMSE over strong forecasting baselines. Conclusion: Protocol-aware forecasting improves robustness and practical usefulness for surveillance dashboards, early warning, and public-health decision support in heterogeneous reporting systems.
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.