ReviewThe Lancet. Global health2026
Research priorities for improved pandemic and epidemic intelligence.
Review in The Lancet. Global health, 2026. 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.
- Integrating mathematical modelling and artificial intelligence to combat emerging viral syndemics: A systematic review.New microbes and new infections · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
25 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pandemic and epidemic intelligence integrates surveillance data with contextual knowledge to assess health risks and inform public health decisions. Although investments in surveillance and public health intelligence have accelerated innovation, research efforts remain thematically and geographically fragmented and the translation of insights into policy and practice is inconsistent. To address this gap, the WHO Hub for Pandemic and Epidemic Intelligence, in collaboration with global partners, conducted a research prioritisation exercise to identify key research gaps and opportunities. Using a structured consultation process and expert-driven scoring, based on a modified Child Health and Nutrition Research Initiative approach, we identified 23 priority research statements across eight thematic domains including data preparedness, quality standards, analytical frameworks, artificial intelligence and technological advances, multisectoral approaches, community-centred approaches, governance, and evidence-to-policy translation. These findings can guide funding agencies, policy makers, and research institutions in prioritising research investments that enhance data integration, diagnostic innovation, and real-time decision making.
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.