ArticleBMC proceedings2025
Data integration and synthesis for pandemic and epidemic intelligence.
Article in BMC proceedings, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Perspective on Better Access to Data and Data Integration in Pharmacovigilance: Information from a Focus Group.Drug safety · 2026Article
- Connecting the Dots: Systems Thinking Highlights the Role of the Environment Sector in One Health Operationalization.Global challenges (Hoboken, NJ) · 2025Article
- Assessing the correlation between STAR health risk forecasts with actual health emergencies in West and Central Africa: a three-year analysis.Frontiers in public health · 2025Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
The COVID-19 pandemic highlighted substantial obstacles in real-time data generation and management needed for clinical research and epidemiological analysis. Three years after the pandemic, reflection on the difficulties of data integration offers potential to improve emergency preparedness. The fourth session of the WHO Pandemic and Epidemic Intelligence Forum sought to report the experiences of key global institutions in data integration and synthesis, with the aim of identifying solutions for effective integration. Data integration, defined as the combination of heterogeneous sources into a cohesive system, allows for combining epidemiological data with contextual elements such as socioeconomic determinants to create a more complete picture of disease patterns. The approach is critical for predicting outbreaks, determining disease burden, and evaluating interventions. The use of contextual information improves real-time intelligence and risk assessments, allowing for faster outbreak responses. This report captures the growing acknowledgment of data integration importance in boosting public health intelligence and readiness and show examples of how global institutions are strengthening initiatives to respond to this need. However, obstacles persist, including interoperability, data standardization, and ethical considerations. The success of future data integration efforts will be determined by the development of a common technical and legal framework, the promotion of global collaboration, and the protection of sensitive data. Ultimately, effective data integration can potentially transform public health intelligence and our way to successfully respond to future pandemics.
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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.