ArticleBMC proceedings2026
Leveraging AI for infectious disease modelling and public health decision making.
Article in BMC proceedings, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is expanding the capacity of public health systems to detect infectious disease signals, forecast outbreaks, analyse pathogen evolution, generate localised risk estimates, and support operational decisions. The thirteenth session of the WHO Pandemic and Epidemic Intelligence Innovation Forum brought together experts from academic, public health, and technology organisations to examine current applications of AI in infectious disease modelling and pandemic preparedness. Examples included genomic surveillance, hybrid epidemiological and machine-learning models, spatial foundation models, AI-enabled decision support, and agent-based simulations for resource allocation. Participants emphasised that technical performance alone is insufficient: tools must be transparent, auditable, transferable across settings, operationally usable, and responsive to local data and infrastructure constraints. Sustained interdisciplinary collaboration, human oversight, robust validation, and equitable design will be essential to embed AI safely and effectively within public health decision-making.
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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.