ReviewLancet regional health. Americas2026
Modernizing public health surveillance for global health security leveraging AI.
Review in Lancet regional health. Americas, 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
32 authors.
Funding
Abstract
An electronic public health surveillance (e-PHS) embracing One Health and participatory approaches will collect and analyze data at the human-animal-environment interface to enhance real-time information for the prevention and control of public health emergencies (PHE) such as infectious disease outbreaks. Yet full implementation is suboptimal worldwide. Leveraging the capabilities of emerging digital technologies legally and ethically, we described the scope, added benefits, and applicability of a novel cloud-based, artificial intelligence-enabled One Health Integrated Disease Surveillance and Response health information system (AI-OneHIS) data infrastructure for modernizing the existing traditional PHS models. This multifaceted innovation will ensure faster data capture, seamless interoperability of fragmented HIS, and precise decision support, while preserving their structures, functionalities, and capabilities for routine operations and data sovereignty. This should enable the prevention, timely detection, and effective response to PHE for improved health outcomes if implemented with fidelity on a strong governance-collaboration-informatics-analytics framework.
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.