ReviewEcoHealth2025
Advancing AMR Surveillance: Confluence of One Health and Big Data Integration : Converging One Health and Big Data for AMR.
Review in EcoHealth, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Global trends in carbapenem-resistant gram-negative bacteria research (2020-2025): a bibliometric analysis and systematic review.Frontiers in cellular and infection microbiology · 2025Pooled it
- Mapping the Convergence of Frontier Technologies for Major Environmental Challenges: A Chemical and Molecular Perspective on the Use of AI for Climate Action and Antimicrobial Resistance.Molecules (Basel, Switzerland) · 2026Review
- Artificial intelligence for early detection and risk prediction of antimicrobial resistance in aquatic ecosystems.npj antimicrobials and resistance · 2026Review
- Building capacities in Sub-Saharan African countries for antimicrobial resistance surveillance in the food and agriculture sectors using the FAO ATLASS tool.Frontiers in veterinary science · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antibiotic resistance poses a critical global health threat, demanding robust surveillance systems to monitor its prevalence, patterns, and trends. The One Health approach has emerged as a comprehensive framework, emphasizing the interconnectedness of human health, animal health, and the environment in addressing this complex issue. This article explores the potential of One Health-based antibiotic resistance surveillance, integrating big data analytics and interdisciplinary collaboration. Challenges and opportunities in harmonizing surveillance efforts across sectors are discussed, underscoring the importance of data sharing and standardization. Advanced technologies like genomics and metagenomics are examined for understanding the genetic basis of antibiotic resistance and tracking its spread. The article also highlights the potential of real-time monitoring and early warning systems to inform evidence-based policies and antimicrobial stewardship programs. By analyzing the state-of-the-art in antibiotic resistance surveillance, this article sheds light on the transformative potential of One Health approaches, leveraging big data and interdisciplinary collaboration to combat antibiotic resistance effectively. The urgency of adopting a united global effort to safeguard the efficacy of antibiotics for future generations is emphasized.
Indexed as
Identifiers
40522529What 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.