ReviewNature reviews. Microbiology2026
Viral emergence and pandemic preparedness in a One Health framework.
Review in Nature reviews. Microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed.
- Airway organoids reveal patterns of influenza A tropism and adaptation in wildlife species.Emerging microbes & infections · 2026Article
- Unannounced Drills Using Patient Actors to Evaluate Health Care Facility Readiness for Infectious Disease Outbreaks - New Jersey, New York, and U.S. Virgin Islands, January-June 2026.MMWR. Morbidity and mortality weekly report · 2026Article
- Convergent receptor use at the coronavirus One Health interface.Nature microbiology · 2026Article
- Article
- Andes hantavirus in South America: Emerging epidemiological trends, regional challenges, and lessons beyond the MV Hondius outbreak.New microbes and new infections · 2026Article
- Andes Virus on a Cruise Ship, what it Tells us About the Global Pandemic Preparedness Agenda.The Lancet regional health. Europe · 2026Review
- Pathogens Associated with Domestic Cats (Pathogens (Basel, Switzerland) · 2026Review
- Targeting Zoonotic Spillover Drivers for Global Pandemic Prevention: A Narrative Review.Microorganisms · 2026Review
- Climate change and infectious diseases: translating evidence into action.Infectious diseases of poverty · 2026Article
- Article
- Case-based Learning and Artificial Intelligence-based Gamification to Improve Undergraduate Students' Motivation for One Health and Climate Change: A Pilot Study.Medical science educator · 2026Article
- Integrating explainable AI and One Health: a new frontier in combating infectious diseases.EBioMedicine · 2026Review
- Article
- Metaviromics reveals a high diversity of viruses belonging to theVirus evolution · 2026Article
- Exploring integrated advice and avian influenza knowledge gaps by using a Highly Pathogenic Avian Influenza outbreak simulation.PloS one · 2026Article
- Artificial intelligence directed computational protein design: lessons from COVID-19 for pandemic-ready vaccines and antibody therapeutics.Journal of pharmacy & pharmaceutical sciences : a publication of the Canadian Society for Pharmaceutical Sciences, Societe canadienne des sciences pharmaceutiques · 2026Review
- Strengthening pandemic preparedness in Peru: a qualitative study of high-level leaders involved in the COVID-19 response.BMJ public health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The risk of viral pathogen transmission between humans and animals (spillover events) and subsequent spread has been increasing due to human impacts on the planet, which lead to changes in the interactions between humans, animals, ecosystems and their pathogens. Key factors (drivers) that increase the risk of disease emergence include climate change, urbanization, land-use changes and global travel, all of which can alter human-animal-environment interactions and increase the likelihood of zoonotic spillovers and vector-borne diseases. Incorporating data on these drivers (such as ecological shifts and patterns of animal movement) into disease surveillance systems can help identify hot spots for disease emergence, which could in theory enable earlier detection of outbreaks and, in turn, increase the effectiveness of intervention strategies. A One Health approach, emphasizing the interconnectedness of human, animal and environmental health, is advocated for addressing these complex challenges. Although conceptually clear and widely endorsed, implementation of One Health approaches towards primary prevention of spillovers is extremely challenging. Here, we summarize current knowledge on disease emergence and its drivers, and discuss how this knowledge could be used towards primary prevention and for the development of risk-targeted One Health early warning surveillance. We consider integrating innovative tools for diagnostics, surveillance and virus characterization, and propose an outlook towards more integrated prevention, early warning and control of emerging infections at the human-animal interface.
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Identifiers
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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.