Evidence map›Paper›PMID 42389321›Full record

ArticleScience in One Health2026

Surveillance and response to emerging and re-emerging zoonotic diseases: advancing One Health from evidence to action.

Xin-Yu Feng, Roger Frutos, Xing-Quan Zhu, Jordi Serra-Cobo, Xiao-Nong Zhou

Abstract readEditorial
In one paragraph

Article in Science in One Health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Xin-Yu FengSchool of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, One Health Center, Shanghai Jiao Tong University-The University of Edinburgh, Shanghai 200025, China.
Roger FrutosFrench Agricultural Research Centre for International Development (CIRAD), Montpellier 34000, France.
Xing-Quan ZhuCollege of Veterinary Medicine, Shanxi Agriculture University, Taiyuan 030801, Shanxi, China.
Jordi Serra-CoboDepartment of Evolutionary Biology, Ecology and Environmental Sciences, Faculty of Biology, University of Barcelona, Barcelona 08007, Spain.
Xiao-Nong ZhouSchool of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, One Health Center, Shanghai Jiao Tong University-The University of Edinburgh, Shanghai 200025, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Zoonotic diseases pose significant threats to global health security, societal stability, and economic well-being by causing widespread illness, overwhelming healthcare systems, disrupting communities, and driving the majority of emerging infectious diseases. This editorial synthesizes key insights on surveillance and response to zoonotic diseases, arguing that current systems must evolve from fragmented, human-centric detection toward integrated, anticipatory platforms. The collected papers underscore that traditional surveillance fails to address the accelerating drivers of zoonotic emergence, such as climate change, agricultural intensification, and antimicrobial resistance. Instead, they advocate for a paradigm shift wherein genomics, artificial intelligence, and digital tools are leveraged to interpret risk signals across human, animal, and environmental domains-enabling early warning and source attribution. Critically, the issue emphasizes that technological advances alone are insufficient; effective response depends on embedding these tools within robust One Health governance, sustained cross-sectoral collaboration, and equity-focused investments. By framing surveillance as an action-oriented continuum, the contributions collectively assert that preventing future zoonotic crises requires transforming One Health from a conceptual ideal into an operational infrastructure capable of translating data into timely, context-specific interventions.

Indexed as

Artificial intelligenceGenomic epidemiologyOne HealthOutbreak preparednessSurveillanceZoonotic diseases

Identifiers

PMID42389321
PMCPMC13320555

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.