From Reactive to Predictive One Health: AI-Enabled Frameworks for Integrated Zoonotic Surveillance and Governance.

Elena Sorrentino, Alessandra Mazzeo, Celestina Mascolo, Michele Valentino Chiara, Sebastiano Rosati, Lucia Maiuro

Abstract read

In one paragraph

Article in International journal of environmental research and public 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

6 authors.

Elena SorrentinoDepartment of Agricultural, Environmental and Food Sciences (DiAAA), University of Molise, Via Francesco de Sanctis snc, 86100 Campobasso, Italy.ORCID 0000-0002-3570-7642

Alessandra MazzeoDepartment of Agricultural, Environmental and Food Sciences (DiAAA), University of Molise, Via Francesco de Sanctis snc, 86100 Campobasso, Italy.ORCID 0000-0003-3969-1294

Celestina MascoloDepartment of Prevention, Complex Structure Animal Health, Local Health Agency of Caserta, Via Feudo San Martino, 81100 Caserta, Italy.ORCID 0009-0003-7644-461X

Michele Valentino ChiaraSector of Collective Prevention and Public and Veterinary Health Sector, General Directorate for Health Protection and Coordination of the Regional Health System, Centro Direzionale C3, 80132 Naples, Italy.

Sebastiano RosatiDepartment of Agricultural, Environmental and Food Sciences (DiAAA), University of Molise, Via Francesco de Sanctis snc, 86100 Campobasso, Italy.

Lucia MaiuroDepartment of Agricultural, Environmental and Food Sciences (DiAAA), University of Molise, Via Francesco de Sanctis snc, 86100 Campobasso, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The operationalization of the One Health (OH) approach remains a major challenge due to persistent fragmentation across human, animal, and environmental data systems. This gap is exacerbated by climate change, which acts as a risk multiplier for pathogen transmission and agri-food system vulnerability. Drawing on more than a decade of research, including the re-emergence of brucellosis in Italy and the 2024

Indexed as

Artificial IntelligenceOne HealthZoonosesAnimalsDisease OutbreaksHumansartificial intelligencedata integrationdecision-support systemsdisease preventionepidemiological surveillancefood safetyOne Healthpredictive modelingpublic healthzoonoses

Identifiers

PMID42512156

DOI10.3390/ijerph23070850

PMCPMC13411890

What OpenQuestion holds

Textmetadata

LicenceCC BY

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.