ReviewMicroorganisms2022
Artificial Intelligence Models for Zoonotic Pathogens: A Survey.
Review in Microorganisms, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Harnessing AI for advancing pathogenic microbiology: a bibliometric and topic modeling approach.Frontiers in microbiology · 2024Pooled it
- From Reactive to Predictive One Health: AI-Enabled Frameworks for Integrated Zoonotic Surveillance and Governance.International journal of environmental research and public health · 2026Article
- Spatio-temporal dynamics, trend analysis, and incidence prediction of echinococcosis in Xinjiang, China (2005-2024).BMC public health · 2026Article
- Multi-model large-scale AI framework for avian influenza surveillance and preparedness: Harnessing large language models to enhance risk communication, real-time decision support, and public health response strategies.One health (Amsterdam, Netherlands) · 2026Article
- Spatio-temporal ensemble machine learning forecasts of animal leptospirosis in enzootic Indian States using integrated environmental predictors.Veterinary research communications · 2026Article
- Assessing the Application of a Genomic Network Analysis in Population Ecology: Inferring Patterns of Dispersal and Geographic Structure in the Emerging Pathogen,Ecology and evolution · 2026Article
- Sequence based virus host prediction: a curated dataset and generalizable framework for training artificial intelligence to identify viruses of humans.Virus evolution · 2026Article
- The role of artificial intelligence in detecting avian influenza virus outbreaks: A review.Open veterinary journal · 2025Review
- Genomics and Bioinformatics in One Health: Transdisciplinary Approaches for Health Promotion and Disease Prevention.International journal of environmental research and public health · 2024Article
- Machine learning approaches for influenza A virus risk assessment identifies predictive correlates using ferret model in vivo data.Communications biology · 2024Article
- Significance of Artificial Intelligence in the Study of Virus-Host Cell Interactions.Biomolecules · 2024Review
- Predicting chronic wasting disease in white-tailed deer at the county scale using machine learning.Scientific reports · 2024Article
- One World, One Health: Zoonotic Diseases, Parasitic Diseases, and Infectious Diseases.Healthcare (Basel, Switzerland) · 2024Article
- Analysis of Retrospective Laboratory Data on the Burden of Bacterial Pathogens Isolated at the National Veterinary Research Institute Nigeria, 2018-2021.Veterinary sciences · 2023Article
- Predictive modelling of Ross River virus using climate data in the Darling Downs.Epidemiology and infection · 2023Article
- Editorial: Zoonotic emerging viral infectious diseases.Frontiers in veterinary science · 2023Article
- Innovative applications of artificial intelligence in zoonotic disease management.Science in One Health · 2023Review
- Advances in Microbial Diagnostics: Machine Learning and Nanotechnology for Zoonotic Disease Control.Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Zoonotic diseases or zoonoses are infections due to the natural transmission of pathogens between species (animals and humans). More than 70% of emerging infectious diseases are attributed to animal origin. Artificial Intelligence (AI) models have been used for studying zoonotic pathogens and the factors that contribute to their spread. The aim of this literature survey is to synthesize and analyze machine learning, and deep learning approaches applied to study zoonotic diseases to understand predictive models to help researchers identify the risk factors, and develop mitigation strategies. Based on our survey findings, machine learning and deep learning are commonly used for the prediction of both foodborne and zoonotic pathogens as well as the factors associated with the presence of the pathogens.
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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.