ReviewCurrent microbiology2026
Emerging and Re-Emerging Viral Infections in Poultry: Integrating Traditional and AI-Based Control Strategies.
Review in Current microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Poultry production remains significantly challenged by emerging and re-emerging avian viral diseases, which are influenced by host-pathogen interactions, viral evolution, and intensified farming systems. Viruses like avian influenza viruses (AIV) and Newcastle disease virus (NDV) remain the most persistent threats, while chicken anaemia virus (CAV), avian metapneumovirus (aMPV), adenoviruses, and astroviruses are increasingly recognized for their potential to affect poultry health and productivity. Despite the continued use of vaccination, biosecurity, and conventional diagnostics, effective control is often limited by delayed detection and inadequate integration of surveillance data. This review aims to critically examine the limitations of existing control strategies for major avian viral diseases and to evaluate the potential of artificial intelligence (AI) in improving disease surveillance and management. The current knowledge is synthesized on the basis of epidemiology and drivers of key viral infections and how AI-based tools can enhance predictive surveillance, real-time diagnostics, outbreak risk assessment, and vaccine development. The analysis indicates that AI-driven approaches can improve early detection and enable more effective use of heterogeneous data sources across animal, environmental, and public health domains. Integrating these approaches within a One Health framework offers opportunities to strengthen preparedness and response systems. Notwithstanding these benefits many issues regarding use of AI still need to be addressed for successful deployment of AI in diagnosing diseases.
Indexed as
Identifiers
42599520What Socratic 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 Socratic graph.