A machine learning-driven early warning system for cryptocaryoniasis in marine aquaculture
This study establishes a robust, scalable framework for real-time disease forecasting in marine aquaculture, adaptable to other aquatic pathogen-host species to support parasite surveillance and precision health management across diverse global aquaculture systems. While further validation with larger datasets and integration of pathogen and host data will enhance future models, this system provides a flexible foundation for advancing disease control in aquatic environments.
Authors
- Fei Yin (ORCID: https://orcid.org/0000-0002-0539-4876)
- Kurt Buchmann (ORCID: https://orcid.org/0000-0002-2747-237X)
- Jorge Galindo‐Villegas (ORCID: https://orcid.org/0000-0002-4250-2477)
- Xiao Xie (ORCID: https://orcid.org/0000-0002-5241-6900)
- Yunyan Jiang
- Suming Zhou (ORCID: https://orcid.org/0000-0002-6519-5094)
- Bo Zhang (ORCID: https://orcid.org/0000-0002-5313-0816)
- Xingyu Wang (ORCID: https://orcid.org/0000-0002-0771-2604)
- Yun Li (ORCID: https://orcid.org/0000-0003-1753-7317)
Institutions
- Ningbo University (CN)
- University of Copenhagen (DK)
- Nord University (NO)
Publication Details
- Journal
- Parasites & Vectors
- Published
- 2025-11-26
- DOI
- https://doi.org/10.1186/s13071-025-07124-z
- Citations
- 2
- Primary Topic
- Aquaculture disease management and microbiota
- Type
- article
- Field-Weighted Citation Impact
- 1.59
Funders
- Ningbo University