Introduction: Disease outbreaks, particularly cryptocaryoniasis caused by the ciliate Cryptocaryon irritans, pose significant barriers to sustainable marine fish aquaculture, undermining productivity, profitability, and biosecurity. Despite its impact, early warning tools for parasitic disease tools leveraging advanced technologies remain underdeveloped.
Material and Methods: We developed a machine learning (ML)-driven early warning system for cryptocaryoniasis, integrating seven years of outbreak surveillance data (n=429 events from 2016–2023) with 17 high-resolution oceanographic and environmental predictors selected for their influence on parasite life cycles across coastal China. Five supervised ML models: logistic regression (LR), support vector machine (SVM), random forest (RF), XGBoost (XGB), and artificial neural network (ANN), were trained using cross-validation and benchmarked in commercial open-sea cages and recirculating aquaculture systems (RAS).
Results: The RF model achieved the highest sensitivity (98.6%), with RF and XGB excelling over (F1 scores: 0.93 and 0.938, respectively), identifying stocking density, water temperature, salinity, pH, and novel predictors like silicate and nitrate as key risk factors. The resulting predictive engine was deployed as a web-based platform, freely accessible online via open-source environmental data, delivering weekly, spatially resolved outbreak forecasts. Field validation, across 12 open-sea cage events and weekly RAS monitoring, confirmed strong predictive accuracy (91.67% in sea cages; 87.5% in RAS), revealing seasonal and latitudinal trends in disease dynamics.
Conclusion: 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.
Keywords: Aquaculture diseases; Cryptocaryon irritans; Disease prediction; Epidemiology; Machine learning models; Parasitology; Sustainability
Funding: Ningbo International Science and Technology Cooperation Project (Grant No. 2023H015); the 2023 Zhejiang Province Project (Grant No. 2023SNJF071); Ningbo Welfare Project (Grant No. 2024S142). The Norwegian Agency for Shared Services in Education and Research provided support through Nord University.