Computational Molecular Biology 2025, Vol.15, No.6, 291-298 http://bioscipublisher.com/index.php/cmb 296 6.2 Model generalization capabilities and overfitting risks Even if the data is sufficient, it is no easy task for the model to maintain stable performance across different regions, different host species, and even constantly changing pathogen strains. Especially on small-scale or imbalanced datasets, the model tends to remember the training samples easily. As a result, it performs averagingly in new scenarios, and the problem of overfitting is exposed. Infectious diseases are inherently dynamic and changing. Pathogens evolve rapidly, and environmental conditions may also change over time. All these require models to have a certain degree of adaptability. To avoid the model "failing" during application, means such as richer high-quality data, diverse validation methods and regularization are particularly crucial (Meslamani et al., 2024). 6.3 Ethical, privacy, and regulatory issues in cross-sectoral data integration Cross-departmental data integration sounds very attractive, but when it comes to actually promoting it, various ethical and privacy concerns often arise first. Especially for sensitive data involving genomes or locations, the fields of animal health, environmental monitoring, and human public health themselves have different requirements and restrictions. Moreover, due to the significant differences in regulatory systems among countries and institutions, it is not easy to formulate a unified data usage agreement. To ensure smoother cooperation, transparency, data protection measures and ethical norms must all be clearly defined; otherwise, trust will be difficult to establish. All these indicate that for the rational use of artificial intelligence in animal disease surveillance, a clear and enforceable governance framework is indispensable, while also ensuring that privacy and ethical principles are truly implemented (Guo et al., 2023). 7 Future Directions and Application Prospects Before discussing the future, many researchers have actually realized that relying solely on data from a single country or institution is difficult to support high-quality animal disease predictions. Therefore, a more practical approach would be to first place genomic information, environmental records, and epidemiological data from different regions on a shared platform, and then consider how to train the model. Once such a global collaboration framework is put into operation, real-time data intercommunication and joint model updates will no longer be difficult problems, and it can also alleviate the current old issues such as data being stored separately and significant regional differences. By leveraging international technological and experience exchanges, the speed and accuracy of prediction results will be more guaranteed, and it will be easier to form a consistent response to sudden risks. Moreover, this platform enables the model to continuously absorb new changes, whether it is pathogen mutations or fluctuations in the ecological environment, and adapt more quickly, ultimately making global animal health monitoring more stable. As for the combination of artificial intelligence and edge computing, although it sounds highly technical, in practical scenarios, its significance lies more in "on-site processing". For instance, at the farm or community level, devices can directly process genomic or environmental data without having to upload it layer by layer to the cloud and wait for results. This will significantly reduce latency and decrease reliance on remote servers. Especially when the epidemic just emerged and many situations were still unclear, local rapid judgment was often more crucial than high computing power. In this way, risk assessment can be closer to the scene, safety management measures can be implemented more quickly, and farmers and local administrative departments can obtain more direct and immediately usable information, thereby improving response speed and resource utilization efficiency. Another trend in the future application of artificial intelligence is to attempt to consider cross-species disease prediction, vaccine development, and policy planning under the same train of thought. As the genomic data of animals and humans can already be mutually verified, AI has the opportunity to identify the potential risks of certain zoonotic diseases in advance and provide references for the selection of vaccine targets, thus making the formulation of protective measures less lagging behind. If this predictive ability is further combined with the policy framework, the decision-making process can be more flexible, more evidence-based, and more conducive to the rational allocation of resources. This approach of integrating veterinary science, public health and regulatory systems is actually more in line with the concept of "One Health" and can better help society deal with those complex and rapidly changing epidemic threats.
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