Abstract: Schistosomiasis is a major public health concern in the world, and precision control is crucial to combating this disease. Due to the complex and diverse transmission route of schistosomiasis, conventional statistical models have significant limitations for precision control of schistosomiasis. As an important branch of artificial intelligence, machine learning has shown remarkable advantages in schistosomiasis control and research. It has been shown that machine learning is highly effective for disease prediction and risk assessment, so as to optimize the disease control strategy and resource allocation and achieve the precision control target. This review summarizes the characteristics of machine learning models and their applications in the research of intermediate host snails and schistosomiasis.

Key words: Schistosomiasis, Machine learning, Artificial intelligence, Application

摘要： 血吸虫病是全球范围内的重大公共卫生问题之一，精准防控是当前应对该疾病的关键。然而，由于血吸虫病传播途径复杂多样，传统统计模型在实现精准防控方面存在明显局限性。机器学习作为人工智能的重要分支，在血吸虫病防控研究中具有显著优势，能更高效地进行疾病预测和风险评估，从而优化防控策略和资源分配，实现精准防控的目标。本文对机器学习模型的特点及其在血吸虫中间宿主螺和血吸虫病研究中的应用进行综述。

关键词: 血吸虫病, 机器学习, 人工智能, 应用