This paper provides a comprehensive survey of recent advancements in leveraging machine learning techniques, particularly Transformer models, for predicting human mobility patterns during epidemics. Understanding how people move during epidemics is essential for modeling the spread of diseases and devising effective response strategies. Forecasting population movement is crucial for informing epidemiological models and facilitating effective response planning in public health emergencies. Predicting mobility patterns can enable authorities to better anticipate the geographical and temporal spread of diseases, allocate resources more efficiently, and implement targeted interventions. We review a range of approaches utilizing both pretrained language models like BERT and Large Language Models (LLMs) tailored specifically for mobility prediction tasks. These models have demonstrated significant potential in capturing complex spatio-temporal dependencies and contextual patterns in textual data.

翻译：本文系统综述了在流行病期间利用机器学习技术（尤其是Transformer模型）预测人类移动模式的最新进展。理解疫情中人群的移动模式对于疾病传播建模和制定有效应对策略至关重要。预测人口流动对公共卫生紧急事件中的流行病学模型构建及响应规划具有关键意义。通过预测移动模式，当局能更好地预判疾病时空传播路径、优化资源配置并实施精准干预。我们回顾了多种利用预训练语言模型（如BERT）和专为移动预测任务定制的大语言模型（LLMs）的方法。这些模型在捕捉文本数据中复杂的时空依赖关系和上下文模式方面展现出显著潜力。