用移动数据动态建模，提升艾滋病诊断预测精度。
MAT-MPNN: A Mobility-Aware Transformer-MPNN Model for Dynamic Spatiotemporal Prediction of HIV Diagnoses in California, Florida, and New England
- 融合交通与人口数据构建动态空间图，替代固定邻接矩阵。
- 在佛罗里达、加州等地误差降低超12.5%，显著优于基线模型。
- 适合公共卫生研究者和疫情预测团队使用。
艾滋病长期构成全球重大健康挑战，其诊断率的时空预测仍是研究重点。传统消息传递神经网络（MPNN）依赖静态二值邻接矩阵，仅反映地理相邻关系，难以捕捉非相邻县之间的传播关联。本文提出一种融合移动感知的Transformer-MPNN框架（MAT-MPNN），用于预测加利福尼亚州、佛罗里达州及新英格兰地区县级艾滋病诊断率。该模型通过移动图生成器（MGG）结合地理与人口信息，动态构建空间关系；再由Transformer编码器提取时序特征。相比表现最佳的混合基线模型（Transformer MPNN），MAT-MPNN在佛罗里达州、加州和新英格兰地区的均方预测误差（MSPE）分别降低27.9%、39.1%和12.5%，预测模型选择准则（PMCC）分别提升7.7%、3.5%和3.9%。在佛罗里达与新英格兰，其性能超越空间变系数自回归模型（SVAR），加州表现相当。结果表明，引入移动感知的动态空间结构可显著提升流行病学预测的准确性与校准能力。
原文摘要 · Abstract (English)
Human Immunodeficiency Virus (HIV) has posed a major global health challenge for decades, and forecasting HIV diagnoses continues to be a critical area of research. However, capturing the complex spatial and temporal dependencies of HIV transmission remains challenging. Conventional Message Passing Neural Network (MPNN) models rely on a fixed binary adjacency matrix that only encodes geographic adjacency, which is unable to represent interactions between non-contiguous counties. Our study proposes a deep learning architecture Mobility-Aware Transformer-Message Passing Neural Network (MAT-MPNN) framework to predict county-level HIV diagnosis rates across California, Florida, and the New England region. The model combines temporal features extracted by a Transformer encoder with spatial relationships captured through a Mobility Graph Generator (MGG). The MGG improves conventional adjacency matrices by combining geographic and demographic information. Compared with the best-performing hybrid baseline, the Transformer MPNN model, MAT-MPNN reduced the Mean Squared Prediction Error (MSPE) by 27.9% in Florida, 39.1% in California, and 12.5% in New England, and improved the Predictive Model Choice Criterion (PMCC) by 7.7%, 3.5%, and 3.9%, respectively. MAT-MPNN also achieved better results than the Spatially Varying Auto-Regressive (SVAR) model in Florida and New England, with comparable performance in California. These results demonstrate that applying mobility-aware dynamic spatial structures substantially enhances predictive accuracy and calibration in spatiotemporal epidemiological prediction.
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