Wang Xiaoyi, Jin Zhen
Complex Systems Research Center, Shanxi University, Taiyuan, Shanxi, China.
Key Laboratory of Complex Systems and Data Science of Ministry of Education, Shanxi University, Taiyuan, Shanxi, China.
PLoS Comput Biol. 2025 Jan 9;21(1):e1012738. doi: 10.1371/journal.pcbi.1012738. eCollection 2025 Jan.
Human mobility between different regions is a major factor in large-scale outbreaks of infectious diseases. Deep learning models incorporating infectious disease transmission dynamics for predicting the spread of multi-regional outbreaks due to human mobility have become a hot research topic. In this study, we incorporate the Graph Transformer Neural Network and graph learning mechanisms into a metapopulation SIR model to build a hybrid framework, Metapopulation Graph Transformer Neural Network (M-Graphormer), for high-dimensional parameter estimation and multi-regional epidemic prediction. The framework effectively solves the problem that existing models may lose some hidden spatial dependencies in the data when dealing with the dynamic graph structure of the network due to human mobility. We performed multi-wave infectious disease prediction in multiple regions based on real epidemic data. The results show that the framework is capable of performing high-dimensional parameter estimation and accurately predicting epidemic transmission dynamics in multiple regions even with low data quality. In addition, we retrospectively extrapolate the temporal evolution patterns of contact rate under different interventions implemented in different regions, reflecting the dynamics of intervention intensity and the need for flexibility in adjusting interventions in different regions. To provide early warning of infectious disease transmission, we retrospectively predicted the arrival time of infectious diseases using data from the early stages of outbreaks.
不同地区之间的人员流动是传染病大规模爆发的一个主要因素。结合传染病传播动力学的深度学习模型，用于预测因人员流动导致的多区域疫情传播，已成为一个热门研究课题。在本研究中，我们将图变换器神经网络和图学习机制纳入一个集合种群SIR模型，以构建一个混合框架——集合种群图变换器神经网络（M-Graphormer），用于高维参数估计和多区域疫情预测。该框架有效解决了现有模型在处理因人员流动导致的网络动态图结构时，可能会丢失数据中一些隐藏空间依赖性的问题。我们基于真实疫情数据在多个区域进行了多波传染病预测。结果表明，即使在数据质量较低的情况下，该框架也能够进行高维参数估计，并准确预测多个区域的疫情传播动态。此外，我们回顾性地推断了不同区域实施不同干预措施下接触率的时间演变模式，反映了干预强度的动态变化以及不同区域调整干预措施灵活性的必要性。为了提供传染病传播的早期预警，我们利用疫情早期阶段的数据回顾性地预测了传染病的到达时间。
