用移动数据和图神经网络预测新冠每日病例，效果优于传统模型。
Leveraging graph neural networks and mobility data for COVID-19 forecasting
- 通过稀疏化输入图结构提升预测稳定性
- 在巴西和中国数据上，图神经网络比LSTM误差更低
- 适合关注疫情波动预测的研究者和公共卫生决策者
新冠疫情已造成数百万人死亡，推动了多种预测模型的发展。本文探讨复杂时空架构与简单时间基线的实用性争议。基于巴西和中国的人员流动数据，研究发现输入图的结构稀疏性和时间粒度是图神经网络（GNN）有效性的决定因素。当预测平滑单调的累计趋势时，标准LSTM已足够；但面对波动剧烈的每日新增病例，GNN显著优于基线模型。通过提取骨干结构，可移除次要连接，大幅提升预测稳定性并降低误差。结果显示，纳入空间依赖关系对建模复杂动态至关重要。具体而言，在巴西和中国数据集上，GCRN和GCLSTM等GNN架构在每日病例预测中均显著优于LSTM（Nemenyi检验，p < 0.05）。最后，将问题转化为二分类任务，以分析上下文窗口大小与预测时长之间的依赖关系。
原文摘要 · Abstract (English)
The COVID-19 pandemic has claimed millions of lives, spurring the development of diverse forecasting models. In this context, the true utility of complex spatio-temporal architectures versus simpler temporal baselines remains a subject of debate. Here, we show that structural sparsification of the input graph and temporal granularity are determining factors for the effectiveness of Graph Neural Networks (GNNs). By leveraging human mobility networks in Brazil and China, we address a conflicting scenario in the literature: while standard LSTMs suffice for smooth, monotonic cumulative trends, GNNs significantly outperform baselines when forecasting volatile daily case counts. We show that backbone extraction substantially enhances predictive stability and reduces predictive error by removing negligible connections. Our results indicate that incorporating spatial dependencies is essential for modeling complex dynamics. Specifically, GNN architectures such as GCRN and GCLSTM outperform the LSTM baseline (Nemenyi test, p < 0.05) on datasets from Brazil and China for daily case predictions. Lastly, we frame the problem as a binary classification task to better analyze the dependency between context sizes and prediction horizons.
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