During the COVID-19 pandemic, a major driver of new surges has been the emergence of new variants. When a new variant emerges in one or more countries, other nations monitor its spread in preparation for its potential arrival. The impact of the variant and the timing of epidemic peaks in a country highly depend on when the variant arrives. The current methods for predicting the spread of new variants rely on statistical modeling, however, these methods work only when the new variant has already arrived in the region of interest and has a significant prevalence. The question arises: Can we predict when (and if) a variant that exists elsewhere will arrive in a given country and reach a certain prevalence? We propose a variant-dynamics-informed Graph Neural Network (GNN) approach. First, We derive the dynamics of variant prevalence across pairs of regions (countries) that applies to a large class of epidemic models. The dynamics suggest that ratios of variant proportions lead to simpler patterns. Therefore, we use ratios of variant proportions along with some parameters estimated from the dynamics as features in a GNN. We develop a benchmarking tool to evaluate variant emergence prediction over 87 countries and 36 variants. We leverage this tool to compare our GNN-based approach against our dynamics-only model and a number of machine learning models. Results show that the proposed dynamics-informed GNN method retrospectively outperforms all the baselines, including the currently pervasive framework of Physics-Informed Neural Networks (PINNs) that incorporates the dynamics in the loss function.

新型冠状病毒变异在全球范围内的传播是COVID-19疫情暴发的主要驱动力之一，本研究提出了一种基于图神经网络的变种动态感知模型，能够预测不同国家特定变异株的到达时间及其传播情况，相较于统计建模和物理感知神经网络等传统方法，该模型在回顾性实验中表现出更高的准确性。

利用动态告知的图神经网络全球预测COVID-19变种的出现