Predicting the number of infections in the anti-epidemic process is extremely beneficial to the government in developing anti-epidemic strategies, especially in fine-grained geographic units. Previous works focus on low spatial resolution prediction, e.g., county-level, and preprocess data to the same geographic level, which loses some useful information. In this paper, we propose a fine-grained population mobility data-based model (FGC-COVID) utilizing data of two geographic levels for community-level COVID-19 prediction. We use the population mobility data between Census Block Groups (CBGs), which is a finer-grained geographic level than community, to build the graph and capture the dependencies between CBGs using graph neural networks (GNNs). To mine as finer-grained patterns as possible for prediction, a spatial weighted aggregation module is introduced to aggregate the embeddings of CBGs to community level based on their geographic affiliation and spatial autocorrelation. Extensive experiments on 300 days LA city COVID-19 data indicate our model outperforms existing forecasting models on community-level COVID-19 prediction.
翻译:预测预防流行病进程中的感染人数对政府制定预防流行病战略极为有益,特别是在精细的地理单位中。以前的工作重点是低空间分辨率预测,例如,县一级和预处理数据到同样的地理层次,从而失去一些有用的信息。在本文件中,我们建议采用一个细微的根据人口流动数据模型(FGC-COVID),利用两个地理层次的数据进行社区一级COVID-19的预测。我们使用人口普查区块组(CBGs)之间的人口流动数据(CBGs),这是比社区更精细的地理层次。我们利用图形神经网络(GNNNSs)建立图表并捕捉到CBGs之间的依赖关系。为了尽可能精细的进行预测,我们采用了一个空间加权汇总模块,以将CBGs嵌入社区一级的地理联系和空间自自动化关系加以汇总。我们对300天的LA CCOVID-19数据进行了广泛的实验,表明我们的模型超过了社区一级COVID预测的现有模型。