Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA.
Department of Industrial Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.
Int J Environ Res Public Health. 2021 Apr 6;18(7):3834. doi: 10.3390/ijerph18073834.
The COVID-19 pandemic has had unprecedented social and economic consequences in the United States. Therefore, accurately predicting the dynamics of the pandemic can be very beneficial. Two main elements required for developing reliable predictions include: (1) a predictive model and (2) an indicator of the current condition and status of the pandemic. As a pandemic indicator, we used the effective reproduction number (Rt), which is defined as the number of new infections transmitted by a single contagious individual in a population that may no longer be fully susceptible. To bring the pandemic under control, Rt must be less than one. To eliminate the pandemic, Rt should be close to zero. Therefore, this value may serve as a strong indicator of the current status of the pandemic. For a predictive model, we used graph neural networks (GNNs), a method that combines graphical analysis with the structure of neural networks. We developed two types of GNN models, including: (1) graph-theory-based neural networks (GTNN) and (2) neighborhood-based neural networks (NGNN). The nodes in both graphs indicated individual states in the United States. While the GTNN model's edges document functional connectivity between states, those in the NGNN model link neighboring states to one another. We trained both models with R numbers collected over the previous four days and asked them to predict the following day for all states in the United States. The performance of these models was evaluated with the datasets that included R values reflecting conditions from 22 January through 26 November 2020 (before the start of COVID-19 vaccination in the United States). To determine the efficiency, we compared the results of two models with each other and with those generated by a baseline Long short-term memory (LSTM) model. The results indicated that the GTNN model outperformed both the NGNN and LSTM models for predicting Rt.
新冠疫情给美国带来了前所未有的社会和经济后果。因此，准确预测疫情动态可能非常有益。开发可靠预测所需的两个主要元素包括：(1)预测模型和(2)当前疫情状况和状态的指标。作为一种大流行指标，我们使用有效繁殖数(Rt)，它定义为在一个可能不再完全易感的人群中，单个传染性个体传播的新感染数量。要控制疫情，Rt 必须小于 1。要消灭疫情，Rt 应接近 0。因此，该值可作为疫情当前状况的有力指标。对于预测模型，我们使用了图神经网络(GNN)，这是一种将图形分析与神经网络结构相结合的方法。我们开发了两种类型的 GNN 模型，包括：(1)基于图论的神经网络(GTNN)和(2)基于邻域的神经网络(NGNN)。图中的节点表示美国的各个州。虽然 GTNN 模型的边记录了各州之间的功能连接，但 NGNN 模型的边将相邻的州连接在一起。我们使用前四天收集的 R 数训练这两种模型，并要求它们对美国所有州的第二天进行预测。使用包括 2020 年 1 月 22 日至 11 月 26 日(美国开始接种 COVID-19 疫苗之前)的条件反映的 R 值的数据集评估了这些模型的性能。为了确定效率，我们将两个模型的结果相互比较，并与基线长短期记忆(LSTM)模型生成的结果进行比较。结果表明，GTNN 模型在预测 Rt 方面优于 NGNN 和 LSTM 模型。
