Examining Deep Learning Models with Multiple Data Sources for COVID-19 Forecasting
Examining Deep Learning Models with Multiple Data Sources for COVID-19 Forecasting
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DOI：
10.1109/bigdata50022.2020.9377904
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Lijing Wang;A. Adiga;S. Venkatramanan;Jiangzhuo Chen;B. Lewis;M. Marathe
中科院分区：
文献类型：
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作者：
Lijing Wang;A. Adiga;S. Venkatramanan;Jiangzhuo Chen;B. Lewis;M. Marathe
COVID-19疫情是自1918年流感大流行以来最严重的公共卫生灾难。在COVID-19等大流行期间，及时可靠地对流行动态进行时空预测至关重要。基于深度学习的时间序列预测模型最近越来越受欢迎，并已成功用于流行病预测。在这里，我们专注于基于深度学习的COVID-19预测模型的设计和分析。我们实现了多个基于递归神经网络的深度学习模型，并使用堆栈集成技术将它们联合收割机组合起来。为综合多种因素对COVID-19传播的影响，我们考虑多种来源，如COVID-19确诊和死亡病例数数据以及测试数据，以更好地进行预测。为了克服训练数据的稀疏性并解决疾病的动态相关性，我们提出了基于聚类的训练用于高分辨率预测。这些方法帮助我们识别由于各种时空效应而导致的某些区域组的相似趋势。我们研究了用于预测县、州和国家一级每周COVID-19新确诊病例的拟议方法。对COVID-19背景下的不同时间序列模型进行了全面比较和分析。结果表明，与更复杂的模型相比，简单的深度学习模型可以实现相当或更好的性能。我们目前正在整合我们的方法，作为我们每周预测的一部分，我们提供给州和联邦当局。
The COVID-19 pandemic represents the most significant public health disaster since the 1918 influenza pandemic. During pandemics such as COVID-19, timely and reliable spatio-temporal forecasting of epidemic dynamics is crucial. Deep learning-based time series models for forecasting have recently gained popularity and have been successfully used for epidemic forecasting. Here we focus on the design and analysis of deep learning-based models for COVID-19 forecasting. We implement multiple recurrent neural network-based deep learning models and combine them using the stacking ensemble technique. In order to incorporate the effects of multiple factors in COVID-19 spread, we consider multiple sources such as COVID-19 confirmed and death case count data and testing data for better predictions. To overcome the sparsity of training data and to address the dynamic correlation of the disease, we propose clustering-based training for high-resolution forecasting. The methods help us to identify the similar trends of certain groups of regions due to various spatio-temporal effects. We examine the proposed method for forecasting weekly COVID-19 new confirmed cases at county-, state-, and country-level. A comprehensive comparison between different time series models in COVID-19 context is conducted and analyzed. The results show that simple deep learning models can achieve comparable or better performance when compared with more complicated models. We are currently integrating our methods as a part of our weekly forecasts that we provide state and federal authorities.