论文标题
condlstm-Q：一种新颖的深度学习模型，用于预测COVID-19以良好的地理量表的死亡率
condLSTM-Q: A novel deep learning model for predicting Covid-19 mortality in fine geographical Scale
论文作者
论文摘要
侧重于不同时空量表的预测模型有利于政府和医疗保健系统，以对抗COVID-19的大流行。在这里，我们介绍了有条件的长期短期记忆网络（condlstm-Q），这是一个良好的模型，可在县级对Covid-19的covid-19死亡通行证进行两周的预测窗口进行分数预测。这个精细的地理量表在公开可用的预测模型中是罕见但有用的功能，这将特别受益于州级官员以协调州内的资源。来自Condlstm-Q的分位数预测将预测的死亡人数的分布告知人们，从而更好地评估了可能的严重性轨迹。鉴于神经网络模型的可伸缩性和概括性，该模型可以轻松地结合其他数据源，并且可以进一步开发以产生其他有用的预测，例如新病例或住院治疗。
Predictive models with a focus on different spatial-temporal scales benefit governments and healthcare systems to combat the COVID-19 pandemic. Here we present the conditional Long Short-Term Memory networks with Quantile output (condLSTM-Q), a well-performing model for making quantile predictions on COVID-19 death tolls at the county level with a two-week forecast window. This fine geographical scale is a rare but useful feature in publicly available predictive models, which would especially benefit state-level officials to coordinate resources within the state. The quantile predictions from condLSTM-Q inform people about the distribution of the predicted death tolls, allowing better evaluation of possible trajectories of the severity. Given the scalability and generalizability of neural network models, this model could incorporate additional data sources with ease, and could be further developed to generate other useful predictions such as new cases or hospitalizations intuitively.
