论文标题
AICOV：COVID-19与人口协变量预测的综合深度学习框架
AICov: An Integrative Deep Learning Framework for COVID-19 Forecasting with Population Covariates
论文作者
论文摘要
COVID-19的大流行对人类生活的健康，经济，社会，政治和几乎所有主要方面都有深远的影响。因此，就它们发生的更广泛的社会背景而言，建模Covid-19和其他大流行者非常重要。我们介绍了AICOV的体系结构，该体系结构为Covid-19与人口协变量的预测提供了一个综合的深度学习框架，其中一些可能是推定的危险因素。我们已经将多种不同的策略整合到了AICOV中，包括使用基于LSTM甚至建模的深度学习策略的能力。为了证明我们的方法，我们进行了一项飞行员，该飞行员将来自多个来源的人口协变量整合在一起。因此，AICOV不仅包含有关COVID-19案件和死亡的数据，而且更重要的是，在当地一级的社会经济，健康和行为风险因素。与仅使用案例和死亡数据的数据相比，编译的数据被馈入AICOV，因此我们通过将数据集成到模型中获得了改进的预测。
The COVID-19 pandemic has profound global consequences on health, economic, social, political, and almost every major aspect of human life. Therefore, it is of great importance to model COVID-19 and other pandemics in terms of the broader social contexts in which they take place. We present the architecture of AICov, which provides an integrative deep learning framework for COVID-19 forecasting with population covariates, some of which may serve as putative risk factors. We have integrated multiple different strategies into AICov, including the ability to use deep learning strategies based on LSTM and even modeling. To demonstrate our approach, we have conducted a pilot that integrates population covariates from multiple sources. Thus, AICov not only includes data on COVID-19 cases and deaths but, more importantly, the population's socioeconomic, health and behavioral risk factors at a local level. The compiled data are fed into AICov, and thus we obtain improved prediction by integration of the data to our model as compared to one that only uses case and death data.
