论文标题
具有复发性神经网络和基于微分方程的时空传染病模型，并应用于COVID-19
A Recurrent Neural Network and Differential Equation Based Spatiotemporal Infectious Disease Model with Application to COVID-19
论文作者
论文摘要
2019年冠状病毒病（Covid-19）的爆发对世界产生了重大影响。对感染的趋势进行建模和对病例的实时预测，可以帮助决策和控制疾病的扩散。但是，由于每日样本的时间有限，诸如复发性神经网络（RNN）之类的数据驱动方法的性能较差。在这项工作中，我们开发了一个基于流行病因方程（SIR）和RNN的综合时空模型。简化和离散化后的前者是区域的时间感染趋势的紧凑模型，而后者则建模了最近的邻近区域的效果。后者捕获了潜在的空间信息。 ％尚未公开报告。我们在意大利培训并测试了我们的模型，并表明它超过了现有的时间模型（完全连接的NN，SIR，ARIMA），以预测，尤其是在有限培训数据的制度中预测。
The outbreaks of Coronavirus Disease 2019 (COVID-19) have impacted the world significantly. Modeling the trend of infection and real-time forecasting of cases can help decision making and control of the disease spread. However, data-driven methods such as recurrent neural networks (RNN) can perform poorly due to limited daily samples in time. In this work, we develop an integrated spatiotemporal model based on the epidemic differential equations (SIR) and RNN. The former after simplification and discretization is a compact model of temporal infection trend of a region while the latter models the effect of nearest neighboring regions. The latter captures latent spatial information. %that is not publicly reported. We trained and tested our model on COVID-19 data in Italy, and show that it out-performs existing temporal models (fully connected NN, SIR, ARIMA) in 1-day, 3-day, and 1-week ahead forecasting especially in the regime of limited training data.
