Detecting the spread of coronavirus will go a long way toward reducing human and economic loss. Unfortunately, existing Epidemiological models used for COVID 19 prediction models are too slow and fail to capture the COVID-19 development in detail. This research uses Partial Differential Equations to improve the processing speed and accuracy of forecasting of COVID 19 governed by SEIRD model equations. The dynamics of COVID 19 were extracted using Convolutional Neural Networks and Deep Residual Recurrent Neural Networks from data simulated using PDEs. The DRRNNs accuracy is measured using Mean Squared Error. The DRRNNs COVID-19 prediction model has been shown to have accurate COVID-19 predictions. In addition, we concluded that DR-RNNs can significantly advance the ability to support decision-making in real time COVID-19 prediction.

翻译：不幸的是,用于COVID 19预测模型的现有流行病学模型太慢,未能详细捕捉COVID-19的开发情况。这项研究使用部分差异方程式来提高COVID 19预测的处理速度和准确性,由SEIRD模型方程式管理。COVID 19的动态利用了使用PDES模拟的数据的革命神经网络和深海残余常态神经网络进行提取。DRRNNS的准确性是用平方误差测量的。DRRNNS COVID-19预测模型已证明有准确的COVID-19预测。此外,我们得出结论,DR-RNNNs能够大大提高实时支持COVID-19预测决策的能力。