  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.
