Deep Learning Model for Predicting Spreading Rates of Pandemics, "COVID-19 as Case Study"

The outbreak of Coronavirus (COVD-19), has led to a catastrophic scenario over the globe, causing the cumulative prevalence of this virus to increase dramatically day by day. Both Machine learning (ML) and deep learning (DL) provide great chances to facilitate tracking disease, anticipating increase in pandemic, and hence planning for coverage techniques to control its spread. This work is based on the application of an advanced mathematical model to examine and predict the increase in a pandemic. On the bases of time-series data, an advanced DL model has been implemented to predict the risk of COVID-19 spreading in Iraq. A hybrid approach is presented where two deep learning algorithms; LSTM and GRU are brought up together to achieve good prediction with rewarding levels of (MAE = 0.109), (MAPE = 0.191) and (RMSE = 0.134).