Using AI and ML techniques to Forecast COVID-19 cases with Real-time Data Sets

The spread of COVID-19, namely SARS-CoV-2, has created a disastrous situation around the world causing an unclear future. Machine Learning (ML) and Deep Learning (DL) have a vital role in tracking the disease, predicting the outgrowth of the epidemic, and outlining strategies and policies to control its spread. Despite the inaccuracies of medical forecasts, the numbers of COVID-19 cases forecasts provide us with valuable information for recognizing the present and preparing for the future. This study proposes a time series based deep learning model, specifically the Long Short-Term Memory (LSTM) model. The model will predict the active, confirmed, deaths and recovered cases for 7 days ahead for Egypt and Saudi Arabia based on real-time data. The Egypt prediction model achieves Mean Absolute Percentage Error (MAPE) of 3.26150, a Root Mean Square Error (RMSE) of 0.0144, a Mean Square Error (MSE) of 0.0002, and a Mean Absolute Error (MAE) of 0.0092. While the Saudi prediction model obtains a MAPE of 5.0553, a RMSE of 0.0170, a MSE of 0.0002, and a MAE of 0.0150.