Predictability of COVID-19 Infections Based on Deep Learning and Historical Data

The COVID-19 disease has spread worldwide since 2020, causing a high number of deaths as well infections, and impacting economic, social health systems. Understanding its dynamics may facilitate better understanding behavior, reducing the impact similar diseases in future. Classical modeling techniques have failed predicting behavior this disease, they been unable to capture hidden features data collected about disease. present research benefits from capacity modern computers new trends artificial intelligence (AI), specifically three deep learning (DL) neural networks: recurrent network (RNN), gated unit (GRU), long short-term memory (LSTM). We thus modelled daily infections four countries (Saudi Arabia, Egypt, Italy, India) that vary their climates, cultures, populations, results show simple-structure RNN algorithm is at DL promising potential can be used efficiently even case limited datasets.