Model for movement of individuals in society after the corona pandemic using deep learning algorithms

The health crisis that attributed to the rapid spread of the COVID-19 has impacted the globe negatively in terms of economy, education and transport and led to the global lockdown. The risk of the COVID-19 infection has been increased due to a lack of successful cure for the disease. Thus, social distancing is considered as the most appropriate precaution measureto control the viral spread throughout the world. Social distancing means that physical contact between individuals can be prevented to reduce the viral transmission effectively. The purpose of this work is to provide a deep learning model capable of predicting the movement of people in the pandemic to take precautions and control the COVID-19 infection. This model is based on twoLSTMand GRU algorithms. The results show that the GRU is better than LSTM in terms of prediction error rate and duration.