COVID-19 Epidemic Analysis using Machine Learning and Deep Learning Algorithms

Narinder Singh Punn; Sanjay Kumar Sonbhadra; Sonali Agarwal

The COVID-19 spread has brought the world under the brink of loss of human lives due to which it is of utmost importance to analyze the transmission growth at the earliest and forecast the forthcoming possibilities of the transmission. With this objective, state-of-the-art mathematical models are adopted based on machine learning such as support vector regression (SVR) [16] and polynomial regression (PR) [17] , and deep learning regression models such as a standard deep neural network (DNN) and recurrent neural networks (RNN) using long short-term memory (LSTM) cells [18] . Machine learning and deep learning approaches are implemented using the python library "sklearn" and "keras" respectively, to predict the total number of confirmed, recovered, and death cases worldwide. The prediction will allow undertaking the necessary decisions based on transmission growth such as increasing the lockdown period, executing the sanitation procedure, providing the everyday resources, etc.