  SARS-CoV2, which causes coronavirus disease (COVID-19) is continuing to
spread globally and has become a pandemic. People have lost their lives due to
the virus and the lack of counter measures in place. Given the increasing
caseload and uncertainty of spread, there is an urgent need to develop machine
learning techniques to predict the spread of COVID-19. Prediction of the spread
can allow counter measures and actions to be implemented to mitigate the spread
of COVID-19. In this paper, we propose a deep learning technique, called Deep
Sequential Prediction Model (DSPM) and machine learning based Non-parametric
Regression Model (NRM) to predict the spread of COVID-19. Our proposed models
were trained and tested on novel coronavirus 2019 dataset, which contains 19.53
Million confirmed cases of COVID-19. Our proposed models were evaluated by
using Mean Absolute Error and compared with baseline method. Our experimental
results, both quantitative and qualitative, demonstrate the superior prediction
performance of the proposed models.
