Deep Learning Models for Early Detection and Prediction of the spread of Novel Coronavirus (COVID-19)
This work addresses the urgent need for accurate spread prediction to inform countermeasures during the COVID-19 pandemic, but it appears incremental as it applies existing deep learning and machine learning techniques to new data.
The paper tackles the problem of predicting the spread of COVID-19 by proposing Deep Sequential Prediction Model (DSPM) and Non-parametric Regression Model (NRM), achieving superior prediction performance compared to a baseline method as demonstrated by Mean Absolute Error on a dataset of 19.53 million confirmed cases.
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.