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

Narinder Singh Punn; Sanjay Kumar Sonbhadra; Sonali Agarwal

As the outbreak of the COVID-19 has become a worldwide pandemic, the real-time analyses of epidemiological data are needed to prepare the society with better action plans against the disease. Since the birth of novel COVID-19 [1] , the world is restlessly fighting with its cause. As of April 1, 2020, based on the globally shared live data by the Johns Hopkins dashboard, worldwide there are 932,605 confirmed cases, out of which 193,177 are recovered and 46,809 lost their lives [2] . COVID-19 belongs to the family of the SARS-CoV and MERS-CoV, where it begins with the initial level symptoms of the common cold to severe level of respiratory diseases causing difficulty in breathing, tiredness, fever, and dry cough [3] . Prasad et al. [4] observed that the identification of the virus can be improved by imaging using immunoelectron microscopy techniques [5] . Figure 1 shows the typical structure of COVID-19 virus, from the throat swab of the first Indian laboratory-confirmed case, captured using sample electron microscopy imaging. Till date, detailed morphology and ultrastructure of this virus remain incompletely understood, and there are no specific vaccines or treatments for COVID-19. However, many ongoing clinical trials are evaluating potential treatments.