Due to its widespread infection and severe threat to the human species, COVID-19 was deemed a pandemic by the World Health Organization (WHO). This study studied the impact of COVID-19 in 5 countries: The United Kingdom, India, Russia, France and Germany in terms of their total cases and deaths, respectively. The total cases and deaths in each nation for the previous 100 days were forecasted using two well-known time series models, ARIMA and Prophet (by Facebook). The predicted and actual values were compared. It was observed that the Prophet model performed sufficiently better than ARIMA. While ARIMA did not even reach half-precision, Prophet accurately forecasted the confirmed cases with 91% precision. Both models' root means squared errors were used to compare the performance. Further, a correlation matrix was constructed to evaluate the relationship between population density, mean cases, mean deaths and other socioeconomic factors. It was observed that the population density of a nation had a significant effect on the development of COVID-19 infection in that country and the country's growth. For a better understanding of the models, more statistical measures could have been used for the comparison. Morever, other the application of other stabilizing methods could have improved the performance of ARIMA model.
