Predictive Analytics of COVID-19 Pandemic: Statistical Modelling Perspective

Abstract

The novel Coronavirus-19 (COVID-19) is an infectious disease and it causes serious lung injury. COVID-19 induces human disease, which has killed numerous people around the world. Moreover, the World Health Organization (WHO) declares this virus as a pandemic, and all countries attempt to monitor and control it by locking all places. The illness induces respiratory influenza-like problems with symptoms such as cold, cough, fever, and difficulty breathing in extremely severe cases. COVID-2019 has been viewed as a global pandemic, and a few analyses are being performed using multiple computational methods to predict the possible development of this pestilence. Considering the various conditions and inquiries these numerical models are based on future tendencies. Multiple techniques have been proposed that could be helpful in forecasting the spread of COVID-19. Through statistical modeling on the COVID-19 data, we performed linear regression, random forest, ARIMA, and LSTMs, to estimate the empirical indication of COVID-19 ailment and intensity in 4 countries (USA, India, Brazil, and Russia), in order to come up with a better validation.