Predictive Analytics of COVID-19 Pandemic: Statistical Modelling Perspective

S Lokesh  KUMAR; Vergin Raja  SAROBIN M; Jani ANBARASI L ANBARASI L

doi:10.53075/Ijmsirq/099687465337

Authors

S Lokesh KUMAR Vellore Institute of Technology, Chennai, India
Vergin Raja SAROBIN M Vellore Institute of Technology, Chennai, India
Jani ANBARASI L ANBARASI L Vellore Institute of Technology, Chennai, India

DOI:

https://doi.org/10.53075/Ijmsirq/099687465337

Keywords:

COVID-19 forecasting, Machine learning, Regression, Time series prediction, Deep learning, Statistical modelling

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 modelling 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.