SARS-CoV-2 forecasting using regression and ARIMA

Respiratory syndrome coronavirus 2 (SARS-CoV-2) is a pandemic coronavirus that is spreading quickly throughout the world. Every person experiences fear due to the unexpected pandemic (Covid-19), which is spreading quickly and affecting people. The affected patients' daily growth rate is accelerating. A predictive analysis was done to determine the potential number of deaths brought on by this pandemic. An official dataset of 35 states/UTs of India was investigated with predictive analysis (regression modeling). To predict the patient's deceased, both recovered and active cases have been impacted. This paper estimated future deceased counts based on active and recovered cases individually and jointly. The high positive linear correlation proved that the active and recovered case affected the patient's deceased rate. Regression models explored high aspects of the deceased ahead. Multiple linear regression has predicted a deceased patient based on active and recovered patients with significant R 2 = 0.89. Further, temporal dynamics of Covid-19 timing analyzed with Auto-Regressive Integrated Moving Average forecasted confirmed, active, recovered, and deceased cases for the next 40 days. According to the results, a high cure is still required for active and recovered patients, and the government should follow in obligatory footsteps to avoid more deceased predicted with the models.