COVID-19 Outbreak: An Epidemic Analysis using Time Series Prediction Model

Abstract

Coronavirus (COVID-19) epidemic affects public health infrastructure across the world. The outbreak is considered as third major Coronavirus epidemic after SARS (Severe Acute Respiratory Syndrome) in the year 2002-2003 and MERS (Middle East Respiratory Syndrome) in 2015 since past 2 decades. It has been observed that the nature of growth of coronavirus is exponential. It has been tough to control and analyze the situation with limited human resource and treatment process must be carried for the large number of patients within an appropriate time. So, it has become obligatory to work on an automated model, grounded on computing approach, for curative measure. This paper concludes a Time Series Forecasting model and analyze the COVID-19 epidemic occurrence to check whether these numbers are going to be increased or decreased in near future. Statistical pattern analysis and data visualization is performed with widely accepted time series approaches as Auto-Regressive Integrated Moving Average (ARIMA) and its constituents Moving Average (MA) and Auto Regressive (AR). Finally, time-dependent parameters can enlighten the trends of the outbreak COVID-19 in India.