Applying Statistical inference techniques to smoothen COVID19 data and predict future infection

Abstract

The main purpose of our work is to collect the second phase COVID19 dataset and doing some analysis by mathematical operations or model on it. The analysis is done on Infection and Deaths (Daily and Cumulative). COVID19 data is collected from 15th May 2021 to 15th May 2022 which is one year in duration. In the dataset, there are some errors as it is not continuously updating sometimes or the data may not be correct so we have generated box-plot and find out some abnormal values which is outlier. Then the abnormal values are fixed by using Savitzky–Golay filter. The Savitzky–Golay filter is used to smooth the curve. The smoothen curve is plotted by using the smoothen values. Standard SIRD Model is applied to analyze the number of susceptible, infected, recovered, and deceased people from the COVID19 dataset.