Time Series Analysis Of COVID-19 Occurrence In Different States Of India: A Periodic Regression Analysis

Abstract

COVID-19 is the deadliest pandemic, with over 18.2 million people infected with the SARS-CoV-2 virus by August 2, 2021 resulting in human deaths and economic losses. A number of countries have  formulated control measures in order to prevent the spread of the virus. However, it is unknown when the outbreak will  subside in different countries around the world. The role of predicting the COVID-19 trend is extremely difficult. Indian government has made disease outbreak analysis a priority in order to implement necessary healthcare measures to reduce the impact of this deadly pandemic  on human health and country’s economics. The time series data for COVID-19 disease was collected from the website www.covid19india.org and were analyzed using a periodic regression model using the data from 22^nd Janaury March 2020 to 01^st Febraury 2021 the estimated number of cases until 27 July, 2021 was predicted to develop a stochastic model using periodic regression and were documented in top 10 highly infected states in India. The analysis revealed a  increasing pattern for the number of reporting cases in the early days of prediction and decreasing trend for the number of reporting cases in the later days of prediction, which could decrease in future days in Karnataka, West Bengal, Uttar Pradesh, Telangana, Bihar and Haryana states. However, in Madhya Pradesh, Andhra Pradesh, Maharashtra and Tamil Nadu states showed a rapid phase of rise in disease incidence, which is likely to infect a larger population and suggests the disease's pandemic existence over a duration. Our model emphasizes the importance of ongoing and continuous efforts that are in place in all states to minimize occurrence of new cases of  infections, so as to potentially improving India's economic wealth with the available resources.