Prediction of COVID-19 pervasiveness in six major affected states of India and two-stage variation with temperature

Abstract

Coronavirus disease knocked in Wuhan city of China in December 2019 which spread quickly across the world and infected millions of people within a short span of time. COVID-19 is a fast-spreading contagious disease which is caused by SARS-CoV-2 (severe acute respiratory syndrome-coronavirus-2). Accurate time series forecasting modeling is the need of the hour to monitor and control the universality of COVID-19 effectively, which will help to take preventive measures to break the ongoing chain of infection. India is the second highly populated country in the world and in summer the temperature rises up to 50°, nowadays in many states have more than 40° temperatures. The present study deals with the development of the autoregressive integrated moving average (ARIMA) model to predict the trend of the number of COVID-19 infected people in most affected states of India and the effect of a rise in temperature on COVID-19 cases. Cumulative data of COVID-19 confirmed cases are taken for study which consists of 77 sample points ranging from 1st March 2020 to 16th May 2020 from six states of India namely Delhi (Capital of India), Madya Pradesh, Maharashtra, Punjab, Rajasthan, and Uttar Pradesh. The developed ARIMA model is further used to make 1-month ahead out of sample predictions for COVID-19. The performance of ARIMA models is estimated by comparing measures of errors for these six states which will help in understanding future trends of COVID-19 outbreak. Temperature rise shows slightly negatively correlated with the rise in daily cases. This study is noble to analyse the variation of COVID-19 cases with respect to temperature and make aware of the state governments and take precautionary measures to flatten the growth curve of confirmed cases of COVID-19 infections in other states of India, nearby countries as well.

Autoregressive models in environmental forecasting time series: a theoretical and application review

“…1 ). The process is repeated until a satisfactory model is obtained which can be then used for prediction (Singh et al 2020a , b , 2021a , b ). In this section, historical evolution of various steps involved in ARMA modeling is discussed in detail.…”

Estimation of COVID-19 patient numbers using artificial neural networks based on air pollutant concentration levels

“…To compare the accuracy of the models, a nonlinear autoregressive neural network was developed. Singh et al ( 2021 ) interested in the development of ARIMA model to predict the number of Covid-19 cases and the effect of a rise in temperature in most affected states of India. Yudistira et al ( 2021 ) presented a multivariate analysis to emerge comprehensive explanations regarding several factors contributing to the dynamics of the pandemic.…”