Assessment of Lockdown Effectiveness in the Wake of COVID-19 in India Using the Auto Regressive Integrated Moving Average Model

ABSTRACT

Background The novel coronavirus disease (COVID-19) has been recognized as a global threat, and several studies are being conducted using various mathematical models to predict the probable evolution of this epidemic, which are subject to potential bias. In this study, we aimed to assess and compare the impact of lockdown among the Punjab, Delhi, and Gujarat states of India using the Auto Regressive Integrated Moving Average (ARIMA) model by comparing forecasted COVID-19 data with real-time data. Methods We analyzed the COVID-19 data of Indian states from the index case until May 17, 2020. Auto Regressive Integrated Moving Average (1,1,3) (0,0,0) model was used to forecast the possible cumulative cases until May 17, from data up to May 3, and compared with real-time data. Recovery rate, case-fatality rate, and test per millions of states were collated. Results The trend of cumulative cases in Punjab was moving downward below the forecasted lower confidence limit (R-2 = 0.9799), whereas the cumulative case trend of Delhi was moving along the forecasted upper confidence limit with the forecasted data until May 3 (R-2 = 0.9971) and the trend of cumulative cases was below the forecasted upper confidence limit (R-2 = 0.9992) in Gujarat. Conclusions In Gujarat and Delhi, the lockdown was not effective in controlling the rise in COVID-19 cases even after the 56th day of lockdown, whereas the Punjab state succeeded in preventing havoc of COVID-19. In lieu of lockdown, using facemasks and improving ventilation in closed workspace settings, crowded spaces, and close-contact settings are more pragmatic than keeping away from others in India.