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.
ABSTRACT
Coronavirus disease 2019 (COVID-19) was declared an epidemic and a global health emergency by the World Health Organization (WHO), prompting various countries to implement early and stringent social distancing protocols through lockdown, to flatten the epidemic curve. The objective of our present study was to assess the impacts and effectiveness of the lockdown protocol in Karnataka and Punjab, compared with the implementation of this method in Australia and the United Kingdom (UK). This study involved the collection of data from different authorized databases, in two phases. The first phase included the time starting with the first-reported index case through the 14th day after the declaration of lockdown, for each country. The second phase involved the data collected between the 15th day through the 28th day of the lockdown. The highest doubling rate for cases was observed in Australia, followed by Karnataka and Punjab, whereas the lowest was observed in the UK. Comparisons of the numbers of the samples tested, the mortality rate, and the recovery rate between Karnataka and Punjab, after the implementation of lockdown, revealed a better recovery rate and lower mortality rate in Karnataka than in Punjab. Our study revealed that the implementation of social distancing and lockdown reduced the transmission of the coronavirus and the number of cases reported. However, the effectiveness of lockdown varied among locations, due to demographic and physiological differences.