COVID-19 and Lockdown in India: Evaluation using Analysis of Covariance

Abstract

Background: The lockdown in India has entered into its ninth month to curb the Coronavirus disease-19 (COVID-19) pandemic. The objective of the present study is to evaluate impact of different phases of lockdown on evolution of new cases of infection and deaths due to COVID-19.Methods: In this retrospective longitudinal study, the Indian data on new cases of infection and deaths due to COVID-19 were retrieved from John Hopkins University dashboard. The cases from 25 March to 31 October 2020 were analyzed using analysis of covariance for four phases of lockdown and five phases of unlockdown.Results: The coefficients of regression for new cases did not differ significantly for initial four phases of lockdown and unlock-1, while from unlock-2 the coefficients showed significant decrease till unlock-5. While death cases showed no significant differences between coefficients of regression for initial four phases of lockdown and unlock, but coefficient of unlock-5 was significantly lower than unlock-4. Conclusion: The trends of coefficients of regression of new cases and deaths reveals positive effects of lockdown in flattening the epidemic curve. Though the pandemic is on downslope, till the availability of vaccines, non-pharmaceutical measures such as social distancing, wearing of masks need to be implemented.