A Comprehensive Public Health Evaluation of Lockdown as a Non-pharmaceutical Intervention on COVID-19 Spread in India: National Trends Masking State Level Variations

Abstract

Introduction India has been under four phases of a national lockdown from March 25 to May 31 in response to the COVID-19 pandemic. Unmasking the state-wise variation in the effect of the nationwide lockdown on the progression of the pandemic could inform dynamic policy interventions towards containment and mitigation. Methods Using data on confirmed COVID-19 cases across 20 states that accounted for more than 99% of the cumulative case counts in India till May 31, 2020, we illustrate the masking of state-level trends and highlight the variations across states by presenting evaluative evidence on some aspects of the COVID-19 outbreak: case-fatality rates, doubling times of cases, effective reproduction numbers, and the scale of testing. Results The estimated effective reproduction number R for India was 3.36 (95% confidence interval (CI): [3.03, 3.71]) on March 24, whereas the average of estimates from May 25 - May 31 stands at 1.27 (95% CI: [1.26, 1.28]). Similarly, the estimated doubling time across India was at 3.56 days on March 24, and the past 7-day average for the same on May 31 is 14.37 days. The average daily number of tests have increased from 1,717 (March 19-25) to 131,772 (May 25-31) with an estimated testing shortfall of 4.58 million tests nationally by May 31. However, various states exhibit substantial departures from these national patterns. Conclusions Patterns of change over lockdown periods indicate the lockdown has been effective in slowing the spread of the virus nationally. The COVID-19 outbreak in India displays large state-level variations and identifying these variations can help in both understanding the dynamics of the pandemic and formulating effective public health interventions. Our framework offers a holistic assessment of the pandemic across Indian states and union territories along with a set of interactive visualization tools that are daily updated at covind19.org.

Chasing the ghost of infection past: identifying thresholds of change during the COVID-19 infection in Spain

“…These stronger isolation policies were probably instrumental in flattening the curve of infections, though not sufficient to bring it to a complete halt. Similar to this case, strong lockdowns involving the enforcement of stringent restrictions to personal movements, use of public spaces, non-essential economic activities and in-office work have successfully contained the spread of COVID-19 in many countries and regions [28,[35][36][37][38][39][40]. Indeed, comparisons between related countries evidence that stricter lockdown policies reduce the number of COVID-19 deaths [41].…”

COVID-19 in Rajasthan: Status and Effects of Containment Measures

“…Even in Italy the CFR is reported more than the present study. 16 Studies [17][18][19][20] conducted in India are very few but they have also reported CFR more than the present study. Basu D et al 17 found CFR 3.5% in their study.…”

“…16 Studies [17][18][19][20] conducted in India are very few but they have also reported CFR more than the present study. Basu D et al 17 found CFR 3.5% in their study. Mazumder A et al 18 conducted a study on 1161 COVID patients and reported that 29 deaths and 94 patients recovered cases calculating CFR 2.5% and Recovery rate 8.1%.…”

“…Studies in India and abroad also reported positive impact of lockdown as in this present study. Basu D et al 17 observed that doubling time has increased from about 5 days to over 10 days at the middle of May 20. Arun Mitra et al 23 Gupta N et al 19 Gupta M et al 20 also reported increase in doubling time after lockdown and concluded that it is muchneeded effort to delay the peak flatten the epidemic-curve.…”

“…Case fatality rate was found lesser than that of reported by other authors. [13][14][15][16][17][18][19][20] whether in-house or in other countries. Most of studies were conducted in China like Wu Z et al 13 reported case fatality rate 2.3% with 14.8% in the above 80 years population in China.…”