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Observational Study
. 2020 Dec 10;10(12):e041778.
doi: 10.1136/bmjopen-2020-041778.

Comprehensive public health evaluation of lockdown as a non-pharmaceutical intervention on COVID-19 spread in India: national trends masking state-level variations

Maxwell Salvatore  1   2 , Deepankar Basu  3 , Debashree Ray  4   5 , Mike Kleinsasser  1 , Soumik Purkayastha  1 , Rupam Bhattacharyya  1 , Bhramar Mukherjee  6   2
Affiliations
Observational Study

Comprehensive public health evaluation of lockdown as a non-pharmaceutical intervention on COVID-19 spread in India: national trends masking state-level variations

Maxwell Salvatore et al. BMJ Open. .

Abstract

Objectives: To evaluate the effect of four-phase national lockdown from March 25 to May 31 in response to the COVID-19 pandemic in India and unmask the state-wise variations in terms of multiple public health metrics.

Design: Cohort study (daily time series of case counts).

Setting: Observational and population based.

Participants: Confirmed COVID-19 cases nationally and across 20 states that accounted for >99% of the current cumulative case counts in India until 31 May 2020.

Exposure: Lockdown (non-medical intervention).

Main outcomes and measures: 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% CI 3.03 to 3.71) on 24 March, whereas the average of estimates from 25 May to 31 May stands at 1.27 (95% CI 1.26 to 1.28). Similarly, the estimated doubling time across India was at 3.56 days on 24 March, and the past 7-day average for the same on 31 May is 14.37 days. The average daily number of tests increased from 1717 (19-25 March) to 113 372 (25-31 May) while the test positivity rate increased from 2.1% to 4.2%, respectively. However, various states exhibit substantial departures from these national patterns.

Conclusions: Patterns of change over lockdown periods indicate the lockdown has been partly effective in slowing the spread of the virus nationally. However, there exist 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 visualisation tools that are daily updated at covind19.org.

Keywords: epidemiology; public health; statistics & research methods.

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Conflict of interest statement

Competing interests: None declared.

Figures

Figure 1
Figure 1
Daily number of reported cases, fatalities and recovered cases in India (panel A) over the period between 15 March and 31 May with four states to capture the variation. Kerala (panel B) was doing well initially but has seen a recent surge of cases. Punjab (panel C) is an example state of ‘doing well’ whereas case counts in Maharashtra (panel D) and Delhi (panel E) are still increasing.
Figure 2
Figure 2
Forest plot dashboard. (A) Forest plot of estimated case fatality rates (CFR1) based on all confirmed cases as of 31 May, along with 95% CIs, for 20 states and union territories of India, and a national summary.(B) Forest plot of estimated doubling times (in days) based on data from a 7-day past window from 31 May, along with 95% CIs, for 20 states and union territories of India, and a national summary. (C) Forest plot of estimated time-varying R (effective basic reproduction number) based on data from a 7-day past window from 31 May, along with 95% CIs, for 20 states and union territories of India, and a national summary. (D) Forest plot of test positivity rates (proportion scale) based on data as of 31 May, for 20 states and union territories of India, along with a national summary.
Figure 3
Figure 3
National estimates of doubling times and time-varying R. (A) Estimated doubling times of total number of COVID-19 cases in India, with averages for the prelockdown and postlockdown periods and past 7-day average as of 31 May. (B) Estimated time-varying R (effective basic reproduction number) for COVID-19 in India with averages for the prelockdown and postlockdown periods and past 7-day average as of 31 May, along with 95% CIs.
Figure 4
Figure 4
State-wise estimates of doubling times and time-varying R. (A) Estimated doubling times of total number of COVID-19 cases in 20 Indian states and union territories. (B) Estimated time-varying R (effective basic reproduction number) for COVID-19 in 20 Indian states and union territories along with 95% CIs.
Figure 5
Figure 5
Time series plot of test positivity rates for India over the period between 1 April and 31 May.
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