Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
Center for Precision Health Data Science, University of Michigan, Ann Arbor, Michigan, USA.
BMJ Open. 2020 Dec 10;10(12):e041778. doi: 10.1136/bmjopen-2020-041778.
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.
Cohort study (daily time series of case counts).
Observational and population based.
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.
Lockdown (non-medical intervention).
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.
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.
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.
评估印度为应对 COVID-19 疫情而于 3 月 25 日至 5 月 31 日实施的四阶段全国封锁措施的效果,并揭示在多项公共卫生指标方面各州的变化情况。
队列研究(病例计数的每日时间序列)。
观察性和基于人群的研究。
全国和 20 个州的确诊 COVID-19 病例,这些病例在截至 2020 年 5 月 31 日占印度当前累计病例数的>99%。
封锁(非医疗干预)。
通过呈现 COVID-19 疫情爆发的某些方面的评估证据,我们说明了州级趋势的掩盖情况,并突出了各州之间的差异:病死率、病例倍增时间、有效繁殖数和检测规模。
印度的估计有效繁殖数 R 于 3 月 24 日为 3.36(95%CI 3.03 至 3.71),而 5 月 25 日至 5 月 31 日的平均估计值为 1.27(95%CI 1.26 至 1.28)。同样,印度的估计倍增时间在 3 月 24 日为 3.56 天,而 5 月 31 日的过去 7 天平均值为 14.37 天。每日平均检测量从 1717(3 月 19 日至 25 日)增加到 113372(5 月 25 日至 31 日),而检测阳性率从 2.1%增加到 4.2%。然而,各州之间存在很大的差异。
封锁期间的变化模式表明,封锁在一定程度上减缓了病毒在全国范围内的传播。然而,州一级的差异很大,了解这些差异有助于理解疫情的动态并制定有效的公共卫生干预措施。我们的框架提供了对印度各邦和联邦属地的大流行的全面评估,以及一组互动可视化工具,每天在 covind19.org 更新。