Comprehensive public health evaluation of lockdown as a non-pharmaceutical intervention on COVID-19 spread in India: national trends masking state-level variations.
Comprehensive public health evaluation of lockdown as a non-pharmaceutical intervention on COVID-19 spread in India: national trends masking state-level variations.
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DOI：
10.1136/bmjopen-2020-041778
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发表时间：
2020-12-10
期刊：
影响因子：
2.9
通讯作者：
Mukherjee B
中科院分区：
文献类型：
--
作者：
Salvatore M;Basu D;Ray D;Kleinsasser M;Purkayastha S;Bhattacharyya R;Mukherjee B
评估印度为应对2019冠状病毒病疫情而于3月25日至5月31日实施的四阶段全国封城措施的效果，并揭示各邦在多项公共卫生指标方面的差异。队列研究（病例计数的每日时间序列）。以观察和人口为基础。截至2020年5月31日，印度全国及20个邦的确诊COVID-19病例占印度目前累计病例数的99%以上。封锁（非医疗干预）。我们通过提供有关COVID-19疫情某些方面的评估证据，说明了州一级趋势的掩盖，并强调了各州之间的差异：病死率，病例倍增时间，有效繁殖数量和检测规模。3月24日，印度的估计有效繁殖数R为3.36（95%CI 3.03至3.71），而5月25日至5月31日的估计平均值为1.27（95%CI 1.26至1.28）。同样，3月24日，印度全境的估计翻一番时间为3.56天，而5月31日过去7天的平均值为14.37天。平均每日检测次数从1 717次（3月19日至25日）增加到113 372次（5月25日至31日），检测阳性率从2.1%增加到4.2%。然而，许多国家表现出与这些国家模式的重大偏离。封锁期间的变化模式表明，封锁在一定程度上有效减缓了病毒在全国范围内的传播。然而，在州一级存在很大的差异，识别这些差异有助于了解大流行的动态和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直辖区疫情的全面评估，沿着了一套交互式可视化工具，每天在covind19.org上更新。
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.
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影响因子：
8.5
作者：
Ghosh, Palash;Ghosh, Rik;Chakraborty, Bibhas
通讯作者：
Chakraborty, Bibhas
影响因子：
120.7
作者：
Pan, An;Liu, Li;Wu, Tangchun
通讯作者：
Wu, Tangchun
影响因子：
7.3
作者：
Rajendrakumar AL;Nair ATN;Nangia C;Chourasia PK;Chourasia MK;Syed MG;Nair AS;Nair AB;Koya MSF
通讯作者：
Koya MSF
DOI：
10.1016/j.trip.2020.100187
发表时间：
2020-09-01
影响因子：
--
作者：
Maji, Avijit;Choudhari, Tushar;Sushma, M. B.
通讯作者：
Sushma, M. B.
影响因子：
5
作者：
Ghani, AC;Donnelly, CA;Leung, GM
通讯作者：
Leung, GM