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印度 COVID-19 疫情的传播动态及最优封锁退出策略建模。

Transmission dynamics of the COVID-19 epidemic in India and modeling optimal lockdown exit strategies.

机构信息

All India Institute of Medical Sciences (AIIMS), Sri Aurobindo Marg, Ansari Nagar East, New Delhi, Delhi 110029, India.

Indian Institute of Science Education and Research (IISER), Dr Homi Bhabha Road, Pune, Maharashtra 411008, India.

出版信息

Int J Infect Dis. 2021 Feb;103:579-589. doi: 10.1016/j.ijid.2020.11.206. Epub 2020 Dec 3.

DOI:10.1016/j.ijid.2020.11.206
PMID:33279653
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7713576/
Abstract

India imposed one of the world's strictest population-wide lockdowns on March 25, 2020 for COVID-19. We estimated epidemiological parameters, evaluated the effect of control measures on the epidemic in India, and explored strategies to exit lockdown. We obtained patient-level data to estimate the delay from onset to confirmation and the asymptomatic proportion. We estimated the basic and time-varying reproduction number (R and R) after adjusting for imported cases and delay to confirmation using incidence data from March 4 to April 25, 2020. Using a SEIR-QDPA model, we simulated lockdown relaxation scenarios and increased testing to evaluate lockdown exit strategies. R for India was estimated to be 2·08, and the R decreased from 1·67 on March 30 to 1·16 on April 22. We observed that the delay from the date of lockdown relaxation to the start of the second wave increases as lockdown is extended farther after the first wave peak-this delay is longer if lockdown is relaxed gradually. Aggressive measures such as lockdowns may be inherently enough to suppress an outbreak; however, other measures need to be scaled up as lockdowns are relaxed. Lower levels of social distancing when coupled with a testing ramp-up could achieve similar outbreak control as an aggressive social distancing regime where testing was not increased.

摘要

2020 年 3 月 25 日,印度为应对 COVID-19 实施了全球最严格的全民封锁措施之一。我们估计了流行病学参数,评估了控制措施对印度疫情的影响,并探讨了封锁退出策略。我们获得了患者层面的数据,以估计从发病到确诊的延迟时间和无症状比例。我们使用 2020 年 3 月 4 日至 4 月 25 日的发病数据,在调整输入病例和确诊延迟后,估计基本和时变繁殖数(R 和 R)。使用 SEIR-QDPA 模型,我们模拟了放松封锁的情景和增加检测,以评估封锁退出策略。印度的 R 估计值为 2.08,R 从 3 月 30 日的 1.67 降至 4 月 22 日的 1.16。我们观察到,从封锁放松日期到第二波开始的延迟时间随着第一波高峰后封锁时间的延长而增加;如果封锁逐渐放松,延迟时间会更长。封锁等严格措施可能足以抑制疫情爆发;然而,随着封锁的放松,还需要扩大其他措施。在放宽社交距离的同时增加检测,可以实现与增加检测但不放宽社交距离的严格措施类似的疫情控制效果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c2a/7713576/bc951516299e/gr5_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c2a/7713576/699d55ef9696/gr1_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c2a/7713576/65b3bf24c774/gr2_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c2a/7713576/82e35b423135/gr3_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c2a/7713576/445f077d5211/gr4_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c2a/7713576/bc951516299e/gr5_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c2a/7713576/699d55ef9696/gr1_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c2a/7713576/65b3bf24c774/gr2_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c2a/7713576/82e35b423135/gr3_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c2a/7713576/445f077d5211/gr4_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c2a/7713576/bc951516299e/gr5_lrg.jpg

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