中国北京SARS-CoV-2 Omicron变体防控模型模拟

IF 4.4 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Shihao Liang , Tianhong Jiang , Zengtao Jiao , Zhengyuan Zhou
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引用次数: 2

摘要

目的2022年,严重急性呼吸系统综合征冠状病毒2型的奥密克戎变异株将取代此前在世界各地传播的变异株。奥密克戎变异株在中国的零星暴发引发了人们对如何正确应对2019冠状病毒病(新冠肺炎)的关注,我们开发了一个改进的SEPIR模型,以数学模拟定制的动态新冠清零策略和奥密克戎疫情的传播。为了证明疫情控制期间动态变化政策部署的有效性,我们根据政策变化日期将传播率修改为四部分,即4月22日至5月2日、5月3日至11日,5月12日至21日、5日至6月8日,并采用马尔可夫链蒙特卡罗(MCMC)来估计不同的传播率。然后,我们改变了这些措施的时间和规模,以了解这些政策对奥密克戎变异株的有效性。结果四个部分的估计有效繁殖数分别为1.75(95%CI 1.66-1.85)、0.89(95%CI 0.79-0.99)、1.15(95%CI 1.05-1.26)和0.53(95%CI 0.48-0.60)。在实验中,我们发现,截至6月8日,如果5月3日不实施政策,累计病例将上升至132609例(95%置信区间59667–250639),是观察到的累计病例数1807的73.39倍,而如果5月22日不实施策略,累计病例数将上升至3235例(95%可信区间1909–4954),增加79.03%。政策实施延迟3天将导致累计病例增加58.28%,延迟7天将导致累积病例增加187.00%。另一方面,提前3或7天采取控制措施只会导致实际累计病例减少38.63%或68.62%。如果在5月3日前3天实施封锁,累计病例将为289例(95%置信区间211–378),减少84%,累计病例为853例(95%可信区间578–1183),减少52.79%。结论动态清零策略可能能够有效地将传播规模降至最低,缩短流行期,减少感染总数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A model simulation on the SARS-CoV-2 Omicron variant containment in Beijing, China

A model simulation on the SARS-CoV-2 Omicron variant containment in Beijing, China

A model simulation on the SARS-CoV-2 Omicron variant containment in Beijing, China

A model simulation on the SARS-CoV-2 Omicron variant containment in Beijing, China

Objective

The Omicron variant of SARS-COV-2 is replacing previously circulating variants around the world in 2022. Sporadic outbreaks of the Omicron variant into China have posed a concern how to properly response to battle against evolving coronavirus disease 2019 (COVID-19).

Methods

Based on the epidemic data from website announced by Beijing Center for Disease Control and Prevention for the recent outbreak in Beijing from April 22nd to June 8th in 2022, we developed a modified SEPIR model to mathematically simulate the customized dynamic COVID-zero strategy and project transmissions of the Omicron epidemic. To demonstrate the effectiveness of dynamic-changing policies deployment during this outbreak control, we modified the transmission rate into four parts according to policy-changing dates as April 22nd to May 2nd, May 3rd to 11st, May 12th to 21st, May 22nd to June 8th, and we adopted Markov chain Monte Carlo (MCMC) to estimate different transmission rate. Then we altered the timing and scaling of these measures used to understand the effectiveness of these policies on the Omicron variant.

Results

The estimated effective reproduction number of four parts were 1.75 (95% CI 1.66–1.85), 0.89 (95% CI 0.79–0.99), 1.15 (95% CI 1.05–1.26) and 0.53 (95% CI 0.48 -0.60), respectively.  In the experiment, we found that till June 8th the cumulative cases would rise to 132,609 (95% CI 59,667–250,639), 73.39 times of observed cumulative cases number 1,807 if no policy were implemented on May 3rd, and would be 3,235 (95% CI 1,909 - 4,954), increased by 79.03% if no policy were implemented on May 22nd. A 3-day delay of the implementation of policies would led to increase of cumulative cases by 58.28% and a 7-day delay would led to increase of cumulative cases by 187.00%. On the other hand, taking control measures 3 or 7 days in advance would result in merely 38.63% or 68.62% reduction of real cumulative cases. And if lockdown implemented 3 days before May 3rd, the cumulative cases would be 289 (95% CI 211–378), reduced by 84%, and the cumulative cases would be 853 (95% CI 578–1,183), reduced by 52.79% if lockdown implemented 3 days after May 3rd.

Conclusion

The dynamic COVID-zero strategy might be able to effectively minimize the scale of the transmission, shorten the epidemic period and reduce the total number of infections.

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来源期刊
Intelligent medicine
Intelligent medicine Surgery, Radiology and Imaging, Artificial Intelligence, Biomedical Engineering
CiteScore
5.20
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