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上海应对奥密克戎挑战的政策选择。

Policy choices for Shanghai responding to challenges of Omicron.

作者信息

Qian Ying, Cao Siqi, Zhao Laijun, Yan Yuge, Huang Jiaoling

机构信息

Business School, University of Shanghai for Science and Technology, Shanghai, China.

School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

出版信息

Front Public Health. 2022 Aug 9;10:927387. doi: 10.3389/fpubh.2022.927387. eCollection 2022.

DOI:10.3389/fpubh.2022.927387
PMID:36016887
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9395601/
Abstract

BACKGROUND

A new wave of Coronavirus disease 2019 (COVID-19) infection driven by Omicron BA.2 subvariant hit Shanghai end of February 2020. With higher transmissibility and milder symptoms, the daily new confirmed cases have soared to more than 20 K within one and a half months. The greatest challenge of Omicron spreading is that the rapidly surging number of infected populations overwhelming the healthcare system. What policy is effective for huge cities to fight against fast-spreading COVID-19 new variant remains a question.

METHODS

A system dynamics model of the Shanghai Omicron epidemic was developed as an extension of the traditional susceptible-exposed-infected-susceptible recovered (SEIR) model to incorporate the policies, such as contact tracing and quarantine, COVID-19 testing, isolation of areas concerned, and vaccination. Epidemic data from Shanghai Municipal Health Commission were collected for model validation.

RESULTS

Three policies were tested with the model: COVID-19 testing, isolation of areas concerned, and vaccination. Maintaining a high level of COVID-19 testing and transfer rate of the infected population can prevent the number of daily new confirmed cases from recurring growth. In the scenario that 50% of the infected population could be transferred for quarantine on daily bases, the daily confirmed asymptomatic cases and symptomatic cases remained at a low level under 100. For isolation of areas concerned, in the scenario with most isolation scope, the peak of daily confirmed asymptomatic and symptomatic cases dropped 18 and 16%, respectively, compared with that in the scenario with least isolation. Regarding vaccination, increasing the vaccination rate from 75 to 95% only slightly reduced the peak of the confirmed cases, but it can reduce the severe cases and death by 170%.

CONCLUSIONS

The effective policies for Omicron include high level of testing capacity with a combination of RAT and PCR testing to identify and quarantine the infected cases, especially the asymptomatic cases. Immediate home-isolation and fast transfer to centralized quarantine location could help control the spread of the virus. Moreover, to promote the vaccination in vulnerable population could significantly reduce the severe cases and death. These policies could be applicable to all metropolises with huge population facing high transmissible low severity epidemic.

摘要

背景

2022年2月底,由奥密克戎BA.2亚变体引发的新一轮新型冠状病毒肺炎(COVID-19)感染袭击了上海。由于传播性更强且症状较轻,在一个半月内每日新增确诊病例飙升至2万多例。奥密克戎传播带来的最大挑战是,感染人群数量迅速激增,使医疗系统不堪重负。对于大城市而言,何种政策能有效对抗快速传播的COVID-19新变种仍是一个问题。

方法

开发了一个上海奥密克戎疫情的系统动力学模型,作为传统的易感-暴露-感染-易感-康复(SEIR)模型的扩展,纳入了接触者追踪与隔离、COVID-19检测、相关区域隔离和疫苗接种等政策。收集上海市卫生健康委员会的疫情数据用于模型验证。

结果

用该模型测试了三项政策:COVID-19检测、相关区域隔离和疫苗接种。保持高水平的COVID-19检测和感染人群转运率可防止每日新增确诊病例数再次增长。在50%的感染人群能够每日被转运至隔离的情况下,每日确诊的无症状病例和有症状病例保持在100例以下的低水平。对于相关区域隔离,在隔离范围最大的情况下,每日确诊的无症状病例和有症状病例的峰值分别比隔离范围最小的情况下降了18%和16%。关于疫苗接种,将接种率从75%提高到95%仅略微降低了确诊病例的峰值,但可将重症病例和死亡病例减少170%。

结论

应对奥密克戎的有效政策包括具备高水平检测能力,结合快速抗原检测(RAT)和聚合酶链反应(PCR)检测来识别和隔离感染病例,尤其是无症状病例。立即居家隔离并快速转运至集中隔离地点有助于控制病毒传播。此外,推动易感人群接种疫苗可显著减少重症病例和死亡病例。这些政策适用于所有面临高传播性、低严重性疫情的人口众多的大都市。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8757/9395601/466aa1cd8b35/fpubh-10-927387-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8757/9395601/71d5b1c55dc0/fpubh-10-927387-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8757/9395601/73e77840ca8f/fpubh-10-927387-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8757/9395601/94ec30e69952/fpubh-10-927387-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8757/9395601/5fce52ddc748/fpubh-10-927387-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8757/9395601/466aa1cd8b35/fpubh-10-927387-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8757/9395601/71d5b1c55dc0/fpubh-10-927387-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8757/9395601/73e77840ca8f/fpubh-10-927387-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8757/9395601/94ec30e69952/fpubh-10-927387-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8757/9395601/5fce52ddc748/fpubh-10-927387-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8757/9395601/466aa1cd8b35/fpubh-10-927387-g0005.jpg

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