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大流行中预测非药物干预措施有效性的平行进化与控制方法

Parallel evolution and control method for predicting the effectiveness of non-pharmaceutical interventions in pandemics.

作者信息

Huang Hai-Nan, Xie Tian, Chen Wei-Fan, Wei Yao-Yao

机构信息

Present Address: School of Economics, Management and Law, University of South China, Hengyang, 421001 Hunan Province China.

School of Management, Jinan University, Guangzhou, 510632 China.

出版信息

Z Gesundh Wiss. 2023 Feb 21:1-12. doi: 10.1007/s10389-023-01843-2.

DOI:10.1007/s10389-023-01843-2
PMID:36844446
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9942014/
Abstract

AIM

Nonpharmaceutical interventions (NPIs) are important strategies to utilize in reducing the negative systemic impact pandemic disasters have on human health. However, early on in the pandemic, the lack of prior knowledge and the rapidly changing nature of pandemics make it challenging to construct effective epidemiological models that can be used for anti-contagion decision-making.

SUBJECT AND METHODS

Based on the parallel control and management theory (PCM) and epidemiological models, we developed a Parallel Evolution and Control Framework for Epidemics (PECFE), which can optimize epidemiological models according to the dynamic information during the evolution of pandemics.

RESULTS

The cross-application between PCM and epidemiological models enabled us to successfully construct an anti-contagion decision-making model for the early stages of COVID-19 in Wuhan, China. Using the model, we estimated the effects of gathering bans, intra-city traffic blockades, emergency hospitals, and disinfection, forecasted pandemic trends under different NPIs strategies, and analyzed specific strategies to prevent pandemic rebounds.

CONCLUSION

The successful simulation and forecasting of the pandemic showed that the PECFE could be effective in constructing decision models during pandemic outbreaks, which is crucial for emergency management where every second counts.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10389-023-01843-2.

摘要

目的

非药物干预措施是减轻大流行灾害对人类健康产生的负面系统性影响的重要策略。然而,在大流行初期,由于缺乏先验知识以及大流行迅速变化的特性,构建可用于反传染决策的有效流行病学模型具有挑战性。

对象与方法

基于并行控制与管理理论(PCM)和流行病学模型,我们开发了一种流行病并行演化与控制框架(PECFE),它可以根据大流行演变过程中的动态信息优化流行病学模型。

结果

PCM与流行病学模型的交叉应用使我们成功构建了中国武汉新冠肺炎早期阶段的反传染决策模型。利用该模型,我们估计了聚集禁令、市内交通封锁、应急医院和消毒的效果,预测了不同非药物干预措施策略下的大流行趋势,并分析了防止大流行反弹的具体策略。

结论

对大流行的成功模拟和预测表明,PECFE在大流行爆发期间构建决策模型方面可能是有效的,这对于分秒必争的应急管理至关重要。

补充信息

在线版本包含可在10.1007/s10389-023-01843-2获取的补充材料。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e55/9942014/0662359c4075/10389_2023_1843_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e55/9942014/e483bef28202/10389_2023_1843_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e55/9942014/b63d1b9f22ca/10389_2023_1843_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e55/9942014/68f4776bd59c/10389_2023_1843_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e55/9942014/62d8542ef511/10389_2023_1843_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e55/9942014/0662359c4075/10389_2023_1843_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e55/9942014/e483bef28202/10389_2023_1843_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e55/9942014/b63d1b9f22ca/10389_2023_1843_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e55/9942014/68f4776bd59c/10389_2023_1843_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e55/9942014/62d8542ef511/10389_2023_1843_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e55/9942014/0662359c4075/10389_2023_1843_Fig5_HTML.jpg

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本文引用的文献

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