在类似covid - 19的疫情背景下,隔离医院战略是减少社区和医院传播的一种方式。

IF 3.6 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
PLoS Computational Biology Pub Date : 2025-10-03 eCollection Date: 2025-10-01 DOI:10.1371/journal.pcbi.1013548
Théo Pinettes, Quentin J Leclerc, Kévin Jean, Laura Temime
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引用次数: 0

摘要

最近的COVID-19疫情证明,医院内患者和医护人员的院内感染可能在病毒大流行的总体动态中发挥重要作用。控制这种风险的一项战略是,让一些医院专门照顾受感染的病人,由卫生保健员轮流在这些医院连续住院和隔离期间轮班。这一战略已在当地各种环境中实施,并在埃及推广。在这里,我们使用一个耦合医院和社区的数学模型,评估了这一战略对整体流行病动态的影响。我们发现,当社区有有效的控制策略,有症状的卫生工作者遵守自我隔离建议时,隔离医院可以显著减少累积病例数和高峰发病率。我们的研究结果对假定的病毒生物学特性的变化是稳健的,这表明在未来的大流行背景下可以考虑隔离医院策略,以最好地保护整个人群。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The quarantine hospital strategy as a way to reduce both community and nosocomial transmission in the context of a COVID-like epidemic.

Nosocomial infections of both patients and healthcare workers (HCWs) in hospitals may play an important part in the overall dynamics of a viral pandemic, as evidenced by the recent COVID-19 experience. A strategy to control this risk consists in dedicating some hospitals to the care of infected patients only, with HCWs alternating between shifts of continuous stay within these hospitals and periods of isolation. This strategy has been implemented locally in various settings and generalized in Egypt. Here, using a mathematical model coupling hospitals and community, we assess the impact of this strategy on overall epidemic dynamics. We find that quarantine hospitals may significantly reduce the number of cumulative cases, as well as the peak incidence, when effective control strategies are in place in the community and symptomatic HCWs comply with self-isolation recommendations. Our results, which are robust to variations in assumed biological characteristics of the virus, suggest that the quarantine hospital strategy could be considered in future pandemic contexts to best protect the entire population.

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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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