An agent-based model to simulate the transmission dynamics of bloodborne pathogens within hospitals.

IF 3.6 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS PLoS Computational Biology Pub Date : 2025-02-24 eCollection Date: 2025-02-01 DOI:10.1371/journal.pcbi.1012850
Paul Henriot, Mohamed El-Kassas, Wagida Anwar, Samia A Girgis, Maha El Gaafary, Kévin Jean, Laura Temime
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Abstract

Mathematical models are powerful tools to analyze pathogen spread and assess control strategies in healthcare settings. Nevertheless, available models focus on nosocomial transmission through direct contact or aerosols rather than through blood, even though bloodborne pathogens remain a significant source of iatrogenic infectious risk. Herein, we propose an agent-based SEI (Susceptible-Exposed-Infected) model to reproduce the transmission of bloodborne pathogens dynamically within hospitals. This model simulates the dynamics of patients between hospital wards, from admission to discharge, as well as the dynamics of the devices used during at-risk invasive procedures, considering that patient contamination occurs after exposure to a contaminated device. We first illustrate the use of this model through a case study on hepatitis C virus (HCV) in Egypt. Model parameters, such as HCV upon-admission prevalence and transition probabilities between wards or ward-specific probabilities of undergoing different invasive procedures, are informed with data collected in Ain Shams University Hospital in Cairo. Our results suggest a low risk of HCV acquisition for patients hospitalized in this university hospital. However, we show that in a low-resource hospital, frequent device shortages could lead to increased risk. We also find that systematically screening patients in a few selected high-risk wards could significantly reduce this risk. We then further explore potential model applications through a second illustrative case study based on HBV nosocomial transmission in Ethiopia. In the future, this model could be used to predict the potential burden of emerging bloodborne pathogens and help implement effective control strategies in various hospital contexts.

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一个基于主体的模型来模拟医院内血源性病原体的传播动态。
数学模型是分析病原体传播和评估医疗机构控制策略的有力工具。尽管如此,尽管血源性病原体仍然是医源性感染风险的重要来源,但现有的模型侧重于通过直接接触或气溶胶而不是通过血液传播的医院传播。在此,我们提出了一个基于agent的SEI(易感-暴露-感染)模型来动态再现医院内血源性病原体的传播。该模型模拟了医院病房之间患者从入院到出院的动态变化,以及在有风险的侵入性手术中使用的设备的动态变化,考虑到患者在暴露于受污染的设备后会受到污染。我们首先通过对埃及丙型肝炎病毒(HCV)的案例研究来说明该模型的使用。模型参数,如HCV在入院时的患病率和病房之间的转移概率或接受不同侵入性手术的病房特定概率,由开罗Ain Shams大学医院收集的数据提供。我们的研究结果表明,在该大学医院住院的患者感染HCV的风险较低。然而,我们表明,在资源匮乏的医院,频繁的设备短缺可能导致风险增加。我们还发现,系统地筛选少数高危病房的患者可以显著降低这种风险。然后,我们通过基于埃塞俄比亚HBV医院传播的第二个说明性案例研究进一步探索潜在的模型应用。在未来,该模型可用于预测新出现的血源性病原体的潜在负担,并有助于在各种医院环境中实施有效的控制策略。
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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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