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

IF 3.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS PLoS Computational Biology Pub Date : 2025-02-24 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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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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