An agent-based model to simulate the transmission dynamics of bloodborne pathogens within hospitals.
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...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-02-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012850 |
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| author | Paul Henriot Mohamed El-Kassas Wagida Anwar Samia A Girgis Maha El Gaafary Kévin Jean Laura Temime |
| author_facet | Paul Henriot Mohamed El-Kassas Wagida Anwar Samia A Girgis Maha El Gaafary Kévin Jean Laura Temime |
| author_sort | Paul Henriot |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-aa31fc9bcdc8461489a697f876e5ad09 |
| institution | OA Journals |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-aa31fc9bcdc8461489a697f876e5ad092025-08-20T01:50:31ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582025-02-01212e101285010.1371/journal.pcbi.1012850An agent-based model to simulate the transmission dynamics of bloodborne pathogens within hospitals.Paul HenriotMohamed El-KassasWagida AnwarSamia A GirgisMaha El GaafaryKévin JeanLaura TemimeMathematical 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.https://doi.org/10.1371/journal.pcbi.1012850 |
| spellingShingle | Paul Henriot Mohamed El-Kassas Wagida Anwar Samia A Girgis Maha El Gaafary Kévin Jean Laura Temime An agent-based model to simulate the transmission dynamics of bloodborne pathogens within hospitals. PLoS Computational Biology |
| title | An agent-based model to simulate the transmission dynamics of bloodborne pathogens within hospitals. |
| title_full | An agent-based model to simulate the transmission dynamics of bloodborne pathogens within hospitals. |
| title_fullStr | An agent-based model to simulate the transmission dynamics of bloodborne pathogens within hospitals. |
| title_full_unstemmed | An agent-based model to simulate the transmission dynamics of bloodborne pathogens within hospitals. |
| title_short | An agent-based model to simulate the transmission dynamics of bloodborne pathogens within hospitals. |
| title_sort | agent based model to simulate the transmission dynamics of bloodborne pathogens within hospitals |
| url | https://doi.org/10.1371/journal.pcbi.1012850 |
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