Transient models to assess transmission and control of airborne infection risks in a respiratory ward

评估呼吸病房空气传播感染风险的传播和控制的瞬态模型

• 批准号: 2438520
• 金额: --
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2438520
• 关键词: Transient; models; assess; transmission; control

Airborne transmission is an infection route for many diseases including influenza, COVID-19 and opportunist pathogens in hospitals. Quantifying risks are necessary to determine appropriate control strategies, both in terms of engineering approaches such as ventilation, and management strategies such as treating, locating and scheduling hospital patients. However airborne transmission is complex to evaluate as it requires understanding of the airflows, infection dynamics and human-environment interactions. The COVID-19 pandemic has shown that we have significant lack of knowledge in this area, particularly around the role of airflows in managing different sizes of airborne particles and the effectiveness of different solutions. Previous studies have developed models to link airflow and infection dynamics to assess the hospital environment, including looking at the influence of airflow patterns in multi-room environments and stochastic effects in small populations. However current models generally assume that both the infection parameters and airflows are steady-state as the outbreak evolves in time. This project aims to explore the influence of transient effects in both time and space to evaluate how short term events can influence individual risk and the overall dynamics of an outbreak. To this aim, we will combine transient airflow models from CFD simulations with stochastic infection dynamics models. The models will be applied to the respiratory wards at St James's Hospital in Leeds, with the objective of evaluating the effectiveness of interventions, including application of air cleaning devices.

空气传播是包括流感、COVID-19和医院机会性病原体在内的许多疾病的感染途径。量化风险对于确定适当的控制策略是必要的，无论是在通风等工程方法方面，还是在治疗、定位和安排医院病人等管理策略方面。然而，评估空气传播是复杂的，因为它需要了解气流、感染动力学和人与环境的相互作用。2019冠状病毒病大流行表明，我们在这一领域的知识严重缺乏，特别是在气流在管理不同大小的空气传播颗粒方面的作用以及不同解决方案的有效性方面。先前的研究已经建立了将气流和感染动力学联系起来的模型，以评估医院环境，包括观察多室环境中气流模式的影响和小人群中的随机效应。然而，目前的模型通常假设感染参数和气流随着疫情的发展都是稳态的。该项目旨在探索瞬时效应在时间和空间上的影响，以评估短期事件如何影响个体风险和爆发的整体动态。为此，我们将结合CFD模拟的瞬态气流模型和随机感染动力学模型。这些模型将应用于利兹圣詹姆斯医院的呼吸病房，目的是评估干预措施的有效性，包括空气净化装置的应用。