Pathogen Control Engineering Institute, School of Civil Engineering, University of Leeds, Woodhouse Lane, Leeds LS2 9JT, UK.
J R Soc Interface. 2009 Dec 6;6 Suppl 6(Suppl 6):S791-800. doi: 10.1098/rsif.2009.0305.focus. Epub 2009 Oct 7.
Understanding the risk of airborne transmission can provide important information for designing safe healthcare environments with an appropriate level of environmental control for mitigating risks. The most common approach for assessing risk is to use the Wells-Riley equation to relate infectious cases to human and environmental parameters. While it is a simple model that can yield valuable information, the model used as in its original presentation has a number of limitations. This paper reviews recent developments addressing some of the limitations including coupling with epidemic models to evaluate the wider impact of control measures on disease progression, linking with zonal ventilation or computational fluid dynamics simulations to deal with imperfect mixing in real environments and recent work on dose-response modelling to simulate the interaction between pathogens and the host. A stochastic version of the Wells-Riley model is presented that allows consideration of the effects of small populations relevant in healthcare settings and it is demonstrated how this can be linked to a simple zonal ventilation model to simulate the influence of proximity to an infector. The results show how neglecting the stochastic effects present in a real situation could underestimate the risk by 15 per cent or more and that the number and rate of new infections between connected spaces is strongly dependent on the airflow. Results also indicate the potential danger of using fully mixed models for future risk assessments, with quanta values derived from such cases less than half the actual source value.
了解空气传播的风险可为设计安全的医疗保健环境提供重要信息，该环境具有适当水平的环境控制以减轻风险。评估风险最常用的方法是使用 Wells-Riley 方程将感染病例与人体和环境参数联系起来。虽然这是一个简单的模型，可以提供有价值的信息，但原始模型存在一些局限性。本文回顾了最近的一些发展，包括解决模型局限性的方法，例如将其与流行病模型耦合，以评估控制措施对疾病进展的更广泛影响；将其与区域通风或计算流体动力学模拟耦合，以解决实际环境中混合不完美的问题；以及最近在剂量反应建模方面的工作，以模拟病原体与宿主之间的相互作用。本文提出了 Wells-Riley 模型的随机版本，该模型可以考虑医疗环境中相关的小种群的影响，并演示了如何将其与简单的区域通风模型联系起来，以模拟与感染者接近的影响。结果表明，在实际情况下忽略随机效应可能会低估 15%或更多的风险，并且连通空间之间新感染的数量和速度强烈依赖于气流。结果还表明，在未来的风险评估中使用完全混合模型可能存在潜在危险，从这些情况下得出的量子值不到实际源值的一半。
