Mathematical models for assessing the role of airflow on the risk of airborne infection in hospital wards
Mathematical models for assessing the role of airflow on the risk of airborne infection in hospital wards
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DOI：
10.1098/rsif.2009.0305.focus
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发表时间：
2009-12-06
影响因子：
3.9
通讯作者：
Sleigh, P. Andrew
中科院分区：
文献类型：
--
作者：
Noakes, Catherine J.;Sleigh, P. Andrew
了解空气传播的风险可以为设计安全的医疗保健环境提供重要信息，并提供适当的环境控制水平，以降低风险。评估风险的最常见方法是使用Wells-Riley方程将感染病例与人类和环境参数联系起来。虽然它是一个可以产生有价值信息的简单模型，但在其原始表示中使用的模型有许多限制。本文回顾了最近的发展，解决了一些局限性，包括与流行病模型耦合来评估控制措施对疾病进展的更广泛影响，与区域通风或计算流体动力学模拟联系起来，以处理真实环境中的不完全混合，以及最近在剂量-反应模型方面的工作，以模拟病原体与宿主之间的相互作用。提出了威尔斯-赖利模型的随机版本，考虑了与医疗机构相关的小人群的影响，并演示了如何将其与简单的分区通风模型联系起来，以模拟接近感染者的影响。研究结果表明，忽略真实情况中存在的随机效应，可能会低估15%或更多的风险，而连接空间之间新感染的数量和比率在很大程度上取决于气流。结果还表明，在未来的风险评估中使用完全混合模型的潜在危险，这种情况下得出的量子值不到实际源值的一半。
Understanding the risk of airborne transmission can provide important information for designing safe healthcare euvironments 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.