Ho Clifford K
Sandia National Laboratories, P.O. Box 5800, MS-1127, Albuquerque, NM 87185, USA.
Appl Math Model. 2021 Jul;95:297-319. doi: 10.1016/j.apm.2021.02.018. Epub 2021 Feb 24.
An integrated modeling approach has been developed to better understand the relative impacts of different expiratory and environmental factors on airborne pathogen transport and transmission, motivated by the recent COVID-19 pandemic. Computational fluid dynamics (CFD) modeling was used to simulate spatial-temporal aerosol concentrations and quantified risks of exposure as a function of separation distance, exposure duration, environmental conditions (e.g., airflow/ventilation), and face coverings. The CFD results were combined with infectivity models to determine probability of infection, which is a function of the spatial-temporal aerosol concentrations, viral load, infectivity rate, viral viability, lung-deposition probability, and inhalation rate. Uncertainty distributions were determined for these parameters from the literature. Probabilistic analyses were performed to determine cumulative distributions of infection probabilities and to determine the most important parameters impacting transmission. This modeling approach has relevance to both pathogen and pollutant dispersion from expelled aerosol plumes.
受近期新冠疫情的推动，已开发出一种综合建模方法，以更好地理解不同呼气和环境因素对空气传播病原体传播和扩散的相对影响。计算流体动力学（CFD）建模用于模拟时空气溶胶浓度，并根据距离、暴露持续时间、环境条件（如气流/通风）和面部覆盖物等因素量化暴露风险。CFD结果与感染性模型相结合，以确定感染概率，该概率是时空气溶胶浓度、病毒载量、感染率、病毒活力、肺部沉积概率和吸入率的函数。根据文献确定了这些参数的不确定性分布。进行概率分析以确定感染概率的累积分布，并确定影响传播的最重要参数。这种建模方法与呼出气溶胶羽流中的病原体和污染物扩散都相关。
