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.

在最近的 COVID-19 大流行的推动下，已经开发出一种综合建模方法，以更好地了解不同呼气和环境因素对空气传播病原体运输和传播的相对影响。计算流体动力学 (CFD) 建模用于模拟时空气溶胶浓度，并将暴露风险量化为分离距离、暴露持续时间、环境条件（例如气流/通风）和面罩的函数。CFD 结果与传染性模型相结合以确定感染概率，这是时空气溶胶浓度、病毒载量、传染率、病毒活力、肺部沉积概率和吸入率的函数。从文献中确定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。这种建模方法与排出的气溶胶羽流中的病原体和污染物扩散有关。