Distribution of microbial pathogens into aerosol and the implications for airborne infection transmission

空气传播对包括结核病、麻疹、新冠肺炎和流感在内的一些感染很重要。传播依赖于人类呼吸系统内的微生物在气雾剂中释放，这些气雾剂足够小，足以在空气中停留足够的时间和浓度，供易感人群吸入。使用质量平衡模型或CFD模拟的传播模型依赖于关于微生物在气溶胶中如何分布的假设；大多数模型假设按体积分布是均匀的。然而，测量病毒排放的实验数据表明，较小的气溶胶中可能存在更多的病原体。这可能是测量方法的人为因素，也可能是流体、微生物的性质或气溶胶产生机制的结果；有关于海洋气溶胶的研究和结核研究的数据表明，富集到较小的气溶胶中。该项目旨在探索这一现象，并评估其对传播风险建模的影响。该项目将涉及(1)系统审查已发表的文献，以了解微生物被划分为不同气溶胶大小的当前证据、微生物气溶胶的测量方法和呼吸气溶胶生成的流体动力学；(2)在专业试验室设施中使用受控生成微生物气溶胶的实验研究，以及可能对人类来源的气溶胶排放进行的研究，以及(3)将关于微生物分配的不同假设应用于CFD或风险模型，以探索这将如何影响预测的感染风险。

Airborne transmission is important for a number of infections including TB, measles, Covid-19 and influenza. Transmission relies on microorganisms within the human respiratory system to be released in aerosols that are small enough to remain airborne for sufficient time and concentration to be inhaled by a susceptible person. Transmission models using either mass balance models or CFD simulations rely on assumptions about how microorganisms are distributed in aerosol; most models assume distribution is uniform by volume. However experimental data measuring viral emissions suggests that there may be proportionally more pathogens in the smaller aerosols. This may be an artefact of measurement methods, or it may result from the properties of the fluid, microorganism or mechanisms for aerosol generation; there are studies of sea aerosols and data from TB studies that suggests enrichment into smaller aerosols. This project aims to explore this phenomena and to evaluate the implications for modelling transmission risks. The project will involve (i) systematic review of published literature to understand the current evidence for partitioning of microorganisms into different aerosol sizes, measurement approaches for microbial aerosols and the fluid dynamics of respiratory aerosol generation (ii) experimental studies using controlled generation of microbial aerosols in a specialist test chamber facility and potentially studies of aerosol emissions from human sources, and (iii) applying different assumptions around microbial partitioning into CFD or risk models to explore how this would influence the predicted risk of infection.