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许多感染都很重要。传播依赖于人体呼吸系统内的微生物以气溶胶的形式释放，这些气溶胶足够小，可以在空气中停留足够长的时间和浓度，被易感者吸入。使用质量平衡模型或CFD模拟的传输模型依赖于关于微生物如何在气溶胶中分布的假设;大多数模型假设分布是均匀的。然而，测量病毒排放的实验数据表明，在较小的气溶胶中可能有更多的病原体。这可能是测量方法造成的，也可能是由于流体、微生物或气溶胶生成机制的特性造成的;对海洋气溶胶的研究和结核病研究的数据表明，浓缩成较小的气溶胶。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。该项目将包括：（一）对已发表的文献进行系统审查，以了解将微生物划分为不同大小的气溶胶的现有证据、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;（二）在专业测试室设施中使用受控微生物气溶胶生成的实验研究，以及对人类来源的气溶胶排放的潜在研究，以及（iii）将围绕微生物分配的不同假设应用到CFD或风险模型中，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.