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As worldwide evidence shows that the predominant transmission route of SARS-CoV-2 and other respiratory pathogens is airborne, the need for suitable methods for the sampling of bioparticles directly from the air is more urgent than ever. The present paper describes the development of a method for the collection of biological aerosols, using a preexisting cyclonic impinger, the Coriolis μ, combined with a lysis buffer and subsequent qPCR analysis of the generated samples in lab. Four phases of method development are described: exploratory, validation, blank tests, and application. The application phase consisted of a field experiment in which the method was simultaneously applied at two daycare facilities. The method achieved a good level of accuracy and reliability in detecting different types of infectious agents in the air, with a global uncertainty of 19.6%. Furthermore, our method allows the simultaneous detection of 26 different respiratory pathogens in air samples, it is relatively simple, and the equipment is easy to use. Additionally, the time to collect a representative sample is short compared to other methods. The method does not cause significant disturbance to those present in the sampled rooms, and it is safe for operators and flexible, meaning it can be used in virtually any environment regardless of use, size, or occupancy. Further research is being developed to allow quantitative analysis of the collected samples and to test the methods' ability to assess the viability of the microorganisms collected in the sample. [ABSTRACT FROM AUTHOR]

Current models to determine the risk of airborne disease infection are typically based on a backward quantification of observed infections, leading to uncertainties, e.g., due to the lack of knowledge whether the index person was a superspreader. In contrast, the present work presents a forward infection risk model that calculates the inhaled dose of infectious virus based on the virus emission rate of an emitter and a prediction of Lagrangian particle trajectories using CFD, taking both the residence time of individual particles and the biodegradation rate into account. The estimation of the dose-response is then based on data from human challenge studies. Considering the available data for SARS-CoV-2 from the literature, it is shown that the model can be used to estimate the risk of infection with SARS-CoV-2 in the cabin of a Do728 single-aisle aircraft. However, the virus emission rate during normal breathing varies between different studies and also by about two orders of magnitude within one and the same study. A sensitivity analysis shows that the uncertainty in the input parameters leads to uncertainty in the prediction of the infection risk, which is between 0 and 12 infections among 70 passengers. This highlights the importance and challenges in terms of superspreaders for risk prediction, which are difficult to capture using standard backward calculations. Further, biological inactivation was found to have no significant impact on the risk of infection for SARS-CoV-2 in the considered aircraft cabin. [ABSTRACT FROM AUTHOR]