Respiratory aerosol emissions: characterising the biological content

Non-pharmaceutical interventions played a significant role in reducing the transmission and impact of COVID-19. However, many (e.g. national lockdowns; social restrictions) had a negative impact on mental health, the functioning of society, and the economy. To help inform decision makers, there is a need to better understand the dispersal and spread of respiratory pathogens.

However, carrying out controlled microbiology-based simulation studies is difficult particularly if the target organism is highly pathogenic (e.g. SARS-CoV-2) or is currently unknown (disease X). Non-microbiology-based techniques (e.g. particle counts, high-speed imaging, computational modelling) have been used to risk assess different activities but provide limited information about potential dispersal, spread and/or infectivity. Such limitations, reduce the speed and efficiency of research, hindering pandemic preparedness.

Index organisms originate from the same source as the target pathogen and thus, if detected within a given environment indicate the risk (or likelihood) of that pathogen being present. Studies carried out over 80-years ago highlighted the potential for oral streptococci to be used as index organisms for respiratory pathogens. More recently, the concentration of Streptococcus salivarius in the air of classrooms was shown to be significantly related to the incidence and cross-infection of measles among the children and, during the pandemic, the dispersal of oral streptococci was shown to differ with individual, activity and mitigation strategy.

However, despite the benefits of using normal respiratory microflora as index organisms for respiratory pathogens, the utility and applicability of this approach has not been fully explored. We propose to investigate how respiratory index organisms can be utilised to predict dispersion patterns and the transmissibility of respiratory pathogens of concern, both known and new.