The generation and dispersal of infectious respiratory particles - using respiratory and oral commensal bacteria as index organisms for respiratory pathogens

Assessing the Generation and Dispersal of Respiratory Particles Using Upper Respiratory Commensal Bacteria as Index Organisms for Respiratory Pathogens | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessing the Generation and Dispersal of Respiratory Particles Using Upper Respiratory Commensal Bacteria as Index Organisms for Respiratory Pathogens Patricia Barkoci, Wilhemina D'Costa, Neville Q Verlander, Nicola Yaxley, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5202589/v2 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Dec, 2025 Read the published version in Scientific Reports → Version 2 posted You are reading this latest preprint version Show more versions Abstract 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 or is currently unknown. An alternative approach is to use commensal upper respiratory tract (URT) bacteria as index organisms. Healthy volunteers were asked to perform defined respiratory activities within a purpose-built flexible-film isolator. The dispersal of large (L-RPs) and small respiratory particles (S-RPs) was assessed via passive (settle plates) and active air sampling, respectively. Activities were carried out with or without a face covering. Dispersal of respiratory particles differed with individual. During speech, increased vocal volume was associated with a significant increase in the number of URT bacteria deposited as L-RPs and captured by air samplers (S-RPs). Disposable (IIR) medical face masks significantly reduced deposition of L-RPs and reduced the odds of recovering higher numbers of S-RPs by 87% (CI: 63%, 96%). Transparent face coverings were as effective as (IIR) face masks, but face shields had limited efficacy, particularly when worn by individuals who dispersed high numbers of IRPs. Commensal URT bacteria can be used to study the risk of respiratory transmission in the absence of the target pathogen. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction The urgency of the COVID-19 pandemic highlighted knowledge gaps among the scientific community regarding the transmission of respiratory pathogens. This, in turn, catalysed a surge of research activity and a substantial expansion of scientific literature. During the early phases of the pandemic, approximately 10,000 COVID-19-related articles were submitted to scientific journals each month [ 1 ]. However, pivotal questions regarding the mechanisms of respiratory particle generation and dispersal, including the potential for ‘super-spreaders’ and for ‘super-spreading’ events (i.e. respiratory activities that could facilitate transmission) continue to warrant investigation. Dispersal of respiratory pathogens can occur when particles containing potentially infectious organisms are expelled from the upper and lower respiratory tracts of infected individuals. These ‘infectious respiratory particles’ (IRPs) can travel through the air and infect individuals via inhalation (Supplementary Figs. 1a and 1b) or by directly depositing onto facial mucosal membranes (Supplementary Fig. 1c). The deposition of IRPs on hands and/or surfaces could also lead to direct (Supplementary Fig. 1d) or indirect (Supplementary Fig. 1e) fomite transmission [ 2 ] The risk of IRP transmission depends on a complex interplay between environmental conditions (e.g., temperature, relative humidity, airflow), the physicochemical characteristics of the pathogen, social behaviour, and mitigation strategy. The activity by which IRPs are expelled by the individual is also important. Expiratory activity has been shown to affect the size [ 3 ] and the concentration of IRPs and the distance they travel [ 4 ]. Previous studies have monitored the number, mass, and size distribution of the particles exhaled by (usually) healthy study participants to risk assess different activities, including singing [ 5 – 8 ], the playing of musical instruments [ 9 – 11 ], loud talking [ 4 , 12 ], and sneezing [ 13 , 14 ]. The effectiveness of face coverings in mitigating the transmission of IRPs has also been assessed across a range of expiratory activities [ 15 – 18 ]. Non-microbiological techniques, such as those used in the aforementioned studies, allow different activities and mitigation strategies to be directly compared. However, studies must be carried out within an environment with a very low (near-zero) background particle concentration [ 19 ] and sampling must be carried out as close to the source as possible, providing limited information about potential dispersal and spread. They also provide limited information about potential infectivity – if, for example, the particles detected carry (or could carry) the target organism. Pathogen-based studies using naturally infected participants have demonstrated the dispersal of respiratory pathogens during expiratory activities such as coughing [ 20 ], talking [ 21 ] or breathing [ 22 , 23 ], and have confirmed the utility of face coverings in mitigating this spread. Whilst these studies provide crucial validation for the role of expiratory activities and source control measures, they are technically and ethically challenging. An alternative approach is to utilise index organisms. For the purposes of this study, we define index organisms as microorganisms originating from the same anatomical source as a given pathogen, such that their detection within a specific environment indicates the potential presence or risk of that pathogen. To assess the risk of IRP transmission in situ without involving infected individuals, previous studies have utilized oral streptococci as key index organisms. Duguid [ 24 ] and Hamburger and Robertson [ 25 ] investigated the dispersal of oral streptococci during respiratory activities such as talking, coughing, and sneezing. Bennett et al [ 26 ] utilised airborne streptococcal counts to evaluate the occupational risk associated with dental procedures, whilst Reid et al. [ 27 ] and Torrey and Lake [ 28 ] established a correlation between the concentration of oral streptococci in the air and the incidence of measles and the common cold, respectively. The oral cavity and upper- and lower respiratory tracts share highly similar microbial communities in healthy individuals [ 29 ], and studies utilising molecular [ 30 ] and culture-based assays [ 31 , 32 ] have shown that respiratory pathogens such as SARS-CoV-2 are frequently present in saliva and/or the upper respiratory tract (URT). We hypothesize that URT bacteria, which are readily detected in oral fluids, could serve as effective proxies (index organisms) for studying the dispersal of respiratory pathogens. Such an approach would mitigate some of the ethical and technical challenges posed by direct pathogen detection and allow the risk of respiratory transmission to be studied in the absence of a target pathogen. To demonstrate the utility of this method, we designed a bespoke flexible-film isolator to examine the generation and dispersal of URT bacteria in relation to individual, respiratory activity and mitigation strategy, specifically the effectiveness of face coverings. Results were validated against methodologically more complex studies published in the literature. Materials and Methods Isolator for Measuring Aerosol and Droplet GENerationN (IMADGENN) IMADGENN is a purpose-built flexible film isolator (PFI Systems Ltd, Milton Keynes, UK) designed to capture respiratory particles generated by study participants (Fig. 1 ). The isolator measures 0.5 m (w) x 1.5 m (l) x 0.6 m (h) and is supplied with HEPA-filtered air. The isolator is fastened to a stand (increasing the overall height of IMADGENN to 1.7 m), and non-gas-tight zips allow panels located at the front and the side of the isolator to be opened, facilitating both study participation and the manipulation of sampling equipment. A second HEPA filter (with an efficiency of 99.995% at 0.3 µm) attached to the extract system allows rapid removal of airborne particles and ensures a low level of background aerosol. With the extraction fan operating, the air flow through IMADGENN is 1.72 m 3 /min (280 air changes/hour). Participants and experimental setup This study was approved by the UK Health Security Agency (Public Health England at the time) Research Ethics and Governance Group (reference number R&D 406). All experiments were performed in accordance with relevant guidelines and regulations. Informed consent was obtained from all volunteers prior to participating in the study, when they were also asked to provide their age. No other personally identifiable information was collected, and none was stored. This study was designed as a proof-of-concept, exploratory investigation to demonstrate the feasibility of using upper respiratory tract (URT) bacteria as proxies for respiratory pathogen dispersal. As such, no formal a priori sample-size calculation was performed. Cohort sizes were determined based on the availability of volunteers and operational constraints (Cohort 1, n = 16; Cohort 2, n = 15). The study is primarily hypothesis-generating and intended to inform the design of future, fully powered investigations. Prior to participating in the study, healthy volunteers were asked to self-test for COVID-19 using a lateral flow device and report a negative test result. All study participants provided a saliva sample immediately before completing the experiment to confirm the presence and concentration of URT bacteria. Before commencing each experimental run, the air filtration system of IMADGENN was operated for three minutes to remove any residual particles. Columbia Blood agar plates (CBA; E & O Laboratories Ltd, Bonnybridge, UK) were positioned inside the isolator at 10 cm intervals (up to 1 meter) to the left, centre and right of the isolator (Fig. 1 ). An active air sampler, an R2S slit-to-agar sampler (Emtek LLC,Longmont, US) operating at a flow rate of 17 or 28.3 litres min − 1 and collecting onto CBA, was positioned inside the isolator 1.2 meters from the participant. The lids of the settle plates were removed, and the isolator was secured by zipping up both the side and front panels. The air filtration system continued to operate for at least three minutes to remove particles introduced by the investigator. It was then switched off. Each participant was positioned directly in front of IMADGENN and was instructed to adjust the chair so that their face was centred adjacent to the front panel (Fig. 1 (a)) and approximately 30 cm from the top of the isolator. The air sampler was operated remotely, and the participant was directed to open the panel and to perform the specified activity. Each experimental run included a fallow period when, following completion of the respiratory activity and with the front panel closed, the air sampler continued to operate. This was to ensure that particles remaining in the air were captured. The air filtration system of the isolator was then switched on and allowed to run for five minutes before the side panel was opened and the agar plates collected and replaced. Respiratory activities For the purposes of this study, URT bacteria that deposited on surfaces (settle plates) were referred to as large respiratory particles (L-RPs), while URT bacteria that remained suspended in the air and captured by the air sampler were termed small IRPs (S-RPs). Here, the term ‘dispersal’ refers specifically to the release or shedding of L-RPs and S-RPs from participants during respiratory activities. It does not refer to the subsequent transport or movement of these particles in the environment. Assessing the impact of increased vocal effort (loudness; cohort 1) Volunteers aged between 21 and 60 years (n = 16; 8 female) were asked to count from one to 100 in their ‘normal’ conversational voice. They were then asked to repeat the activity whilst shouting as loud as was comfortable at a sustained amplitude. No face covering was worn. A decibel (dB) meter (Cirrus Research Plc, Hunmanby, UK) was placed inside the isolator (0.8 meters from the participant) and used to monitor sound level. The mean, minimum, and maximum dB level was recorded. Each experimental run, including the fallow period, was four minutes. Assessing the effectiveness of face coverings in reducing dispersal (cohort 2) Volunteers aged between 20 and 65 years (n = 15; 7 female) were asked to perform a series of respiratory activities, specifically: to cough three times, count from one to 100, exhale through the mouth, sing “Happy Birthday”, exhale through the nose, count from one to 100, and cough three times again. Each participant carried out the same series of respiratory activities when wearing and when not wearing a face covering. Each experimental run, including the fallow period, was 15 minutes. Culture and identification of URT bacteria Each saliva sample was serially diluted (10-fold) in sterile water, and aliquots (100µl) of the 10 − 5 and 10 − 6 dilutions were cultured in duplicate on CBA. All saliva samples were then disposed of, and all CBA plates, including those removed from IMADGENN, were incubated at 37°C for 48 hours. After incubation, bacterial colonies were counted and presumptive URT bacteria identified by matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (Bruker Daltonik MALDI biotyper; Bruker, Bremen, Germany) using the extended direct transfer method. Data analysis Descriptive and non-parametric analysis was performed using GraphPad Prism 9 or R (version 4.3.3). Further data manipulation and inferential analysis were performed in STATA, version 17.0. As each participant contributed multiple measurements, mixed effects models were used, with participant as the random effect and the remaining variables as the fixed effects. Following the conclusion of the modelling process, if the random effect was practically indistinguishable from zero, it was removed, after which the measures of effect (odds ratio (OR) or relative risk ratio (RRR), as appropriate), together with their 95% confidence intervals (CIs), were obtained. Where possible, the likelihood ratio test was used to obtain p-values; otherwise, the p-values were obtained by means of the Wald test. Statistical significance level was taken to be 5%. Ordinal logistic regression was used, fitting a generalised ordinal logistic regression model at the end of the modelling process, when the test for proportionality of odds as implemented by Williams [ 33 ] or, where not possible, the approximate likelihood ratio test, as described by Wolfe and Gould [ 34 ] indicated a possible violation of the assumption. The amount of data available determined the analysis strategy. This and the modelling procedure are described in full in the supplementary material. Results Index organisms for respiratory pathogens: Commensal URT bacteria in saliva and respiratory particles The mean concentration of URT bacteria recovered from the saliva of the participants ranged from 3 x 10 6 to 2.9 x 10 9 CFU/ml (cohort 1) and from 1.5 x 10 7 to 2.5 x 10 8 CFU/ml (cohort 2). Neisseria spp (comprising species flavescence, perflava, subflava, mucosa, elongata, macacae ) and Streptococcus spp ( cristatus, sanguinis, parasanguinis, mitis, salivarius, oralis, pneumoniae, australis, infantis, vestibularis, gordonii ) were the most common URT bacteria present both in saliva and in the respiratory particles produced by all participants. Other predominant URT commensals were Rothia spp ( aeria, mucilaginosa, dentocariosa ), Actinomyces spp ( naeslundii, oris, odontolyticus ), Gemella spp ( haemolysans, sanguinis ), Haemophilus parainfluenzae , and Moraxella spp. The impact of increased vocal effort (loudness) on the dispersal of IRPs Large respiratory particles (L-RPs) When volunteers (n = 16) were instructed to count from 1 to 100 in their normal conversational voice, the mean dB level measured within IMADGENN was 75 dB, and the number of URT bacteria deposited on settle plates within 0.5 m from the source ranged from 1 to 48 colony-forming units (CFU) with a median of 13 CFU (Fig. 3 a). When the volunteers were instructed to repeat the counting while ‘shouting’, the mean dB level recorded was 92 dB, equating to an increase of 15 dB and 17 dB for males and females, respectively. This increased vocal effort resulted in a 15-fold increase in the median number of URT bacteria deposited as L-RPs (range: 36 CFU − 1251 CFU (median 197 CFU); Fig. 3 a). Deposition significantly decreased with increasing distance from the source (p < 0.001; Fig. 4 ). Small respiratory particles (S-RPs) Without considering other factors (univariate analysis), increased vocal effort (‘shouting’) was associated with a significant increase in the number of URT bacteria captured by the air sampler (S-RPs; p = 0.01) and a significant linear association between mean dB level and the concentration of URT bacteria recovered as S-RPs. Some individuals dispersed comparatively more S-RPs (Fig. 3 b). However, after adjusting for age, activity, and height, the odds of recovering a higher number of URT bacteria as S-RPs increased by 34% for every dB increase in mean voice level (95% CI: 1.06, 1.70; p = 0.003). The effectiveness of face coverings in reducing the dispersal of IRPs When volunteers (n = 15) were asked to perform a series of respiratory activities without a face covering, the median number of URT bacteria recovered as L-RPs and deposited within 1 m from the source was 84 CFU (range: 6 CFU – 978 CFU). Carrying out the same series of respiratory activities whilst wearing a disposable (IIR) medical face mask reduced the median number of URT bacteria deposited as L-RPs to 1 CFU (range: 0 CFU to 6 CFU; p = < 0.004, Fig. 5 a) and reduced the odds of detecting higher numbers of S-RPs (1.2 m from source) by 87% (95% CI: 63%, 96%; Fig. 5 b). When volunteers wearing a face shield carried out the same series of respiratory activities, the median number of URT bacteria deposited within 1 m from the source was 54 CFU (range: 0–769 CFU). In contrast, when a reusable clear window mask, a ClearMask™ or a Panoramic Mio-Mask (Fig. 2 ) was worn, the median number of URT bacteria recovered was ≤ 2 CFU (range: 0–11 CFU) suggesting that wearing any of these transparent face coverings (TFCs) would be as effective as a (IIR) medical face mask in preventing dispersal of L-RPs (Fig. 6 a). Wearing a face shield increased the odds of capturing higher numbers of URT bacteria as S-RPs but not significantly (OR 2.62, CI: 0.51, 13.5). In contrast, wearing any of the TFCs significantly reduced the odds of detecting higher numbers of S-RPs. The reusable clear window mask was the most effective, significantly reducing the odds by 99% (CI: 91% − 100%) (Fig. 6 b). Inter-individual variability in dispersal of IRPs In cohort 1 (n = 16), individuals with elevated (log) concentrations of URT bacteria in their saliva had increased odds of dispersing higher numbers of L-RPs (95% CI: 1.22, 2.03). However, no significant association was observed with S-RPs. Gender had no significant impact on the dispersal of S-RPs, but after adjusting for height, activity, and dB level, the odds of recovering higher numbers of S-RPs increased by 9% for every year increase in age (95% CI: 1.00-1.19). Older individuals also had significantly higher odds of dispersing higher numbers of L-RPs. In cohort 2 (n = 15), we observed a non-significant trend toward a U-shaped association between the (log) salivary concentration of URT bacteria and dispersal of L-RPs (p = 0.08). There was also a significant U-shaped association between age and the odds of detecting higher numbers of S-RPs. In this cohort, the number of L-RPs generated by study participants again differed with gender, with the male participants dispersing seven times more URT bacteria than the females (Supplementary Fig. 2). Two of the male participants generated 66% of all the URT bacteria deposited as L-RPs and 81% of the URT bacteria captured as S-RPs (Fig. 7 ). When these were removed as outliers or ‘super-emitters’, and by univariant analysis only, the remaining male participants still dispersed significantly more URT bacteria than the females (p = 0.04). Discussion Investigating respiratory pathogen transmission presents numerous challenges. Mathematical modelling has been used to better understand transmission patterns of respiratory illness [ 35 , 36 ] but lacks direct comparability to the complexities of living systems. In vivo models, which often involve animals [ 37 ] or human challenge [ 38 ] are expensive and subject to lengthy ethical approvals. We propose using upper respiratory tract (URT) bacteria as index organisms for respiratory pathogens. Such an approach overcomes many of the limitations associated with pathogen-based studies and non-microbiological methods of assessing the spread and dispersal of IRPs, and provides a practicable, ethical, and scalable alternative for investigating respiratory transmission. In healthy individuals, the microbial community of the lung is indistinguishable from that of the upper airways [ 29 ] meaning that, in our study, the origin of the bacteria expelled by study participants could not be determined. However, it is likely, due to the type of respiratory activity performed, that the majority of the bacteria detected originated from the upper respiratory tract [ 39 ]. Although viable SARS-CoV-2 is less frequently isolated from saliva compared to nasal or nasopharyngeal samples [ 40 , 41 ], it has nonetheless been cultured from saliva [ 42 , 43 ] and other URT secretions [ 31 , 32 , 44 ]. Active viral replication in minor salivary and parotid glands has also been demonstrated [ 45 ]. These findings, although specific to SARS-CoV-2, highlight the potential for saliva and URT secretions to serve as reservoirs for respiratory pathogens and for commensal URT bacteria, which are readily detected in oral fluids, to be used to study the risk of respiratory transmission. To demonstrate the applicability of this approach, we assessed the impact of increased vocal effort on the generation and dispersal of respiratory particles and compared our findings to those of previous studies [ 4 , 12 ]. Use of URT bacteria confirmed the expected increase in L-RPs and S-RPs and provided a straightforward alternative to more complex methods. During the COVID-19 pandemic, the mandating of face coverings in public places was a major public health strategy that aimed to mitigate the spread of the virus. Studies using techniques such as aerodynamic particle sizing [ 12 , 46 ], laser imaging [ 15 ], and mathematical modelling [ 47 – 50 ] suggested that disposable (IIR) medical face masks would be effective in reducing transmission of SARS-CoV-2. Pathogen-based studies assessing the dispersal of IRPs from human volunteers have yielded similar findings. For example, when wearing a (IIR) medical face mask and when coughing directly onto a Petri dish containing viral media, patients with symptoms of influenza dispersed lower levels of detectable viral RNA in L-RPs than when no face covering was worn [ 20 ]. Studies using a Gesundheit device have also demonstrated that face coverings, including IIR medical face masks, can substantially reduce the emission of viral RNA in larger respiratory particles, while being less effective for smaller size fractions [ 22 , 23 ]. Consistent with this, it has been shown that smaller particles are released to a greater extent and can escape from all sides of the mask, with the largest proportion leaking from the top around the nose [ 46 ]. Nevertheless, even with such leakage, (IIR) face masks still provide substantial overall source control [ 46 ]. Studies measuring viral RNA require participants to be infected with the target virus. Our approach, utilizing commensal bacteria and healthy participants, confirmed the effectiveness of (IIR) medical face masks in significantly reducing the release of L-RPs and S-RPs whilst mitigating the infection risks associated with pathogen-based methods. Facial expressions are a key component of nonverbal communication - a particularly important aspect of effective communication. During the pandemic, the ubiquity of face masks in healthcare and other settings, created a barrier to effective communication and, inadvertently, an increase in communication stress [ 51 ]. While this is of relevance throughout the general population, it is particularly significant for individuals who rely on visual cues, such as those who are hard of hearing. A technical specification for transparent face coverings was originally developed by the NHS Transparent Face Mask Working Group (NHS England and NHS Improvement) in 2021 to provide guidance on performance. At the time, limited data were available on their effectiveness for source control and respiratory particle dispersal, so performance assumptions were made. This specification was withdrawn in March 2023 as it was considered out of date. A study using a dummy head and breathing simulator demonstrated the functional efficiency of a clear window mask, albeit with reduced source control for smaller particles [ 52 ]. Similar studies have highlighted the comparative ineffectiveness of face shields, particularly with the movement of the head [ 53 , 54 ]; a finding also predicted by computational fluid dynamics models [ 55 ]. Using URT bacteria as index organisms, we also demonstrated that when worn by human participants, the three transparent face coverings (clear window mask, the ClearMask™, and the Panoramic Mio-Mask) were as effective in capturing L-RPs and S-RPs as the (IIR) medical face mask. In contrast, face shields, when used as source control, had limited efficacy (Fig. 6 ), particularly when worn by individuals who dispersed high numbers of URT particles (Supplementary Fig. 3). Throughout our study, significant inter-individual variability in URT particle dispersal was observed, which aligns with previous findings [ 12 , 17 , 38 ]. This variability supports the “superspreader” concept, where a small proportion of individuals contribute disproportionately to particle dispersal [ 56 ]. While the exact causes remain unclear, our results and those of others suggest that age may play a role, with older individuals potentially dispersing more particles [ 57 – 59 ]. In our study, males dispersed significantly more URT bacteria than females. This gender-related observation is supported by the findings of Yan et al. [ 21 ] and Yang et al. [ 60 ] who also reported increased droplet concentration with age. Other factors that have been linked to ‘super-emitting’ individuals include lung capacity, specifically peak expiratory flow [ 38 ] and obesity [ 57 , 58 ]. When investigating the dispersal (and transmission) of respiratory pathogens, it is important to acknowledge the potential for high inter-individual variability. The presence (or absence) of ‘super-emitting’ individuals within a study cohort will influence the data obtained and, importantly, its interpretation. To better understand when individuals may expel and disperse more infectious respiratory particles, there is first a need to understand why. More studies are needed to clarify respiratory emission dynamics, and our approach of using URT bacteria as index organisms provides a useful tool to further explore factors that drive individual variation. In conclusion, we describe a relatively simple, affordable, and accessible method for measuring the dispersal of large- and small IRPs. The techniques employed allowed us to confirm that increased vocal effort increases the emission of L-RPs and S-RPs, and that (IIR) medical face masks and transparent face coverings (excluding face shields) can effectively prevent their spread. We identified ‘super emitters’ among those participating in this study and, as in previous studies, observed an association between age and particle dispersal, and showed that face coverings have the greatest impact when worn by high emitters. However, it is acknowledged that whilst URT bacteria can be considered appropriate index organisms for bacterial pathogens, data may not necessarily translate to respiratory viruses. Infections caused by Streptococcus pyogenes , specifically invasive Group A streptococcal (iGAS) disease, can induce severe clinical manifestations and are not infrequent [ 61 ]. Our results demonstrate that wearing a (IIR) medical face mask (or transparent face covering) significantly reduces the dispersal of oral streptococci - a potential basis for future recommendations. The respiratory tract harbours a less explored, yet rich virome consisting of high concentrations of non-pathogenic viruses. Among these viruses is Torque teno virus, previously suggested as a potential marker for SARS-CoV-2 infection resolution [ 62 ] and intensive care unit admissions [ 63 ]. Current work is investigating the utility of commensal viral markers. Whilst acknowledging the constraints of our study, including a small cohort size, limited age diversity, and the dimensional constraints of IMADGENN potentially impacting the rate of aerosol decay, we believe its use together with commensal URT bacteria, including streptococci, represents a rapid and efficient system for examining patterns of dispersal (who, why, when) and mitigation strategies for respiratory pathogens. Our current research is expanding upon our findings and further developing and refining this methodology for broader application in the study of respiratory pathogen dispersal. Declarations Competing interests The authors declare no competing interests. UKHSA acts independently from any company named in this manuscript and does not endorse or promote any of the products or practices evaluated or tested as part of this project. Author contributions GM designed the research; PB, WDC, and NY performed the experiments; GM, PB, and NY analysed the data; NQV performed statistical analysis; PB and GM wrote the manuscript. The manuscript was reviewed by all authors for accuracy. Acknowledgements The authors wish to thank Simon Parks, Allan Bennett, and all those who participated in the study, which was supported by funding from UKHSA and the PROTECT COVID-19 National Core Study on transmission and environment, managed by the Health and Safety Executive on behalf of the UK Government. The views expressed are those of the authors and not necessarily those of UKHSA or any other Government Agency. Data availability statement The raw data generated for this study are available in Figshare repository – https://doi.org/10.6084/m9.figshare.27159852.v1 References Chen, Q., Allot, A. & Lu, Z. LitCovid: an open database of COVID-19 literature. Nucleic Acids Research 49 , D1534-D1540 (2020). https://doi.org/10.1093/nar/gkaa952 WHO. Global technical consultation report on proposed terminology for pathogens that transmit through the air. (Geneva, 2024). Johnson, G. R. & Morawska, L. The mechanism of breath aerosol formation. J Aerosol Med Pulm Drug Deliv 22 , 229-237 (2009). https://doi.org/10.1089/jamp.2008.0720 Stadnytskyi, V., Bax, C. E., Bax, A. & Anfinrud, P. The airborne lifetime of small speech droplets and their potential importance in SARS-CoV-2 transmission. Proceedings of the National Academy of Sciences 117 , 11875-11877 (2020). https://doi.org/doi:10.1073/pnas.2006874117 Alsved, M. et al. Exhaled respiratory particles during singing and talking. Aerosol Science and Technology 54 , 1245-1248 (2020). https://doi.org/10.1080/02786826.2020.1812502 Kato, H. et al. Expansion of droplets during speaking and singing in Japanese. PLoS One 17 , e0272122 (2022). https://doi.org/10.1371/journal.pone.0272122 Bahl, P. et al. Droplets and Aerosols Generated by Singing and the Risk of Coronavirus Disease 2019 for Choirs. Clin Infect Dis 72 , e639-e641 (2021). https://doi.org/10.1093/cid/ciaa1241 Loudon, R. G. & Roberts, R. M. Singing and the dissemination of tuberculosis. Am Rev Respir Dis 98 , 297-300 (1968). https://doi.org/10.1164/arrd.1968.98.2.297 McCarthy, L. P. et al. Aerosol and droplet generation from performing with woodwind and brass instruments. Aerosol Science and Technology 55 , 1277-1287 (2021). https://doi.org/10.1080/02786826.2021.1947470 Wang, L. et al. Characterization of aerosol plumes from singing and playing wind instruments associated with the risk of airborne virus transmission. Indoor Air 32 , e13064 (2022). https://doi.org/10.1111/ina.13064 Tanner, K. et al. Large Particle Emissions from Human Vocalization and Playing of Wind Instruments. Environ Sci Technol 57 , 15392-15400 (2023). https://doi.org/10.1021/acs.est.3c03588 Asadi, S. et al. Aerosol emission and superemission during human speech increase with voice loudness. Scientific Reports 9 , 2348 (2019). https://doi.org/10.1038/s41598-019-38808-z Han, Z. Y., Weng, W. G. & Huang, Q. Y. Characterizations of particle size distribution of the droplets exhaled by sneeze. J R Soc Interface 10 , 20130560 (2013). https://doi.org/10.1098/rsif.2013.0560 Scharfman, B. E., Techet, A. H., Bush, J. W. M. & Bourouiba, L. Visualization of sneeze ejecta: steps of fluid fragmentation leading to respiratory droplets. Experiments in Fluids 57 , 24 (2016). https://doi.org/10.1007/s00348-015-2078-4 Ho, K. M. A. et al. Spatiotemporal droplet dispersion measurements demonstrate face masks reduce risks from singing. Scientific Reports 11 , 24183 (2021). https://doi.org/10.1038/s41598-021-03519-x Verma, S., Dhanak, M. & Frankenfield, J. Visualizing the effectiveness of face masks in obstructing respiratory jets. Phys Fluids (1994) 32 , 061708 (2020). https://doi.org/10.1063/5.0016018 Asadi, S. et al. Efficacy of masks and face coverings in controlling outward aerosol particle emission from expiratory activities. Scientific Reports 10 , 15665 (2020). https://doi.org/10.1038/s41598-020-72798-7 Kniesburges, S. et al. Effects of surgical masks on aerosol dispersion in professional singing. J Expo Sci Environ Epidemiol 32 , 727-734 (2022). https://doi.org/10.1038/s41370-021-00385-7 Gregson, F. K. A. et al. Analytical challenges when sampling and characterising exhaled aerosol. Aerosol Science and Technology 56 , 160-175 (2022). https://doi.org/10.1080/02786826.2021.1990207 Johnson, D. F., Druce, J. D., Birch, C. & Grayson, M. L. A quantitative assessment of the efficacy of surgical and N95 masks to filter influenza virus in patients with acute influenza infection. Clin Infect Dis 49 , 275-277 (2009). https://doi.org/10.1086/600041 Yan, J. et al. Infectious virus in exhaled breath of symptomatic seasonal influenza cases from a college community. Proceedings of the National Academy of Sciences 115 , 1081-1086 (2018). https://doi.org/10.1073/pnas.1716561115 Milton, D. K., Fabian, M. P., Cowling, B. J., Grantham, M. L. & McDevitt, J. J. Influenza virus aerosols in human exhaled breath: particle size, culturability, and effect of surgical masks. PLoS Pathog 9 , e1003205 (2013). https://doi.org/10.1371/journal.ppat.1003205 Leung, N. H. L. et al. Respiratory virus shedding in exhaled breath and efficacy of face masks. Nature Medicine 26 , 676-680 (2020). https://doi.org/10.1038/s41591-020-0843-2 Duguid, J. P. The size and the duration of air-carriage of respiratory droplets and droplet-nuclei. J Hyg (Lond) 44 , 471-479 (1946). https://doi.org/10.1017/s0022172400019288 Hamburger, M., Jr. & Robertson, O. H. Expulsion of group A hemolytic streptococci in droplets and droplet nuclei by sneezing, coughing and talking. Am J Med 4 , 690-701 (1948). https://doi.org/10.1016/s0002-9343(48)90392-1 Bennett, A. M. et al. Microbial aerosols in general dental practice. Br Dent J 189 , 664-667 (2000). https://doi.org/10.1038/sj.bdj.4800859 Reid, D. D., Lidwell, O. M. & Williams, R. E. O. Counts of Air-Borne Bacteria as Indices of Air Hygiene. Epidemiology and Infection 54 , 524-532 (1956). https://doi.org/10.1017/S0022172400044806 Torrey, J. C. & Lake, M. Streptococci in air as indicator of nasopharyngeal contaminantion. JAMA 117 , 1425-1430 (1941). https://doi.org/doi:10.1001/jama.1941.02820430021006 Charlson, E. S. et al. Topographical continuity of bacterial populations in the healthy human respiratory tract. Am J Respir Crit Care Med 184 , 957-963 (2011). https://doi.org/10.1164/rccm.201104-0655OC Mohammadi, A., Esmaeilzadeh, E., Li, Y., Bosch, R. J. & Li, J. Z. SARS-CoV-2 detection in different respiratory sites: A systematic review and meta-analysis. EBioMedicine 59 , 102903 (2020). https://doi.org/10.1016/j.ebiom.2020.102903 Kinoshita, H. et al. Improved efficacy of SARS-CoV-2 isolation from COVID-19 clinical specimens using VeroE6 cells overexpressing TMPRSS2 and human ACE2. Sci Rep 14 , 24858 (2024). https://doi.org/10.1038/s41598-024-75038-4 La Scola, B. et al. Viral RNA load as determined by cell culture as a management tool for discharge of SARS-CoV-2 patients from infectious disease wards. Eur J Clin Microbiol Infect Dis 39 , 1059-1061 (2020). https://doi.org/10.1007/s10096-020-03913-9 Williams, R. Generalized Ordered Logit/Partial Proportional Odds Models for Ordinal Dependent Variables. The Stata Journal 6 , 58-82 (2006). https://doi.org/10.1177/1536867X0600600104 Wolfe, R. & Gould, W. An approximate likelihood-ratio test for ordinal response models. Stata Technical Bulletin 7 (1998). Dhaoui, I. et al. Mathematical modelling of COVID-19: a systematic review and quality assessment in the early epidemic response phase. Int J Infect Dis 116 , S110 (2022). https://doi.org/10.1016/j.ijid.2021.12.260 Adekola, H. A., Adekunle, I. A., Egberongbe, H. O., Onitilo, S. A. & Abdullahi, I. N. Mathematical modeling for infectious viral disease: The COVID-19 perspective. J Public Aff 20 , e2306 (2020). https://doi.org/10.1002/pa.2306 Li, Y. et al. Review of animal transmission experiments of respiratory viruses: Implications for transmission risk of SARS-COV-2 in humans via different routes. Risk Anal (2023). https://doi.org/10.1111/risa.14129 Zhou, J. et al. Viral emissions into the air and environment after SARS-CoV-2 human challenge: a phase 1, open label, first-in-human study. Lancet Microbe 4 , e579-e590 (2023). https://doi.org/10.1016/s2666-5247(23)00101-5 Johnson, G. R. et al. Modality of human expired aerosol size distributions. Journal of Aerosol Science 42 , 839-851 (2011). https://doi.org/https://doi.org/10.1016/j.jaerosci.2011.07.009 Yazawa, S. et al. Evaluation of SARS-CoV-2 isolation in cell culture from nasal/nasopharyngeal swabs or saliva specimens of patients with COVID-19. Scientific Reports 13 , 8893 (2023). https://doi.org/10.1038/s41598-023-35915-w Todsen, T. et al. Higher SARS-CoV-2 detection of oropharyngeal compared with nasopharyngeal or saliva specimen for molecular testing: a multicentre randomised comparative accuracy study. Thorax 78 , 1028-1034 (2023). https://doi.org/10.1136/thorax-2022-219599 To, K. K. et al. Consistent Detection of 2019 Novel Coronavirus in Saliva. Clin Infect Dis 71 , 841-843 (2020). https://doi.org/10.1093/cid/ciaa149 Wölfel, R. et al. Virological assessment of hospitalized patients with COVID-2019. Nature 581 , 465-469 (2020). https://doi.org/10.1038/s41586-020-2196-x Yamada, S. et al. Assessment of SARS-CoV-2 infectivity of upper respiratory specimens from COVID-19 patients by virus isolation using VeroE6/TMPRSS2 cells. BMJ Open Respir Res 8 (2021). https://doi.org/10.1136/bmjresp-2020-000830 Huang, N. et al. SARS-CoV-2 infection of the oral cavity and saliva. Nature Medicine 27 , 892-903 (2021). https://doi.org/10.1038/s41591-021-01296-8 Cappa, C. D. et al. Expiratory aerosol particle escape from surgical masks due to imperfect sealing. Scientific Reports 11 , 12110 (2021). https://doi.org/10.1038/s41598-021-91487-7 Stutt, R., Retkute, R., Bradley, M., Gilligan, C. A. & Colvin, J. A modelling framework to assess the likely effectiveness of facemasks in combination with 'lock-down' in managing the COVID-19 pandemic. Proc Math Phys Eng Sci 476 , 20200376 (2020). https://doi.org/10.1098/rspa.2020.0376 Rao, I. J., Vallon, J. J. & Brandeau, M. L. Effectiveness of Face Masks in Reducing the Spread of COVID-19: A Model-Based Analysis. Med Decis Making 41 , 988-1003 (2021). https://doi.org/10.1177/0272989x211019029 Robinson, J. F. et al. Efficacy of face coverings in reducing transmission of COVID-19: Calculations based on models of droplet capture. Phys Fluids (1994) 33 , 043112 (2021). https://doi.org/10.1063/5.0047622 Catching, A., Capponi, S., Yeh, M. T., Bianco, S. & Andino, R. Examining the interplay between face mask usage, asymptomatic transmission, and social distancing on the spread of COVID-19. Sci Rep 11 , 15998 (2021). https://doi.org/10.1038/s41598-021-94960-5 Saunders, G. H., Jackson, I. R. & Visram, A. S. Impacts of face coverings on communication: an indirect impact of COVID-19. International Journal of Audiology 60 , 495-506 (2021). https://doi.org/10.1080/14992027.2020.1851401 Chazelet, S. & Pacault, S. Efficiency of Community Face Coverings and Surgical Masks to Limit the Spread of Aerosol. Ann Work Expo Health 66 , 495-509 (2022). https://doi.org/10.1093/annweh/wxab089 Lindsley, W. G., Blachere, F. M., Law, B. F., Beezhold, D. H. & Noti, J. D. Efficacy of face masks, neck gaiters and face shields for reducing the expulsion of simulated cough-generated aerosols. Aerosol Sci Technol 55 , 449-457 (2021). https://doi.org/10.1080/02786826.2020.1862409 Pan, J., Harb, C., Leng, W. & Marr, L. C. Inward and outward effectiveness of cloth masks, a surgical mask, and a face shield. Aerosol Science and Technology 55 , 718-733 (2021). https://doi.org/10.1080/02786826.2021.1890687 Tretiakow, D., Tesch, K. & Skorek, A. Mitigation effect of face shield to reduce SARS-CoV-2 airborne transmission risk: Preliminary simulations based on computed tomography. Environ Res 198 , 111229 (2021). https://doi.org/10.1016/j.envres.2021.111229 Woolhouse, M. E. et al. Heterogeneities in the transmission of infectious agents: implications for the design of control programs. Proc Natl Acad Sci U S A 94 , 338-342 (1997). https://doi.org/10.1073/pnas.94.1.338 Edwards, D. A. et al. Exhaled aerosol increases with COVID-19 infection, age, and obesity. Proc Natl Acad Sci U S A 118 (2021). https://doi.org/10.1073/pnas.2021830118 George, C. E. et al. COVID-19 symptoms are reduced by targeted hydration of the nose, larynx and trachea. Scientific Reports 12 , 4599 (2022). https://doi.org/10.1038/s41598-022-08609-y Schumm, B. et al. Lung aerosol particle emission increases with age at rest and during exercise. Proc Natl Acad Sci U S A 120 , e2301145120 (2023). https://doi.org/10.1073/pnas.2301145120 Yang, S., Lee, G. W., Chen, C. M., Wu, C. C. & Yu, K. P. The size and concentration of droplets generated by coughing in human subjects. J Aerosol Med 20 , 484-494 (2007). https://doi.org/10.1089/jam.2007.0610 Lu, Q., Yu, D. & Yang, Y. Group A Streptococcus Is Still at Large. J Clin Med 12 (2023). https://doi.org/10.3390/jcm12072739 Mendes-Correa, M. C. et al. Torquetenovirus in saliva: A potential biomarker for SARS-CoV-2 infection? PLoS One 16 , e0256357 (2021). https://doi.org/10.1371/journal.pone.0256357 Feghoul, L. et al. Respiratory torque teno virus load at emergency department visit predicts intensive care unit admission of SARS-CoV-2 infected patients. J Med Virol 95 , e29319 (2023). https://doi.org/10.1002/jmv.29319 Additional Declarations The authors declare no competing interests. Supplementary Files BarkocietalSupplementary210325.docx Supplementary material Cite Share Download PDF Status: Published Journal Publication published 24 Dec, 2025 Read the published version in Scientific Reports → Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":619016,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIsolator for Measuring Aerosol and Droplet GENerationN (IMADGENN):\u003c/strong\u003e front participant panel (a), supply HEPA filter (b), extract HEPA filter (c), air sampler (d), settle plates (e), side panel (f). Created in BioRender.com\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5231720/v1/10694fd7438e2233d8914752.png"},{"id":92962338,"identity":"317912ba-7bf4-4230-bdc6-04d79ebaf8b5","added_by":"auto","created_at":"2025-10-03 08:39:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1285512,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTransparent face coverings:\u003c/strong\u003e (a) ClearMask™ (ClearMask LLC, Baltimore, USA), (b) reusable clear window mask (Stevenage Packing Limited, Hertfordshire, UK), (c) Panoramic Mio-Mask (L.J.A MIERS \u0026amp; Co. Ltd, Cambridgeshire, UK), (d) face shield (SciQuip Ltd, Shropshire, UK)\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5231720/v1/85f52579e86c340c330f3aa5.png"},{"id":92962341,"identity":"f4beb1d5-23cb-4492-b26d-997cc79b1873","added_by":"auto","created_at":"2025-10-03 08:55:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":377358,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe mean number (± SD) of small and large respiratory particles recovered from droplets deposited on surfaces at increasing distance from the source\u003c/strong\u003e. Healthy volunteers (n=16) were asked to count from 1 to 100 when shouting (red bars) or in their normal conversational voice (blue bars).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5231720/v1/223c9363be1892e8d54f7d79.png"},{"id":92962343,"identity":"e4bff7c0-752a-4309-b669-7aed0d881de0","added_by":"auto","created_at":"2025-10-03 08:39:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":203974,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImpact of increased vocal effort (loudness) on the dispersal of small and large respiratory particles (S-RPs and L-RPs). \u0026nbsp;\u003c/strong\u003eHealthy volunteers (n=16) were asked to count from 1 to 100 when shouting or in their normal conversational voice. Truncated violin plots with box plot illustrate the total number of URT bacteria deposited on surfaces within 0.5 m from the source (L-RPs) (a) and captured by the air sampler (S-RPs) positioned 1.2 m from the source and operating at 17 L/min (b).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5231720/v1/574f972973ac832e662945d1.png"},{"id":92962347,"identity":"fb288e9a-0bdd-4e1d-af6e-a804dbeea31e","added_by":"auto","created_at":"2025-10-03 08:39:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":343543,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe effectiveness of (IIR) face coverings in reducing the dispersal of small and large respiratory particles (S-RPs and L-RPs). \u003c/strong\u003eTruncated violin plots with box plot illustrate the total number of URT bacteria deposited on surfaces (L-RPs) within 1 m from source (a) and captured by the air sampler (S-RPs) (b) positioned 1.2 m from source and operating at 17 L/min when volunteers (n=15) were asked to perform a series of respiratory activities when wearing and when not wearing a (IIR) medical face mask.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5231720/v1/52c9db310568b1fbfa0ebf8b.png"},{"id":92962349,"identity":"4869953c-8194-4d8e-bb6b-4c986e3bdb31","added_by":"auto","created_at":"2025-10-03 08:47:51","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":219393,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe effectiveness of face coverings in reducing the dispersal of small and large respiratory particles (S-RPs and L-RPs).\u003c/strong\u003e The total number of URT bacteria deposited on surfaces (L-RPs) within 1 m from source (a) and captured by the air sampler (S-RPs) (b) when volunteers (n=15) were asked to perform a series of respiratory activities when not wearing a face covering, when wearing a disposable (IIR) medical mask or a randomly assigned transparent face covering.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5231720/v1/1d5abd5ba5a80420d9c5c02d.jpg"},{"id":92962352,"identity":"6c1760c1-223d-40da-afe0-41ced89fc35a","added_by":"auto","created_at":"2025-10-03 08:39:51","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":319413,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInter-individual variability in dispersal of respiratory particles (RPs).\u003c/strong\u003e Two participants (cohort 2) generated 66% and 81% of the URT bacteria deposited on surfaces (L-RPs) (a) and captured by the air sampler (S-RPs) (b) respectively.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-5231720/v1/a43ef9074512a208206303ff.png"},{"id":99172240,"identity":"2866cafd-59b4-42ba-b405-e98bbeae4216","added_by":"auto","created_at":"2025-12-29 16:05:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5867374,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5202589/v2/4839da5f-d171-4782-9354-d1f2eb788ee0.pdf"},{"id":92962331,"identity":"bca2c03e-46e5-4e5f-b21d-b7f6e098f2d2","added_by":"auto","created_at":"2025-10-03 08:39:51","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":249064,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary material\u003c/p\u003e","description":"","filename":"BarkocietalSupplementary210325.docx","url":"https://assets-eu.researchsquare.com/files/rs-5231720/v1/ad02e5285c9504b425f85d96.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"Assessing the Generation and Dispersal of Respiratory Particles Using Upper Respiratory Commensal Bacteria as Index Organisms for Respiratory Pathogens","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe urgency of the COVID-19 pandemic highlighted knowledge gaps among the scientific community regarding the transmission of respiratory pathogens. This, in turn, catalysed a surge of research activity and a substantial expansion of scientific literature. During the early phases of the pandemic, approximately 10,000 COVID-19-related articles were submitted to scientific journals each month [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, pivotal questions regarding the mechanisms of respiratory particle generation and dispersal, including the potential for \u0026lsquo;super-spreaders\u0026rsquo; and for \u0026lsquo;super-spreading\u0026rsquo; events (i.e. respiratory activities that could facilitate transmission) continue to warrant investigation.\u003c/p\u003e\u003cp\u003eDispersal of respiratory pathogens can occur when particles containing potentially infectious organisms are expelled from the upper and lower respiratory tracts of infected individuals. These \u0026lsquo;infectious respiratory particles\u0026rsquo; (IRPs) can travel through the air and infect individuals via inhalation (Supplementary Figs.\u0026nbsp;1a and 1b) or by directly depositing onto facial mucosal membranes (Supplementary Fig.\u0026nbsp;1c). The deposition of IRPs on hands and/or surfaces could also lead to direct (Supplementary Fig.\u0026nbsp;1d) or indirect (Supplementary Fig.\u0026nbsp;1e) fomite transmission [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eThe risk of IRP transmission depends on a complex interplay between environmental conditions (e.g., temperature, relative humidity, airflow), the physicochemical characteristics of the pathogen, social behaviour, and mitigation strategy. The activity by which IRPs are expelled by the individual is also important. Expiratory activity has been shown to affect the size [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and the concentration of IRPs and the distance they travel [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePrevious studies have monitored the number, mass, and size distribution of the particles exhaled by (usually) healthy study participants to risk assess different activities, including singing [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], the playing of musical instruments [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], loud talking [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and sneezing [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The effectiveness of face coverings in mitigating the transmission of IRPs has also been assessed across a range of expiratory activities [\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNon-microbiological techniques, such as those used in the aforementioned studies, allow different activities and mitigation strategies to be directly compared. However, studies must be carried out within an environment with a very low (near-zero) background particle concentration [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and sampling must be carried out as close to the source as possible, providing limited information about potential dispersal and spread. They also provide limited information about potential infectivity \u0026ndash; if, for example, the particles detected carry (or could carry) the target organism.\u003c/p\u003e\u003cp\u003ePathogen-based studies using naturally infected participants have demonstrated the dispersal of respiratory pathogens during expiratory activities such as coughing [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], talking [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] or breathing [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and have confirmed the utility of face coverings in mitigating this spread. Whilst these studies provide crucial validation for the role of expiratory activities and source control measures, they are technically and ethically challenging.\u003c/p\u003e\u003cp\u003eAn alternative approach is to utilise index organisms. For the purposes of this study, we define \u003cem\u003eindex organisms\u003c/em\u003e as microorganisms originating from the same anatomical source as a given pathogen, such that their detection within a specific environment indicates the potential presence or risk of that pathogen.\u003c/p\u003e\u003cp\u003eTo assess the risk of IRP transmission \u003cem\u003ein situ\u003c/em\u003e without involving infected individuals, previous studies have utilized oral streptococci as key index organisms. Duguid [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and Hamburger and Robertson [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] investigated the dispersal of oral streptococci during respiratory activities such as talking, coughing, and sneezing. Bennett et al [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] utilised airborne streptococcal counts to evaluate the occupational risk associated with dental procedures, whilst Reid et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and Torrey and Lake [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] established a correlation between the concentration of oral streptococci in the air and the incidence of measles and the common cold, respectively.\u003c/p\u003e\u003cp\u003eThe oral cavity and upper- and lower respiratory tracts share highly similar microbial communities in healthy individuals [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and studies utilising molecular [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and culture-based assays [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] have shown that respiratory pathogens such as SARS-CoV-2 are frequently present in saliva and/or the upper respiratory tract (URT). We hypothesize that URT bacteria, which are readily detected in oral fluids, could serve as effective proxies (index organisms) for studying the dispersal of respiratory pathogens. Such an approach would mitigate some of the ethical and technical challenges posed by direct pathogen detection and allow the risk of respiratory transmission to be studied in the absence of a target pathogen.\u003c/p\u003e\u003cp\u003eTo demonstrate the utility of this method, we designed a bespoke flexible-film isolator to examine the generation and dispersal of URT bacteria in relation to individual, respiratory activity and mitigation strategy, specifically the effectiveness of face coverings. Results were validated against methodologically more complex studies published in the literature.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eIsolator for Measuring Aerosol and Droplet GENerationN (IMADGENN)\u003c/h2\u003e\u003cp\u003eIMADGENN is a purpose-built flexible film isolator (PFI Systems Ltd, Milton Keynes, UK) designed to capture respiratory particles generated by study participants (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The isolator measures 0.5 m (w) x 1.5 m (l) x 0.6 m (h) and is supplied with HEPA-filtered air. The isolator is fastened to a stand (increasing the overall height of IMADGENN to 1.7 m), and non-gas-tight zips allow panels located at the front and the side of the isolator to be opened, facilitating both study participation and the manipulation of sampling equipment. A second HEPA filter (with an efficiency of 99.995% at 0.3 \u0026micro;m) attached to the extract system allows rapid removal of airborne particles and ensures a low level of background aerosol. With the extraction fan operating, the air flow through IMADGENN is 1.72 m\u003csup\u003e3\u003c/sup\u003e/min (280 air changes/hour).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eParticipants and experimental setup\u003c/h3\u003e\n\u003cp\u003eThis study was approved by the UK Health Security Agency (Public Health England at the time) Research Ethics and Governance Group (reference number R\u0026amp;D 406). All experiments were performed in accordance with relevant guidelines and regulations. Informed consent was obtained from all volunteers prior to participating in the study, when they were also asked to provide their age. No other personally identifiable information was collected, and none was stored.\u003c/p\u003e\u003cp\u003eThis study was designed as a proof-of-concept, exploratory investigation to demonstrate the feasibility of using upper respiratory tract (URT) bacteria as proxies for respiratory pathogen dispersal. As such, no formal a priori sample-size calculation was performed. Cohort sizes were determined based on the availability of volunteers and operational constraints (Cohort 1, n\u0026thinsp;=\u0026thinsp;16; Cohort 2, n\u0026thinsp;=\u0026thinsp;15). The study is primarily hypothesis-generating and intended to inform the design of future, fully powered investigations.\u003c/p\u003e\u003cp\u003ePrior to participating in the study, healthy volunteers were asked to self-test for COVID-19 using a lateral flow device and report a negative test result. All study participants provided a saliva sample immediately before completing the experiment to confirm the presence and concentration of URT bacteria.\u003c/p\u003e\u003cp\u003eBefore commencing each experimental run, the air filtration system of IMADGENN was operated for three minutes to remove any residual particles. Columbia Blood agar plates (CBA; E \u0026amp; O Laboratories Ltd, Bonnybridge, UK) were positioned inside the isolator at 10 cm intervals (up to 1 meter) to the left, centre and right of the isolator (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). An active air sampler, an R2S slit-to-agar sampler (Emtek LLC,Longmont, US) operating at a flow rate of 17 or 28.3 litres min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and collecting onto CBA, was positioned inside the isolator 1.2 meters from the participant. The lids of the settle plates were removed, and the isolator was secured by zipping up both the side and front panels. The air filtration system continued to operate for at least three minutes to remove particles introduced by the investigator. It was then switched off.\u003c/p\u003e\u003cp\u003eEach participant was positioned directly in front of IMADGENN and was instructed to adjust the chair so that their face was centred adjacent to the front panel (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(a)) and approximately 30 cm from the top of the isolator. The air sampler was operated remotely, and the participant was directed to open the panel and to perform the specified activity.\u003c/p\u003e\u003cp\u003eEach experimental run included a fallow period when, following completion of the respiratory activity and with the front panel closed, the air sampler continued to operate. This was to ensure that particles remaining in the air were captured. The air filtration system of the isolator was then switched on and allowed to run for five minutes before the side panel was opened and the agar plates collected and replaced.\u003c/p\u003e\n\u003ch3\u003eRespiratory activities\u003c/h3\u003e\n\u003cp\u003eFor the purposes of this study, URT bacteria that deposited on surfaces (settle plates) were referred to as large respiratory particles (L-RPs), while URT bacteria that remained suspended in the air and captured by the air sampler were termed small IRPs (S-RPs).\u003c/p\u003e\u003cp\u003eHere, the term \u0026lsquo;dispersal\u0026rsquo; refers specifically to the release or shedding of L-RPs and S-RPs from participants during respiratory activities. It does not refer to the subsequent transport or movement of these particles in the environment.\u003c/p\u003e\n\u003ch3\u003eAssessing the impact of increased vocal effort (loudness; cohort 1)\u003c/h3\u003e\n\u003cp\u003eVolunteers aged between 21 and 60 years (n\u0026thinsp;=\u0026thinsp;16; 8 female) were asked to count from one to 100 in their \u0026lsquo;normal\u0026rsquo; conversational voice. They were then asked to repeat the activity whilst shouting as loud as was comfortable at a sustained amplitude. No face covering was worn. A decibel (dB) meter (Cirrus Research Plc, Hunmanby, UK) was placed inside the isolator (0.8 meters from the participant) and used to monitor sound level. The mean, minimum, and maximum dB level was recorded. Each experimental run, including the fallow period, was four minutes.\u003c/p\u003e\n\u003ch3\u003eAssessing the effectiveness of face coverings in reducing dispersal (cohort 2)\u003c/h3\u003e\n\u003cp\u003eVolunteers aged between 20 and 65 years (n\u0026thinsp;=\u0026thinsp;15; 7 female) were asked to perform a series of respiratory activities, specifically: to cough three times, count from one to 100, exhale through the mouth, sing \u0026ldquo;Happy Birthday\u0026rdquo;, exhale through the nose, count from one to 100, and cough three times again. Each participant carried out the same series of respiratory activities when wearing and when not wearing a face covering. Each experimental run, including the fallow period, was 15 minutes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eCulture and identification of URT bacteria\u003c/h2\u003e\u003cp\u003eEach saliva sample was serially diluted (10-fold) in sterile water, and aliquots (100\u0026micro;l) of the 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e and 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e dilutions were cultured in duplicate on CBA. All saliva samples were then disposed of, and all CBA plates, including those removed from IMADGENN, were incubated at 37\u0026deg;C for 48 hours.\u003c/p\u003e\u003cp\u003eAfter incubation, bacterial colonies were counted and presumptive URT bacteria identified by matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (Bruker Daltonik MALDI biotyper; Bruker, Bremen, Germany) using the extended direct transfer method.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eDescriptive and non-parametric analysis was performed using GraphPad Prism 9 or R (version 4.3.3). Further data manipulation and inferential analysis were performed in STATA, version 17.0.\u003c/p\u003e\u003cp\u003eAs each participant contributed multiple measurements, mixed effects models were used, with participant as the random effect and the remaining variables as the fixed effects. Following the conclusion of the modelling process, if the random effect was practically indistinguishable from zero, it was removed, after which the measures of effect (odds ratio (OR) or relative risk ratio (RRR), as appropriate), together with their 95% confidence intervals (CIs), were obtained. Where possible, the likelihood ratio test was used to obtain p-values; otherwise, the p-values were obtained by means of the Wald test. Statistical significance level was taken to be 5%.\u003c/p\u003e\u003cp\u003eOrdinal logistic regression was used, fitting a generalised ordinal logistic regression model at the end of the modelling process, when the test for proportionality of odds as implemented by Williams [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] or, where not possible, the approximate likelihood ratio test, as described by Wolfe and Gould [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] indicated a possible violation of the assumption.\u003c/p\u003e\u003cp\u003eThe amount of data available determined the analysis strategy. This and the modelling procedure are described in full in the supplementary material.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eIndex organisms for respiratory pathogens: Commensal URT bacteria in saliva and respiratory particles\u003c/h2\u003e\u003cp\u003eThe mean concentration of URT bacteria recovered from the saliva of the participants ranged from 3 x 10\u003csup\u003e6\u003c/sup\u003e to 2.9 x 10\u003csup\u003e9\u003c/sup\u003e CFU/ml (cohort 1) and from 1.5 x 10\u003csup\u003e7\u003c/sup\u003e to 2.5 x 10\u003csup\u003e8\u003c/sup\u003e CFU/ml (cohort 2). \u003cem\u003eNeisseria\u003c/em\u003e spp (comprising species \u003cem\u003eflavescence, perflava, subflava, mucosa, elongata, macacae\u003c/em\u003e) and \u003cem\u003eStreptococcus\u003c/em\u003e spp (\u003cem\u003ecristatus, sanguinis, parasanguinis, mitis, salivarius, oralis, pneumoniae, australis, infantis, vestibularis, gordonii\u003c/em\u003e) were the most common URT bacteria present both in saliva and in the respiratory particles produced by all participants. Other predominant URT commensals were \u003cem\u003eRothia spp\u003c/em\u003e (\u003cem\u003eaeria, mucilaginosa, dentocariosa\u003c/em\u003e), \u003cem\u003eActinomyces spp\u003c/em\u003e (\u003cem\u003enaeslundii, oris, odontolyticus\u003c/em\u003e), \u003cem\u003eGemella spp\u003c/em\u003e (\u003cem\u003ehaemolysans, sanguinis\u003c/em\u003e), \u003cem\u003eHaemophilus parainfluenzae\u003c/em\u003e, and \u003cem\u003eMoraxella spp.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eThe impact of increased vocal effort (loudness) on the dispersal of IRPs\u003c/h2\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003eLarge respiratory particles (L-RPs)\u003c/h2\u003e\u003cp\u003eWhen volunteers (n\u0026thinsp;=\u0026thinsp;16) were instructed to count from 1 to 100 in their normal conversational voice, the mean dB level measured within IMADGENN was 75 dB, and the number of URT bacteria deposited on settle plates within 0.5 m from the source ranged from 1 to 48 colony-forming units (CFU) with a median of 13 CFU (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). When the volunteers were instructed to repeat the counting while \u0026lsquo;shouting\u0026rsquo;, the mean dB level recorded was 92 dB, equating to an increase of 15 dB and 17 dB for males and females, respectively. This increased vocal effort resulted in a 15-fold increase in the median number of URT bacteria deposited as L-RPs (range: 36 CFU \u0026minus;\u0026thinsp;1251 CFU (median 197 CFU); Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Deposition significantly decreased with increasing distance from the source (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eSmall respiratory particles (S-RPs)\u003c/h2\u003e\u003cp\u003eWithout considering other factors (univariate analysis), increased vocal effort (\u0026lsquo;shouting\u0026rsquo;) was associated with a significant increase in the number of URT bacteria captured by the air sampler (S-RPs; p\u0026thinsp;=\u0026thinsp;0.01) and a significant linear association between mean dB level and the concentration of URT bacteria recovered as S-RPs. Some individuals dispersed comparatively more S-RPs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). However, after adjusting for age, activity, and height, the odds of recovering a higher number of URT bacteria as S-RPs increased by 34% for every dB increase in mean voice level (95% CI: 1.06, 1.70; p\u0026thinsp;=\u0026thinsp;0.003).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eThe effectiveness of face coverings in reducing the dispersal of IRPs\u003c/h2\u003e\u003cp\u003eWhen volunteers (n\u0026thinsp;=\u0026thinsp;15) were asked to perform a series of respiratory activities without a face covering, the median number of URT bacteria recovered as L-RPs and deposited within 1 m from the source was 84 CFU (range: 6 CFU \u0026ndash; 978 CFU).\u003c/p\u003e\u003cp\u003eCarrying out the same series of respiratory activities whilst wearing a disposable (IIR) medical face mask reduced the median number of URT bacteria deposited as L-RPs to 1 CFU (range: 0 CFU to 6 CFU; p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.004, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003ea) and reduced the odds of detecting higher numbers of S-RPs (1.2 m from source) by 87% (95% CI: 63%, 96%; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWhen volunteers wearing a face shield carried out the same series of respiratory activities, the median number of URT bacteria deposited within 1 m from the source was 54 CFU (range: 0\u0026ndash;769 CFU). In contrast, when a reusable clear window mask, a ClearMask\u0026trade; or a Panoramic Mio-Mask (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e) was worn, the median number of URT bacteria recovered was \u0026le; 2 CFU (range: 0\u0026ndash;11 CFU) suggesting that wearing any of these transparent face coverings (TFCs) would be as effective as a (IIR) medical face mask in preventing dispersal of L-RPs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWearing a face shield increased the odds of capturing higher numbers of URT bacteria as S-RPs but not significantly (OR 2.62, CI: 0.51, 13.5). In contrast, wearing any of the TFCs significantly reduced the odds of detecting higher numbers of S-RPs. The reusable clear window mask was the most effective, significantly reducing the odds by 99% (CI: 91% \u0026minus;\u0026thinsp;100%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eInter-individual variability in dispersal of IRPs\u003c/h2\u003e\u003cp\u003eIn cohort 1 (n\u0026thinsp;=\u0026thinsp;16), individuals with elevated (log) concentrations of URT bacteria in their saliva had increased odds of dispersing higher numbers of L-RPs (95% CI: 1.22, 2.03). However, no significant association was observed with S-RPs. Gender had no significant impact on the dispersal of S-RPs, but after adjusting for height, activity, and dB level, the odds of recovering higher numbers of S-RPs increased by 9% for every year increase in age (95% CI: 1.00-1.19). Older individuals also had significantly higher odds of dispersing higher numbers of L-RPs.\u003c/p\u003e\u003cp\u003eIn cohort 2 (n\u0026thinsp;=\u0026thinsp;15), we observed a non-significant trend toward a U-shaped association between the (log) salivary concentration of URT bacteria and dispersal of L-RPs (p\u0026thinsp;=\u0026thinsp;0.08). There was also a significant U-shaped association between age and the odds of detecting higher numbers of S-RPs. In this cohort, the number of L-RPs generated by study participants again differed with gender, with the male participants dispersing seven times more URT bacteria than the females (Supplementary Fig.\u0026nbsp;2). Two of the male participants generated 66% of all the URT bacteria deposited as L-RPs and 81% of the URT bacteria captured as S-RPs (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). When these were removed as outliers or \u0026lsquo;super-emitters\u0026rsquo;, and by univariant analysis only, the remaining male participants still dispersed significantly more URT bacteria than the females (p\u0026thinsp;=\u0026thinsp;0.04).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eInvestigating respiratory pathogen transmission presents numerous challenges. Mathematical modelling has been used to better understand transmission patterns of respiratory illness [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] but lacks direct comparability to the complexities of living systems. \u003cem\u003eIn vivo\u003c/em\u003e models, which often involve animals [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] or human challenge [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] are expensive and subject to lengthy ethical approvals.\u003c/p\u003e\u003cp\u003eWe propose using upper respiratory tract (URT) bacteria as index organisms for respiratory pathogens. Such an approach overcomes many of the limitations associated with pathogen-based studies and non-microbiological methods of assessing the spread and dispersal of IRPs, and provides a practicable, ethical, and scalable alternative for investigating respiratory transmission.\u003c/p\u003e\u003cp\u003eIn healthy individuals, the microbial community of the lung is indistinguishable from that of the upper airways [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] meaning that, in our study, the origin of the bacteria expelled by study participants could not be determined. However, it is likely, due to the type of respiratory activity performed, that the majority of the bacteria detected originated from the upper respiratory tract [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough viable SARS-CoV-2 is less frequently isolated from saliva compared to nasal or nasopharyngeal samples [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], it has nonetheless been cultured from saliva [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] and other URT secretions [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Active viral replication in minor salivary and parotid glands has also been demonstrated [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. These findings, although specific to SARS-CoV-2, highlight the potential for saliva and URT secretions to serve as reservoirs for respiratory pathogens and for commensal URT bacteria, which are readily detected in oral fluids, to be used to study the risk of respiratory transmission.\u003c/p\u003e\u003cp\u003eTo demonstrate the applicability of this approach, we assessed the impact of increased vocal effort on the generation and dispersal of respiratory particles and compared our findings to those of previous studies [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Use of URT bacteria confirmed the expected increase in L-RPs and S-RPs and provided a straightforward alternative to more complex methods.\u003c/p\u003e\u003cp\u003eDuring the COVID-19 pandemic, the mandating of face coverings in public places was a major public health strategy that aimed to mitigate the spread of the virus. Studies using techniques such as aerodynamic particle sizing [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], laser imaging [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and mathematical modelling [\u003cspan additionalcitationids=\"CR48 CR49\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] suggested that disposable (IIR) medical face masks would be effective in reducing transmission of SARS-CoV-2. Pathogen-based studies assessing the dispersal of IRPs from human volunteers have yielded similar findings. For example, when wearing a (IIR) medical face mask and when coughing directly onto a Petri dish containing viral media, patients with symptoms of influenza dispersed lower levels of detectable viral RNA in L-RPs than when no face covering was worn [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Studies using a Gesundheit device have also demonstrated that face coverings, including IIR medical face masks, can substantially reduce the emission of viral RNA in larger respiratory particles, while being less effective for smaller size fractions [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Consistent with this, it has been shown that smaller particles are released to a greater extent and can escape from all sides of the mask, with the largest proportion leaking from the top around the nose [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Nevertheless, even with such leakage, (IIR) face masks still provide substantial overall source control [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Studies measuring viral RNA require participants to be infected with the target virus. Our approach, utilizing commensal bacteria and healthy participants, confirmed the effectiveness of (IIR) medical face masks in significantly reducing the release of L-RPs and S-RPs whilst mitigating the infection risks associated with pathogen-based methods.\u003c/p\u003e\u003cp\u003eFacial expressions are a key component of nonverbal communication - a particularly important aspect of effective communication. During the pandemic, the ubiquity of face masks in healthcare and other settings, created a barrier to effective communication and, inadvertently, an increase in communication stress [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. While this is of relevance throughout the general population, it is particularly significant for individuals who rely on visual cues, such as those who are hard of hearing. A technical specification for transparent face coverings was originally developed by the NHS Transparent Face Mask Working Group (NHS England and NHS Improvement) in 2021 to provide guidance on performance. At the time, limited data were available on their effectiveness for source control and respiratory particle dispersal, so performance assumptions were made. This specification was withdrawn in March 2023 as it was considered out of date.\u003c/p\u003e\u003cp\u003eA study using a dummy head and breathing simulator demonstrated the functional efficiency of a clear window mask, albeit with reduced source control for smaller particles [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Similar studies have highlighted the comparative ineffectiveness of face shields, particularly with the movement of the head [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]; a finding also predicted by computational fluid dynamics models [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eUsing URT bacteria as index organisms, we also demonstrated that when worn by human participants, the three transparent face coverings (clear window mask, the ClearMask\u0026trade;, and the Panoramic Mio-Mask) were as effective in capturing L-RPs and S-RPs as the (IIR) medical face mask. In contrast, face shields, when used as source control, had limited efficacy (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), particularly when worn by individuals who dispersed high numbers of URT particles (Supplementary Fig.\u0026nbsp;3).\u003c/p\u003e\u003cp\u003eThroughout our study, significant inter-individual variability in URT particle dispersal was observed, which aligns with previous findings [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. This variability supports the \u0026ldquo;superspreader\u0026rdquo; concept, where a small proportion of individuals contribute disproportionately to particle dispersal [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. While the exact causes remain unclear, our results and those of others suggest that age may play a role, with older individuals potentially dispersing more particles [\u003cspan additionalcitationids=\"CR58\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. In our study, males dispersed significantly more URT bacteria than females. This gender-related observation is supported by the findings of Yan et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and Yang et al. [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] who also reported increased droplet concentration with age. Other factors that have been linked to \u0026lsquo;super-emitting\u0026rsquo; individuals include lung capacity, specifically peak expiratory flow [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and obesity [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhen investigating the dispersal (and transmission) of respiratory pathogens, it is important to acknowledge the potential for high inter-individual variability. The presence (or absence) of \u0026lsquo;super-emitting\u0026rsquo; individuals within a study cohort will influence the data obtained and, importantly, its interpretation. To better understand when individuals may expel and disperse more infectious respiratory particles, there is first a need to understand why. More studies are needed to clarify respiratory emission dynamics, and our approach of using URT bacteria as index organisms provides a useful tool to further explore factors that drive individual variation.\u003c/p\u003e\u003cp\u003eIn conclusion, we describe a relatively simple, affordable, and accessible method for measuring the dispersal of large- and small IRPs. The techniques employed allowed us to confirm that increased vocal effort increases the emission of L-RPs and S-RPs, and that (IIR) medical face masks and transparent face coverings (excluding face shields) can effectively prevent their spread. We identified \u0026lsquo;super emitters\u0026rsquo; among those participating in this study and, as in previous studies, observed an association between age and particle dispersal, and showed that face coverings have the greatest impact when worn by high emitters.\u003c/p\u003e\u003cp\u003eHowever, it is acknowledged that whilst URT bacteria can be considered appropriate index organisms for bacterial pathogens, data may not necessarily translate to respiratory viruses. Infections caused by \u003cem\u003eStreptococcus pyogenes\u003c/em\u003e, specifically invasive Group A streptococcal (iGAS) disease, can induce severe clinical manifestations and are not infrequent [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Our results demonstrate that wearing a (IIR) medical face mask (or transparent face covering) significantly reduces the dispersal of oral streptococci - a potential basis for future recommendations. The respiratory tract harbours a less explored, yet rich virome consisting of high concentrations of non-pathogenic viruses. Among these viruses is Torque teno virus, previously suggested as a potential marker for SARS-CoV-2 infection resolution [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e] and intensive care unit admissions [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Current work is investigating the utility of commensal viral markers.\u003c/p\u003e\u003cp\u003eWhilst acknowledging the constraints of our study, including a small cohort size, limited age diversity, and the dimensional constraints of IMADGENN potentially impacting the rate of aerosol decay, we believe its use together with commensal URT bacteria, including streptococci, represents a rapid and efficient system for examining patterns of dispersal (who, why, when) and mitigation strategies for respiratory pathogens. Our current research is expanding upon our findings and further developing and refining this methodology for broader application in the study of respiratory pathogen dispersal.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests. UKHSA acts independently from any company named in this manuscript and does not endorse or promote any of the products or practices evaluated or tested as part of this project.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e\u003cp\u003eGM designed the research; PB, WDC, and NY performed the experiments; GM, PB, and NY analysed the data; NQV performed statistical analysis; PB and GM wrote the manuscript. The manuscript was reviewed by all authors for accuracy.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eThe authors wish to thank Simon Parks, Allan Bennett, and all those who participated in the study, which was supported by funding from UKHSA and the PROTECT COVID-19 National Core Study on transmission and environment, managed by the Health and Safety Executive on behalf of the UK Government. The views expressed are those of the authors and not necessarily those of UKHSA or any other Government Agency.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data generated for this study are available in Figshare repository – https://doi.org/10.6084/m9.figshare.27159852.v1\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChen, Q., Allot, A. \u0026amp; Lu, Z. LitCovid: an open database of COVID-19 literature. \u003cem\u003eNucleic Acids Research\u003c/em\u003e \u003cstrong\u003e49\u003c/strong\u003e, D1534-D1540 (2020). https://doi.org/10.1093/nar/gkaa952\u003c/li\u003e\n\u003cli\u003eWHO. Global technical consultation report on proposed terminology for pathogens that transmit through the air. (Geneva, 2024).\u003c/li\u003e\n\u003cli\u003eJohnson, G. R. \u0026amp; Morawska, L. The mechanism of breath aerosol formation. \u003cem\u003eJ Aerosol Med Pulm Drug Deliv\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 229-237 (2009). https://doi.org/10.1089/jamp.2008.0720\u003c/li\u003e\n\u003cli\u003eStadnytskyi, V., Bax, C. E., Bax, A. \u0026amp; Anfinrud, P. The airborne lifetime of small speech droplets and their potential importance in SARS-CoV-2 transmission. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e \u003cstrong\u003e117\u003c/strong\u003e, 11875-11877 (2020). https://doi.org/doi:10.1073/pnas.2006874117\u003c/li\u003e\n\u003cli\u003eAlsved, M.\u003cem\u003e et al.\u003c/em\u003e Exhaled respiratory particles during singing and talking. \u003cem\u003eAerosol Science and Technology\u003c/em\u003e \u003cstrong\u003e54\u003c/strong\u003e, 1245-1248 (2020). https://doi.org/10.1080/02786826.2020.1812502\u003c/li\u003e\n\u003cli\u003eKato, H.\u003cem\u003e et al.\u003c/em\u003e Expansion of droplets during speaking and singing in Japanese. \u003cem\u003ePLoS One\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, e0272122 (2022). https://doi.org/10.1371/journal.pone.0272122\u003c/li\u003e\n\u003cli\u003eBahl, P.\u003cem\u003e et al.\u003c/em\u003e Droplets and Aerosols Generated by Singing and the Risk of Coronavirus Disease 2019 for Choirs. \u003cem\u003eClin Infect Dis\u003c/em\u003e \u003cstrong\u003e72\u003c/strong\u003e, e639-e641 (2021). https://doi.org/10.1093/cid/ciaa1241\u003c/li\u003e\n\u003cli\u003eLoudon, R. G. \u0026amp; Roberts, R. M. Singing and the dissemination of tuberculosis. \u003cem\u003eAm Rev Respir Dis\u003c/em\u003e \u003cstrong\u003e98\u003c/strong\u003e, 297-300 (1968). https://doi.org/10.1164/arrd.1968.98.2.297\u003c/li\u003e\n\u003cli\u003eMcCarthy, L. P.\u003cem\u003e et al.\u003c/em\u003e Aerosol and droplet generation from performing with woodwind and brass instruments. \u003cem\u003eAerosol Science and Technology\u003c/em\u003e \u003cstrong\u003e55\u003c/strong\u003e, 1277-1287 (2021). https://doi.org/10.1080/02786826.2021.1947470\u003c/li\u003e\n\u003cli\u003eWang, L.\u003cem\u003e et al.\u003c/em\u003e Characterization of aerosol plumes from singing and playing wind instruments associated with the risk of airborne virus transmission. \u003cem\u003eIndoor Air\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, e13064 (2022). https://doi.org/10.1111/ina.13064\u003c/li\u003e\n\u003cli\u003eTanner, K.\u003cem\u003e et al.\u003c/em\u003e Large Particle Emissions from Human Vocalization and Playing of Wind Instruments. \u003cem\u003eEnviron Sci Technol\u003c/em\u003e \u003cstrong\u003e57\u003c/strong\u003e, 15392-15400 (2023). https://doi.org/10.1021/acs.est.3c03588\u003c/li\u003e\n\u003cli\u003eAsadi, S.\u003cem\u003e et al.\u003c/em\u003e Aerosol emission and superemission during human speech increase with voice loudness. \u003cem\u003eScientific Reports\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 2348 (2019). https://doi.org/10.1038/s41598-019-38808-z\u003c/li\u003e\n\u003cli\u003eHan, Z. Y., Weng, W. G. \u0026amp; Huang, Q. Y. Characterizations of particle size distribution of the droplets exhaled by sneeze. \u003cem\u003eJ R Soc Interface\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 20130560 (2013). https://doi.org/10.1098/rsif.2013.0560\u003c/li\u003e\n\u003cli\u003eScharfman, B. E., Techet, A. H., Bush, J. W. M. \u0026amp; Bourouiba, L. Visualization of sneeze ejecta: steps of fluid fragmentation leading to respiratory droplets. \u003cem\u003eExperiments in Fluids\u003c/em\u003e \u003cstrong\u003e57\u003c/strong\u003e, 24 (2016). https://doi.org/10.1007/s00348-015-2078-4\u003c/li\u003e\n\u003cli\u003eHo, K. M. A.\u003cem\u003e et al.\u003c/em\u003e Spatiotemporal droplet dispersion measurements demonstrate face masks reduce risks from singing. \u003cem\u003eScientific Reports\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 24183 (2021). https://doi.org/10.1038/s41598-021-03519-x\u003c/li\u003e\n\u003cli\u003eVerma, S., Dhanak, M. \u0026amp; Frankenfield, J. Visualizing the effectiveness of face masks in obstructing respiratory jets. \u003cem\u003ePhys Fluids (1994)\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 061708 (2020). https://doi.org/10.1063/5.0016018\u003c/li\u003e\n\u003cli\u003eAsadi, S.\u003cem\u003e et al.\u003c/em\u003e Efficacy of masks and face coverings in controlling outward aerosol particle emission from expiratory activities. \u003cem\u003eScientific Reports\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 15665 (2020). https://doi.org/10.1038/s41598-020-72798-7\u003c/li\u003e\n\u003cli\u003eKniesburges, S.\u003cem\u003e et al.\u003c/em\u003e Effects of surgical masks on aerosol dispersion in professional singing. \u003cem\u003eJ Expo Sci Environ Epidemiol\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 727-734 (2022). https://doi.org/10.1038/s41370-021-00385-7\u003c/li\u003e\n\u003cli\u003eGregson, F. K. A.\u003cem\u003e et al.\u003c/em\u003e Analytical challenges when sampling and characterising exhaled aerosol. \u003cem\u003eAerosol Science and Technology\u003c/em\u003e \u003cstrong\u003e56\u003c/strong\u003e, 160-175 (2022). https://doi.org/10.1080/02786826.2021.1990207\u003c/li\u003e\n\u003cli\u003eJohnson, D. F., Druce, J. D., Birch, C. \u0026amp; Grayson, M. L. A quantitative assessment of the efficacy of surgical and N95 masks to filter influenza virus in patients with acute influenza infection. \u003cem\u003eClin Infect Dis\u003c/em\u003e \u003cstrong\u003e49\u003c/strong\u003e, 275-277 (2009). https://doi.org/10.1086/600041\u003c/li\u003e\n\u003cli\u003eYan, J.\u003cem\u003e et al.\u003c/em\u003e Infectious virus in exhaled breath of symptomatic seasonal influenza cases from a college community. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e \u003cstrong\u003e115\u003c/strong\u003e, 1081-1086 (2018). https://doi.org/10.1073/pnas.1716561115\u003c/li\u003e\n\u003cli\u003eMilton, D. K., Fabian, M. P., Cowling, B. J., Grantham, M. L. \u0026amp; McDevitt, J. J. Influenza virus aerosols in human exhaled breath: particle size, culturability, and effect of surgical masks. \u003cem\u003ePLoS Pathog\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, e1003205 (2013). https://doi.org/10.1371/journal.ppat.1003205\u003c/li\u003e\n\u003cli\u003eLeung, N. H. L.\u003cem\u003e et al.\u003c/em\u003e Respiratory virus shedding in exhaled breath and efficacy of face masks. \u003cem\u003eNature Medicine\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e, 676-680 (2020). https://doi.org/10.1038/s41591-020-0843-2\u003c/li\u003e\n\u003cli\u003eDuguid, J. P. The size and the duration of air-carriage of respiratory droplets and droplet-nuclei. \u003cem\u003eJ Hyg (Lond)\u003c/em\u003e \u003cstrong\u003e44\u003c/strong\u003e, 471-479 (1946). https://doi.org/10.1017/s0022172400019288\u003c/li\u003e\n\u003cli\u003eHamburger, M., Jr. \u0026amp; Robertson, O. H. Expulsion of group A hemolytic streptococci in droplets and droplet nuclei by sneezing, coughing and talking. \u003cem\u003eAm J Med\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 690-701 (1948). https://doi.org/10.1016/s0002-9343(48)90392-1\u003c/li\u003e\n\u003cli\u003eBennett, A. M.\u003cem\u003e et al.\u003c/em\u003e Microbial aerosols in general dental practice. \u003cem\u003eBr Dent J\u003c/em\u003e \u003cstrong\u003e189\u003c/strong\u003e, 664-667 (2000). https://doi.org/10.1038/sj.bdj.4800859\u003c/li\u003e\n\u003cli\u003eReid, D. D., Lidwell, O. M. \u0026amp; Williams, R. E. O. Counts of Air-Borne Bacteria as Indices of Air Hygiene. \u003cem\u003eEpidemiology and Infection\u003c/em\u003e \u003cstrong\u003e54\u003c/strong\u003e, 524-532 (1956). https://doi.org/10.1017/S0022172400044806\u003c/li\u003e\n\u003cli\u003eTorrey, J. C. \u0026amp; Lake, M. Streptococci in air as indicator of nasopharyngeal contaminantion. \u003cem\u003eJAMA\u003c/em\u003e \u003cstrong\u003e117\u003c/strong\u003e, 1425-1430 (1941). https://doi.org/doi:10.1001/jama.1941.02820430021006\u003c/li\u003e\n\u003cli\u003eCharlson, E. S.\u003cem\u003e et al.\u003c/em\u003e Topographical continuity of bacterial populations in the healthy human respiratory tract. \u003cem\u003eAm J Respir Crit Care Med\u003c/em\u003e \u003cstrong\u003e184\u003c/strong\u003e, 957-963 (2011). https://doi.org/10.1164/rccm.201104-0655OC\u003c/li\u003e\n\u003cli\u003eMohammadi, A., Esmaeilzadeh, E., Li, Y., Bosch, R. J. \u0026amp; Li, J. Z. SARS-CoV-2 detection in different respiratory sites: A systematic review and meta-analysis. \u003cem\u003eEBioMedicine\u003c/em\u003e \u003cstrong\u003e59\u003c/strong\u003e, 102903 (2020). https://doi.org/10.1016/j.ebiom.2020.102903\u003c/li\u003e\n\u003cli\u003eKinoshita, H.\u003cem\u003e et al.\u003c/em\u003e Improved efficacy of SARS-CoV-2 isolation from COVID-19 clinical specimens using VeroE6 cells overexpressing TMPRSS2 and human ACE2. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 24858 (2024). https://doi.org/10.1038/s41598-024-75038-4\u003c/li\u003e\n\u003cli\u003eLa Scola, B.\u003cem\u003e et al.\u003c/em\u003e Viral RNA load as determined by cell culture as a management tool for discharge of SARS-CoV-2 patients from infectious disease wards. \u003cem\u003eEur J Clin Microbiol Infect Dis\u003c/em\u003e \u003cstrong\u003e39\u003c/strong\u003e, 1059-1061 (2020). https://doi.org/10.1007/s10096-020-03913-9\u003c/li\u003e\n\u003cli\u003eWilliams, R. Generalized Ordered Logit/Partial Proportional Odds Models for Ordinal Dependent Variables. \u003cem\u003eThe Stata Journal\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 58-82 (2006). https://doi.org/10.1177/1536867X0600600104\u003c/li\u003e\n\u003cli\u003eWolfe, R. \u0026amp; Gould, W. An approximate likelihood-ratio test for ordinal response models. \u003cem\u003eStata Technical Bulletin\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e (1998).\u003c/li\u003e\n\u003cli\u003eDhaoui, I.\u003cem\u003e et al.\u003c/em\u003e Mathematical modelling of COVID-19: a systematic review and quality assessment in the early epidemic response phase. \u003cem\u003eInt J Infect Dis\u003c/em\u003e \u003cstrong\u003e116\u003c/strong\u003e, S110 (2022). https://doi.org/10.1016/j.ijid.2021.12.260\u003c/li\u003e\n\u003cli\u003eAdekola, H. A., Adekunle, I. A., Egberongbe, H. O., Onitilo, S. A. \u0026amp; Abdullahi, I. N. Mathematical modeling for infectious viral disease: The COVID-19 perspective. \u003cem\u003eJ Public Aff\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, e2306 (2020). https://doi.org/10.1002/pa.2306\u003c/li\u003e\n\u003cli\u003eLi, Y.\u003cem\u003e et al.\u003c/em\u003e Review of animal transmission experiments of respiratory viruses: Implications for transmission risk of SARS-COV-2 in humans via different routes. \u003cem\u003eRisk Anal\u003c/em\u003e (2023). https://doi.org/10.1111/risa.14129\u003c/li\u003e\n\u003cli\u003eZhou, J.\u003cem\u003e et al.\u003c/em\u003e Viral emissions into the air and environment after SARS-CoV-2 human challenge: a phase 1, open label, first-in-human study. \u003cem\u003eLancet Microbe\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, e579-e590 (2023). https://doi.org/10.1016/s2666-5247(23)00101-5\u003c/li\u003e\n\u003cli\u003eJohnson, G. R.\u003cem\u003e et al.\u003c/em\u003e Modality of human expired aerosol size distributions. \u003cem\u003eJournal of Aerosol Science\u003c/em\u003e \u003cstrong\u003e42\u003c/strong\u003e, 839-851 (2011). https://doi.org/https://doi.org/10.1016/j.jaerosci.2011.07.009\u003c/li\u003e\n\u003cli\u003eYazawa, S.\u003cem\u003e et al.\u003c/em\u003e Evaluation of SARS-CoV-2 isolation in cell culture from nasal/nasopharyngeal swabs or saliva specimens of patients with COVID-19. \u003cem\u003eScientific Reports\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 8893 (2023). https://doi.org/10.1038/s41598-023-35915-w\u003c/li\u003e\n\u003cli\u003eTodsen, T.\u003cem\u003e et al.\u003c/em\u003e Higher SARS-CoV-2 detection of oropharyngeal compared with nasopharyngeal or saliva specimen for molecular testing: a multicentre randomised comparative accuracy study. \u003cem\u003eThorax\u003c/em\u003e \u003cstrong\u003e78\u003c/strong\u003e, 1028-1034 (2023). https://doi.org/10.1136/thorax-2022-219599\u003c/li\u003e\n\u003cli\u003eTo, K. K.\u003cem\u003e et al.\u003c/em\u003e Consistent Detection of 2019 Novel Coronavirus in Saliva. \u003cem\u003eClin Infect Dis\u003c/em\u003e \u003cstrong\u003e71\u003c/strong\u003e, 841-843 (2020). https://doi.org/10.1093/cid/ciaa149\u003c/li\u003e\n\u003cli\u003eW\u0026ouml;lfel, R.\u003cem\u003e et al.\u003c/em\u003e Virological assessment of hospitalized patients with COVID-2019. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e581\u003c/strong\u003e, 465-469 (2020). https://doi.org/10.1038/s41586-020-2196-x\u003c/li\u003e\n\u003cli\u003eYamada, S.\u003cem\u003e et al.\u003c/em\u003e Assessment of SARS-CoV-2 infectivity of upper respiratory specimens from COVID-19 patients by virus isolation using VeroE6/TMPRSS2 cells. \u003cem\u003eBMJ Open Respir Res\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e (2021). https://doi.org/10.1136/bmjresp-2020-000830\u003c/li\u003e\n\u003cli\u003eHuang, N.\u003cem\u003e et al.\u003c/em\u003e SARS-CoV-2 infection of the oral cavity and saliva. \u003cem\u003eNature Medicine\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e, 892-903 (2021). https://doi.org/10.1038/s41591-021-01296-8\u003c/li\u003e\n\u003cli\u003eCappa, C. D.\u003cem\u003e et al.\u003c/em\u003e Expiratory aerosol particle escape from surgical masks due to imperfect sealing. \u003cem\u003eScientific Reports\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 12110 (2021). https://doi.org/10.1038/s41598-021-91487-7\u003c/li\u003e\n\u003cli\u003eStutt, R., Retkute, R., Bradley, M., Gilligan, C. A. \u0026amp; Colvin, J. A modelling framework to assess the likely effectiveness of facemasks in combination with \u0026apos;lock-down\u0026apos; in managing the COVID-19 pandemic. \u003cem\u003eProc Math Phys Eng Sci\u003c/em\u003e \u003cstrong\u003e476\u003c/strong\u003e, 20200376 (2020). https://doi.org/10.1098/rspa.2020.0376\u003c/li\u003e\n\u003cli\u003eRao, I. J., Vallon, J. J. \u0026amp; Brandeau, M. L. Effectiveness of Face Masks in Reducing the Spread of COVID-19: A Model-Based Analysis. \u003cem\u003eMed Decis Making\u003c/em\u003e \u003cstrong\u003e41\u003c/strong\u003e, 988-1003 (2021). https://doi.org/10.1177/0272989x211019029\u003c/li\u003e\n\u003cli\u003eRobinson, J. F.\u003cem\u003e et al.\u003c/em\u003e Efficacy of face coverings in reducing transmission of COVID-19: Calculations based on models of droplet capture. \u003cem\u003ePhys Fluids (1994)\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e, 043112 (2021). https://doi.org/10.1063/5.0047622\u003c/li\u003e\n\u003cli\u003eCatching, A., Capponi, S., Yeh, M. T., Bianco, S. \u0026amp; Andino, R. Examining the interplay between face mask usage, asymptomatic transmission, and social distancing on the spread of COVID-19. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 15998 (2021). https://doi.org/10.1038/s41598-021-94960-5\u003c/li\u003e\n\u003cli\u003eSaunders, G. H., Jackson, I. R. \u0026amp; Visram, A. S. Impacts of face coverings on communication: an indirect impact of COVID-19. \u003cem\u003eInternational Journal of Audiology\u003c/em\u003e \u003cstrong\u003e60\u003c/strong\u003e, 495-506 (2021). https://doi.org/10.1080/14992027.2020.1851401\u003c/li\u003e\n\u003cli\u003eChazelet, S. \u0026amp; Pacault, S. Efficiency of Community Face Coverings and Surgical Masks to Limit the Spread of Aerosol. \u003cem\u003eAnn Work Expo Health\u003c/em\u003e \u003cstrong\u003e66\u003c/strong\u003e, 495-509 (2022). https://doi.org/10.1093/annweh/wxab089\u003c/li\u003e\n\u003cli\u003eLindsley, W. G., Blachere, F. M., Law, B. F., Beezhold, D. H. \u0026amp; Noti, J. D. Efficacy of face masks, neck gaiters and face shields for reducing the expulsion of simulated cough-generated aerosols. \u003cem\u003eAerosol Sci Technol\u003c/em\u003e \u003cstrong\u003e55\u003c/strong\u003e, 449-457 (2021). https://doi.org/10.1080/02786826.2020.1862409\u003c/li\u003e\n\u003cli\u003ePan, J., Harb, C., Leng, W. \u0026amp; Marr, L. C. Inward and outward effectiveness of cloth masks, a surgical mask, and a face shield. \u003cem\u003eAerosol Science and Technology\u003c/em\u003e \u003cstrong\u003e55\u003c/strong\u003e, 718-733 (2021). https://doi.org/10.1080/02786826.2021.1890687\u003c/li\u003e\n\u003cli\u003eTretiakow, D., Tesch, K. \u0026amp; Skorek, A. Mitigation effect of face shield to reduce SARS-CoV-2 airborne transmission risk: Preliminary simulations based on computed tomography. \u003cem\u003eEnviron Res\u003c/em\u003e \u003cstrong\u003e198\u003c/strong\u003e, 111229 (2021). https://doi.org/10.1016/j.envres.2021.111229\u003c/li\u003e\n\u003cli\u003eWoolhouse, M. E.\u003cem\u003e et al.\u003c/em\u003e Heterogeneities in the transmission of infectious agents: implications for the design of control programs. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e \u003cstrong\u003e94\u003c/strong\u003e, 338-342 (1997). https://doi.org/10.1073/pnas.94.1.338\u003c/li\u003e\n\u003cli\u003eEdwards, D. A.\u003cem\u003e et al.\u003c/em\u003e Exhaled aerosol increases with COVID-19 infection, age, and obesity. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e \u003cstrong\u003e118\u003c/strong\u003e (2021). https://doi.org/10.1073/pnas.2021830118\u003c/li\u003e\n\u003cli\u003eGeorge, C. E.\u003cem\u003e et al.\u003c/em\u003e COVID-19 symptoms are reduced by targeted hydration of the nose, larynx and trachea. \u003cem\u003eScientific Reports\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 4599 (2022). https://doi.org/10.1038/s41598-022-08609-y\u003c/li\u003e\n\u003cli\u003eSchumm, B.\u003cem\u003e et al.\u003c/em\u003e Lung aerosol particle emission increases with age at rest and during exercise. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e \u003cstrong\u003e120\u003c/strong\u003e, e2301145120 (2023). https://doi.org/10.1073/pnas.2301145120\u003c/li\u003e\n\u003cli\u003eYang, S., Lee, G. W., Chen, C. M., Wu, C. C. \u0026amp; Yu, K. P. The size and concentration of droplets generated by coughing in human subjects. \u003cem\u003eJ Aerosol Med\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 484-494 (2007). https://doi.org/10.1089/jam.2007.0610\u003c/li\u003e\n\u003cli\u003eLu, Q., Yu, D. \u0026amp; Yang, Y. Group A Streptococcus Is Still at Large. \u003cem\u003eJ Clin Med\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e (2023). https://doi.org/10.3390/jcm12072739\u003c/li\u003e\n\u003cli\u003eMendes-Correa, M. C.\u003cem\u003e et al.\u003c/em\u003e Torquetenovirus in saliva: A potential biomarker for SARS-CoV-2 infection? \u003cem\u003ePLoS One\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, e0256357 (2021). https://doi.org/10.1371/journal.pone.0256357\u003c/li\u003e\n\u003cli\u003eFeghoul, L.\u003cem\u003e et al.\u003c/em\u003e Respiratory torque teno virus load at emergency department visit predicts intensive care unit admission of SARS-CoV-2 infected patients. \u003cem\u003eJ Med Virol\u003c/em\u003e\u003cstrong\u003e95\u003c/strong\u003e, e29319 (2023). https://doi.org/10.1002/jmv.29319\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5202589/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5202589/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThere 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 or is currently unknown. An alternative approach is to use commensal upper respiratory tract (URT) bacteria as index organisms.\u003c/p\u003e\u003cp\u003eHealthy volunteers were asked to perform defined respiratory activities within a purpose-built flexible-film isolator. The dispersal of large (L-RPs) and small respiratory particles (S-RPs) was assessed via passive (settle plates) and active air sampling, respectively. Activities were carried out with or without a face covering.\u003c/p\u003e\u003cp\u003eDispersal of respiratory particles differed with individual. During speech, increased vocal volume was associated with a significant increase in the number of URT bacteria deposited as L-RPs and captured by air samplers (S-RPs). Disposable (IIR) medical face masks significantly reduced deposition of L-RPs and reduced the odds of recovering higher numbers of S-RPs by 87% (CI: 63%, 96%). Transparent face coverings were as effective as (IIR) face masks, but face shields had limited efficacy, particularly when worn by individuals who dispersed high numbers of IRPs.\u003c/p\u003e\u003cp\u003eCommensal URT bacteria can be used to study the risk of respiratory transmission in the absence of the target pathogen.\u003c/p\u003e","manuscriptTitle":"Assessing the Generation and Dispersal of Respiratory Particles Using Upper Respiratory Commensal Bacteria as Index Organisms for Respiratory Pathogens","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2025-10-03 08:39:46","doi":"10.21203/rs.3.rs-5202589/v2","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}},{"code":1,"date":"2024-11-15 22:49:54","doi":"10.21203/rs.3.rs-5202589/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b00553d2-932a-4187-a23e-1b48c9c0f165","owner":[],"postedDate":"October 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-29T15:59:33+00:00","versionOfRecord":{"articleIdentity":"rs-5202589","link":"https://doi.org/10.1038/s41598-025-28373-z","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-24 15:57:12","publishedOnDateReadable":"December 24th, 2025"},"versionCreatedAt":"2025-10-03 08:39:46","video":"","vorDoi":"10.1038/s41598-025-28373-z","vorDoiUrl":"https://doi.org/10.1038/s41598-025-28373-z","workflowStages":[]},"version":"v2","identity":"rs-5202589","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5202589","identity":"rs-5202589","version":["v2"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}