Aerosol measurement identifies SARS-CoV 2 PCR positive adults compared with healthy controls

Quantifying Exhaled Particles in Healthy Humans During Various Respiratory Activities Under Realistic Conditions

Background: Quantitatively collecting and characterizing exhaled aerosols is vital for infection risk assessment, but the entire droplet size spectrum has often been neglected. We analyzed particle number and size distribution of healthy participants in various respiratory activities, considering inter-individual variability, and deployed a simplified far-field model to inform on infection risks. Methods: Participants repeated the same respiratory activities on two visits. Particles were collected using an airtight extraction helmet supplied with High Efficiency Particulate Air (HEPA) filtered air. The sampling volume flow was transported to two particle counters covering the small and large particle spectrum. The applied simple mass balance model included respiratory activity, viral load, room size, and air exchange rates. Results: Thirty participants completed the study. The major fraction of the number-based size distribution was <5 μm in all respiratory activities. In contrast, the major fraction of the volume-based size distribution was 2-12 μm in tidal breathing, but >60 μm in all other activities. Aerosol volume flow was lowest in tidal breathing, 10-fold higher in quiet/normal speaking, deep breathing, coughing, and 100-fold higher in loud speaking/singing. Intra-individual reproducibility was high. Between participants, aerosol volume flow varied by two orders of magnitude in droplets <80 μm, and three orders of magnitude in droplets >80 μm. Simple model calculations not accounting for potential particle size-dependent differences in viral load and infection-related differences were used to model airborne pathogen concentrations. Conclusions: Quantitative analysis of exhaled aerosols for the entire droplet size spectrum as well as the variability in aerosol emission between individuals provides information that can support infection research. Clinical Trial Registration number: NCT04771585.

Comparing and validating air sampling methods for SARS-CoV-2 detection in HVAC ducts of student dorms

The Coronavirus disease 2019 (COVID-19) pandemic demonstrated the threat of airborne pathogenic respiratory viruses such as the airborne Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The ability to detect circulating viruses in a workplace or dormitory setting allows an early warning system that can alert occupants to implement precautions (e.g. masking) and/or trigger individual testing to allow isolation and quarantine measures to halt contagion. This work extends and validates the first successful detection of SARS-CoV-2 virus in dormitory Heating, Ventilation, and Air Conditioning (HVAC) systems and compares different air sampling methods and media types combined with optimized quantitative Reverse-Transcription PCR (qRT-PCR) analysis. The study was performed in two environments; large dormitories of students who underwent periodic testing for COVID-19 (unknown environment) and the HVAC air from a suite with a student who had tested positive for COVID-19 (known dorm). The air sampling methods were performed using Filter Cassettes, BioSampler, AerosolSense Sampler and Button Sampler (with four media types with different pore sizes of 5 μm, 3 μm, 3 μm (gelatin), and 1.2 μm). The SARS-CoV-2 positive air samples were compared with the positive samples collected by individual student campus track tracing methods using PCR testing on saliva and nasopharyngeal samples. The results show a detection rate of 73% in the unknown environment and a 78% detection rate in the known dorm. Our data show that the virus was detectable with all the sampling methods we employed. However, the AerosolSense sampler and BioSampler performed the best at 63% and 61% detection rates, compared to 25% for the Filter Cassettes and 23% for the Button Sampler. Despite the success rate, it is not possible to definitively conclude which method is most sensitive due to the limited number of samples. These results show that with careful sampling and optimized PCR methods, pathogenic respiratory viruses can be detected in large buildings using HVAC return air.

Blazing the trail for innovative tuberculosis diagnostics

The COVID-19 pandemic brought diagnostics into the spotlight in an unprecedented way not only for case management but also for population health, surveillance, and monitoring. The industry saw notable levels of investment and accelerated research which sparked a wave of innovation. Simple non-invasive sampling methods such as nasal swabs have become widely used in settings ranging from tertiary hospitals to the community. Self-testing has also been adopted as standard practice using not only conventional lateral flow tests but novel and affordable point-of-care molecular diagnostics. The use of new technologies, including artificial intelligence-based diagnostics, have rapidly expanded in the clinical setting. The capacity for next-generation sequencing and acceptance of digital health has significantly increased. However, 4 years after the pandemic started, the market for SARS-CoV-2 tests is saturated, and developers may benefit from leveraging their innovations for other diseases; tuberculosis (TB) is a worthwhile portfolio expansion for diagnostics developers given the extremely high disease burden, supportive environment from not-for-profit initiatives and governments, and the urgent need to overcome the long-standing dearth of innovation in the TB diagnostics field. In exchange, the current challenges in TB detection may be resolved by adopting enhanced swab-based molecular methods, instrument-based, higher sensitivity antigen detection technologies, and/or artificial intelligence-based digital health technologies developed for COVID-19. The aim of this article is to review how such innovative approaches for COVID-19 diagnosis can be applied to TB to have a comparable impact.

Exhaled aerosols among PCR-confirmed SARS-CoV-2-infected children

Background

Available data on aerosol emissions among children and adolescents during spontaneous breathing are limited. Our aim was to gain insight into the role of children in the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and whether aerosol measurements among children can be used to help detect so-called superspreaders-infected individuals with extremely high numbers of exhaled aerosol particles.

Methods

In this prospective study, the aerosol concentrations of SARS-CoV-2 PCR-positive and SARS-CoV-2 PCR-negative children and adolescents (2-17 years) were investigated. All subjects were asked about their current health status and medical history. The exhaled aerosol particle counts of PCR-negative and PCR-positive subjects were measured using the Resp-Aer-Meter (Palas GmbH, Karlsruhe, Germany) and compared using linear regression.

Results

A total of 250 children and adolescents were included in this study, 105 of whom were SARS-CoV-2 positive and 145 of whom were SARS-CoV-2 negative. The median age in both groups was 9 years (IQR 7-11 years). A total of 124 (49.6%) participants were female, and 126 (50.4%) participants were male. A total of 81.9% of the SARS-CoV-2-positive group had symptoms of viral infection. The median particle count of all individuals was 79.55 particles/liter (IQR 44.55-141.15). There was a tendency for older children to exhale more particles (1-5 years: 79.54 p/L; 6-11 years: 77.96 p/L; 12-17 years: 98.63 p/L). SARS-CoV-2 PCR status was not a bivariate predictor (t = 0.82, p = 0.415) of exhaled aerosol particle count; however, SARS-CoV-2 status was shown to be a significant predictor in a multiple regression model together with age, body mass index (BMI), COVID-19 vaccination, and past SARS-CoV-2 infection (t = 2.81, p = 0.005). COVID-19 vaccination status was a highly significant predictor of exhaled aerosol particles (p < .001).

Conclusion

During SARS-CoV-2 infection, children and adolescents did not have elevated aerosol levels. In addition, no superspreaders were found.