Aerosol emissions from wind instruments: effects of performer age, sex, sound pressure level, and bell covers.

Aerosol emissions from wind instruments are a suspected route of transmission for airborne infectious diseases, such as SARS-CoV-2. We evaluated aerosol number emissions (from 0.25 to 35.15 µm) from 81 volunteer performers of both sexes and varied age (12 to 63 years) while playing wind instruments (bassoon, clarinet, flute, French horn, oboe, piccolo, saxophone, trombone, trumpet, and tuba) or singing. Measured emissions spanned more than two orders of magnitude, ranging in rate from < 8 to 1,815 particles s-1, with brass instruments, on average, producing 191% (95% CI 81-367%) more aerosol than woodwinds. Being male was associated with a 70% increase in emissions (vs. female; 95% CI 9-166%). Each 1 dBA increase in sound pressure level was associated with a 28% increase (95% CI 10-40%) in emissions from brass instruments; sound pressure level was not associated with woodwind emissions. Age was not a significant predictor of emissions. The use of bell covers reduced aerosol emissions from three brass instruments tested (trombone, tuba, and trumpet), with average reductions ranging from 53 to 73%, but not for the two woodwind instruments tested (oboe and clarinet). Results from this work can facilitate infectious disease risk management for the performing arts.

Tracking the transmission dynamics of COVID-19 with a time-varying coefficient state-space model.

The spread of COVID-19 has been greatly impacted by regulatory policies and behavior patterns that vary across counties, states, and countries. Population-level dynamics of COVID-19 can generally be described using a set of ordinary differential equations, but these deterministic equations are insufficient for modeling the observed case rates, which can vary due to local testing and case reporting policies and nonhomogeneous behavior among individuals. To assess the impact of population mobility on the spread of COVID-19, we have developed a novel Bayesian time-varying coefficient state-space model for infectious disease transmission. The foundation of this model is a time-varying coefficient compartment model to recapitulate the dynamics among susceptible, exposed, undetected infectious, detected infectious, undetected removed, hospitalized, detected recovered, and detected deceased individuals. The infectiousness and detection parameters are modeled to vary by time, and the infectiousness component in the model incorporates information on multiple sources of population mobility. Along with this compartment model, a multiplicative process model is introduced to allow for deviation from the deterministic dynamics. We apply this model to observed COVID-19 cases and deaths in several U.S. states and Colorado counties. We find that population mobility measures are highly correlated with transmission rates and can explain complicated temporal variation in infectiousness in these regions. Additionally, the inferred connections between mobility and epidemiological parameters, varying across locations, have revealed the heterogeneous effects of different policies on the dynamics of COVID-19.