A Quantitative Risk Estimation Platform for Indoor Aerosol Transmission of COVID-19

Aerosol transmission has played a significant role in the transmission of COVID-19 disease worldwide. We developed a COVID-19 aerosol transmission risk estimation model to better understand how key parameters associated with indoor spaces and infector emissions affect inhaled deposited dose of aerosol particles that convey the SARS-CoV-2 virus. The model calculates the concentration of size-resolved, virus-laden aerosol particles in well-mixed indoor air challenged by emissions from an index case(s). The model uses a mechanistic approach, accounting for particle emission dynamics, particle deposition to indoor surfaces, ventilation rate, and single-zone filtration. The novelty of this model relates to the concept of inhaled & deposited dose in the respiratory system of receptors linked to a dose-response curve for human coronavirus HCoV-229E. We estimated the volume of inhaled & deposited dose of particles in the 0.5-4 mu m range expressed in picoliters (pL) in a well-documented COVID-19 outbreak in restaurant X in Guangzhou China. We anchored the attack rate with the dose-response curve of HCoV-229E which provides a preliminary estimate of the average SARS-CoV-2 dose per person, expressed in plaque forming units (PFUs). For a reasonable emission scenario, we estimate approximately three PFU per pL deposited, yielding roughly 10 PFUs deposited in the respiratory system of those infected in restaurant X. To explore the model's utility, we tested it with four COVID-19 outbreaks. The risk estimates from the model fit reasonably well with the reported number of confirmed cases given available metadata from the outbreaks and uncertainties associated with model assumptions.

Automated summary

The model estimates the risk of COVID-19 aerosol transmission by calculating the concentration of virus-laden aerosol particles in indoor air and considering parameters such as particle emission dynamics. It was tested with four COVID-19 outbreaks and showed reasonably good fit with the reported number of confirmed cases, taking into account uncertainties associated with model assumptions.