Quantifying quanta: Determining emission rates from clinical data - Benjamin Jones; Christopher Iddon; Max Sherman (Elsevier, 2024)
It is important to quantify uncertainty in the viable genomic material encapsulated in the respiratory particles emitted by infected people so that it can be converted into an emission rate as a function of respiratory and metabolic activities and used to estimate the probability of infection for an indoor scenario. Clinical measurements of viral loads for SARS-CoV-2 made using infection surveys, Gesundheit-II samplers, and human challenge studies are evaluated and a mathematical model is derived to estimate the quantum emission rate as a function of the genomic and viable viral loads. Modelled emission rates for SARS-CoV-2 agree with clinical data above detection limits. The viral load is found to vary over at least 6 orders of magnitude because it is person and time dependent, and contingent on many other factors that are difficult to quantify. It is similarly large for other respiratory pathogens. Therefore, the genomic and viable-virion emission rates display similar heterogeneity. When emission rates are used to estimate absolute infection risk using the Wells-Riley model, the predictions are so uncertain that they cannot be used in any meaningful way to provide useful quantitative guidance for designing indoor spaces.