Toward understanding the risk of secondary airborne infection: emission of respirable pathogens

Abstract

Certain respiratory tract infections are transmitted through air. Coughing and sneezing by an infected person can emit pathogen-containing particles with diameters less than 10 microm that can reach the alveolar region. Based on our analysis of the sparse literature on respiratory aerosols, we estimated that emitted particles quickly decrease in diameter due to water loss to one-half the initial values, and that in one cough the volume in particles with initial diameters less than 20 microm is 60 x 10(-8) mL. The pathogen emission rate from a source case depends on the frequency of expiratory events, the respirable particle volume, and the pathogen concentration in respiratory fluid. Viable airborne pathogens are removed by exhaust ventilation, particle settling, die-off, and air disinfection methods; each removal mechanism can be assigned a first-order rate constant. The pathogen concentration in well-mixed room air depends on the emission rate, the size distribution of respirable particles carrying pathogens, and the removal rate constants. The particle settling rate and the alveolar deposition fraction depend on particle size. Given these inputs plus a susceptible person's breathing rate and exposure duration to room air, an expected alveolar dosemicrois estimated. If the infectious dose is one organism, as appears to be true for tuberculosis, infection risk is estimated by the expression: R = 1-exp(-micro). Using published tuberculosis data concerning cough frequency, bacilli concentration in respiratory fluid, and die-off rate, we illustrate the model via a plausible scenario for a person visiting the room of a pulmonary tuberculosis case. We suggest that patients termed "superspreaders" or "dangerous disseminators" are those infrequently encountered persons with high values of cough and/or sneeze frequency, elevated pathogen concentration in respiratory fluid, and/or increased respirable aerosol volume per expiratory event such that their pathogen emission rate is much higher than average.

FIGURE 1.. Log-probability plots of particle diameter (μm) versus cumulative percentile by count. The circles show Duguid's cough data, where the diameters are the initial particle diameters. The line labeled “D” shows the expected cumulative percentiles for Duguid's data given a fitted two-parameter lognormal distribution with estimated parameters of GM = 14 μm and GSD = 2.6. The diamonds show the Louden and Roberts cough data, where the diameters are the equilibrium particle diameters. The line labeled “L&R” shows the expected cumulative percentiles for the Louden and Roberts data given a fitted two-parameter lognormal distribution with estimated parameters of GM = 12 μm and GSD = 8.4.

FIGURE 2.. Log-probability plots of particle diameter (μm) versus cumulative percentile by count for the adjusted Louden and Roberts cough data, where the diameters are the presumed initial particle diameters (2× the reported values). The diamonds show the observed cumulative percentiles. The solid line shows the expected cumulative percentiles given a fitted two-parameter lognormal distribution model for the data with the estimates GM = 24 μm and GSD = 8.4. The dashed curved line shows the expected cumulative percentiles given a fitted mixture model of two lognormal distributions for the data. There is a distribution of “small” particles with estimates GM = 9.8 μm and GSD = 9.0, and a distribution of “large” particles with estimates GM = 160 μm and GSD = 1.7. The small particle distribution contains 71% of all the cough particles, and the large particle distribution contains 29%.