On modeling airborne infection risk

TL;DR

This paper extends the Wells-Riley model to population-level airborne infection risk analysis, considering pathogen transmission modes and biological, physical, and behavioral factors, with implications for epidemic dynamics.

Contribution

It introduces a population-level model linking epidemiological dynamics with airborne infection risk, incorporating biological and behavioral parameters affecting transmission.

Findings

Infection risk increases with shorter viral latency periods.

Longer exposure duration raises infection probability.

Risk dynamics follow the infected population's epidemic curve.

Abstract

Airborne infection risk analysis is usually performed for enclosed spaces where susceptible individuals are exposed to infectious airborne respiratory droplets by inhalation. It is usually based on exponential, dose-response models of which a widely used variant is the Wells-Riley (WR) model. We revisit this infection-risk estimate and extend it to the population level. We use an epidemiological model where the mode of pathogen transmission, either airborne or contact, is explicitly considered. We illustrate the link between epidemiological models and the WR model. We argue that airborne infection quanta are, up to an overall density, airborne infectious respiratory droplets modified by a parameter that depends on biological properties of the pathogen, physical properties of the droplet, and behavioural parameters of the individual. We calculate the time-dependent risk to be infected during the epidemic for two scenarios. We show how the epidemic infection risk depends on the viral latent period and the event time, the time infection occurs. The infection risk follows the dynamics of the infected population. As the latency period decreases, infection risk increases. The longer a susceptible is present in the epidemic, the higher is its risk of infection by equal exposure time to the mode of transmission.