On modelling airborne infection risk

Airborne infection risk analysis is usually performed for enclosed spaces where susceptible indi- viduals 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 epidemiolog- ical model where the mode of pathogen transmission, airborne or contact, is explicitly considered. We illustrate the link between epidemiological models and the WR and the Gammaitoni and Nucci models. 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 for two scenarios. We show how the epidemic infection risk de- pends on th..., The study has no original data. The numerical results of this study are available within this paper. The code that produced the results is available here. The parameter values and their justification are in the Electronic Supplementary Material of this paper., , # On modelling airborne infection risk

[https://doi.org/10.5061/dryad.x0k6djhs4](https://doi.org/10.5061/dryad.x0k6djhs4)

No original data were used. We generated data via numerical simulations of four epidemic scenarios to calculate the epidemic risk. As described in the manuscript, these were specified by low (0.1 days)/high (6.0) pathogen latent periods and low (1.0 day)/high(7.0)  risk times.

## Description of the data and file structure

The epidemic-risk data were generated via the MATLAB code SEIR_DC_Simulations_Final.m which uses the ODE solver dropletODEs. The code contains all the necessary input data, which are described in detail in the Electronic Supplementary Material.

The output simulation data generated by SEIR_DC_Simulations_Final.m are stored in the MAT-files Epi*Low(High)Low(High)*Days.mat where the first Low/High refers to the pathogen latent period and the second Low/High to the risk period. Each MAT-file corresponds to a scenario simulation for which the user m...