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On modelling airborne infection risk

Cite this dataset

Drossinos, Yannis; Stilianakis, Nikolaos (2024). On modelling airborne infection risk [Dataset]. Dryad. https://doi.org/10.5061/dryad.x0k6djhs4

Abstract

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 the viral latent period and the event time, the time infection occurs. 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 its risk of infection for equal exposure time to the mode of transmission is.

README: On modelling airborne infection risk

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 must specify the latent period of the pathogen and the risk time 

The code SEIR_DC_Risk_Plot_Final.m reads the MAT-files and produces the figures in the manuscript. 

Sharing/Access information

No original data were used. We produced numerical results based on parameters from the literature and we developed a MATLAB code for this purpose.

Code/Software

See "Description of the data and file structure". We used the MATLAB version R2022b Update 7, 64-bit (maci64).

Methods

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.