Airborne pathogen transmission mechanisms play a key role in the spread of
infectious diseases such as COVID-19. In this work, we propose a computational
fluid dynamics (CFD) approach to model and statistically characterize airborne
pathogen transmission via pathogen-laden particles in turbulent channels from a
molecular communication viewpoint. To this end, turbulent flows induced by
coughing and the turbulent dispersion of droplets and aerosols are modeled by
using the Reynolds-averaged Navier-Stokes equations coupled with the realizable
k−ϵ model and the discrete random walk model, respectively. Via
simulations realized by a CFD simulator, statistical data for the number of
received particles are obtained. These data are post-processed to obtain the
statistical characterization of the turbulent effect in the reception and to
derive the probability of infection. Our results reveal that the turbulence has
an irregular effect on the probability of infection, which shows itself by the
multi-modal distribution as a weighted sum of normal and Weibull distributions.
Furthermore, it is shown that the turbulent MC channel is characterized via
multi-modal, i.e., sum of weighted normal distributions, or stable
distributions, depending on the air velocity.Comment: Accepted to be published in IEEE Transactions on Molecular,
Biological and Multi-Scale Communication