A Computational Approach for the Characterization of Airborne Pathogen Transmission in Turbulent Molecular Communication Channels
arXiv:2305.01412 · doi:10.1109/TMBMC.2023.3273193
Abstract
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 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.
Accepted to be published in IEEE Transactions on Molecular, Biological and Multi-Scale Communications
References in corpus (4)
- The Flow Physics of COVID-19
- Infectious Disease Transmission via Aerosol Propagation from a Molecular Communication Perspective: Shannon Meets Coronavirus
- Duality between Coronavirus Transmission and Air-based Macroscopic Molecular Communication
- Mobile Human Ad Hoc Networks: A Communication Engineering Viewpoint on Interhuman Airborne Pathogen Transmission