Mobile Human Ad Hoc Networks: A Communication Engineering Viewpoint on Interhuman Airborne Pathogen Transmission



Fatih Gulec, Baris Atakan, Falko Dressler


A number of transmission models for airborne pathogens transmission, as required to understand airborne infectious diseases such as COVID-19, have been proposed independently from each other, at different scales, and by researchers from various disciplines. We propose a communication engineering approach that blends different disciplines such as epidemiology, biology, medicine, and fluid dynamics. The aim is to present a unified framework using communication engineering, and to highlight future research directions for modeling the spread of infectious diseases through airborne transmission. We introduce the concept of mobile human ad hoc networks (MoHANETs), which exploits the similarity of airborne transmission-driven human groups with mobile ad hoc networks and uses molecular communication as the enabling paradigm. In the MoHANET architecture, a layered structure is employed where the infectious human emitting pathogen-laden droplets and the exposed human to these droplets are considered as the transmitter and receiver, respectively. Our proof-of-concept results, which we validated using empirical COVID-19 data, clearly demonstrate the ability of our MoHANET architecture to predict the dynamics of infectious diseases by considering the propagation of pathogen-laden droplets, their reception and mobility of humans.



Dr. Gulec -- Thank you for the very interesting post & linking the paper, and being among the first to try the new arXiv/openAI functionality. Question on your preprint: does your model "know" the underlying laws of physics, like Navier-Stokes or Bernoulli equations, or it's "assumption free?" Also, what are the input parameters (let's say how would you differentiate between the COVID and say HMV viral particles?) and the output data? 
Thank you,
Question from a relevant research @ScienceCast Board.


The main idea of the proposed MoHANET framework is to use analytical approaches rather than statistical data. We use the underlying fluid dynamics based on our previous paper. With a communication perspective between two people, we derived the probability of infection based on the physical parameters such as emitted number of droplets in a cough, or the initial velocity of a cough etc. This probability is used as a parameter in the SIR epidemiological model to estimate the long term time course of an epidemic. In MoHANET paper, we also took into account the mobility of humans. Hence, we connected the mobility, the fluid dynamics of infectious disease spread and its long-term epidemiological model.


For interested readers the original published paper can accessed via this link:

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