Mobile human ad hoc networks: A communication engineering viewpoint on interhuman airborne pathogen transmission

Abstract

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.

Keywords: Airborne pathogen transmission; COVID-19; Epidemiology; Infectious disease; Mobile human ad hoc networks; Molecular communication.

Fig. 1

The spread of an infectious disease through airborne pathogen transmission with communication engineering perspective and effective issues for an indoor sneezing/coughing scenario.

Fig. 2

Communication engineering framework to model the spread of infectious diseases through airborne pathogen transmission and the layered MoHANET architecture.

Fig. 3

Two-layered receiver.

Fig. 4

The mean number of droplets in the cloud and their reception by the RX.

Fig. 5

The spread of an infectious disease in a MoHANET through 2-D space for three different time instances. As time progresses, the number of nodes changes with mobility and the nodes change their state according to their exposure to pathogen-laden droplets.

Fig. 6

Number of infected humans (nodes) in a MoHANET with $\gamma =142.4$, ${\beta }_2=0.037$ and the COVID-19 data of Italy for the first 150 days (31 January 2020–28 June 2020) at the beginning of the pandemic. The root mean square error between these curves is 5110.2.

Fig. 7

Total number of actively infected humans (nodes) in a MoHANET for different threshold values.

Fig. 8

Effect of the received number of droplets on infectious disease spread through airborne transmission.

Fig. 9

Effect of different pause time distributions on infectious disease spread through airborne transmission for $d_{inf}=1$ m and $\gamma =140$.