On Mutual Information Analysis of Infectious Disease Transmission via Particle Propagation

TL;DR

This paper analyzes infectious disease transmission via particle propagation using mutual information, providing analytical and simulation-based insights into infection rates across various indoor environments.

Contribution

It introduces a mutual information framework to evaluate infection performance and analyzes five basic channel models for pathogen transmission.

Findings

Particle transfer significantly contributes to diseases like COVID-19 and influenza.

Analytical infection rate expressions are derived for basic channel models.

Numerical simulations illustrate transmission dynamics in different indoor settings.

Abstract

Besides mimicking bio-chemical and multi-scale communication mechanisms, molecular communication forms a theoretical framework for virus infection processes. Towards this goal, aerosol and droplet transmission has recently been modeled as a multiuser scenario. In this letter, the "infection performance" is evaluated by means of a mutual information analysis, and by an even simpler probabilistic performance measure which is closely related to absorbed viruses. The so-called infection rate depends on the distribution of the channel input events as well as on the transition probabilities between channel input and output events. The infection rate is investigated analytically for five basic discrete memoryless channel models. Numerical results for the transition probabilities are obtained by Monte Carlo simulations for pathogen-laden particle transmission in four typical indoor environments: two-person office, corridor, classroom, and bus. Particle transfer contributed significantly to infectious diseases like SARS-CoV-2 and influenza.