Spatial Super-Infection and Co-Infection Dynamics in Networks

TL;DR

This paper introduces two new spatial models for the spread of multiple pathogens on networks, capturing complex interactions like super-infection and co-infection, and analyzing pattern formation and stability conditions.

Contribution

It presents novel multiplex reaction-diffusion models for multi-pathogen dynamics, including conditions for pattern instabilities and broad applications beyond epidemiology.

Findings

Conditions for Turing and Turing-Hopf instabilities established

Experimental evidence of epidemic pattern formation provided

Models applicable to information, malware, and transportation networks

Abstract

Understanding interactions between the spread of multiple pathogens during an epidemic is crucial to assessing the severity of infections in human communities. In this paper, we introduce two new Multiplex Bi-Virus Reaction-Diffusion models (MBRD) on multiplex metapopulation networks: the super-infection model (MBRD-SI) and the co-infection model (MBRD-CI). These frameworks capture two-pathogen dynamics with spatial diffusion and cross-diffusion, allowing the prediction of infection clustering and large-scale spatial distributions. We establish conditions for Turing and Turing-Hopf instabilities in both models and provide experimental evidence of epidemic pattern formation. Beyond epidemiology, we discuss applications of the MBRD framework to information propagation, malware diffusion, and urban transportation networks.