Dynamics of infection spread and hotspot growth in bi-pathogen networks: A simulation study

Understanding the spatio-temporal evolution of epidemics with multiple pathogens requires models that can express interactions between strains and heterogeneity across regions. Building on the Multiplex Bi-Virus Reaction-Diffusion framework (MBRD) introduced in our companion paper, we use numerical experiments to characterize how the super-infection model (MBRD-SI) and the co-infection model (MBRD-CI) behave across network topologies and parameter regimes. We examine (i) diffusion-driven pattern formation and hotspot growth following perturbations of a homogeneous steady state, and (ii) point-source introductions of two pathogens at distinct locations and times. Within this modeling framework, we quantify how hotspot amplitude, spread indices, and saturation times vary with transmission/removal parameters, interaction coefficients, and layer connectivity. The results are intended as simulation-based evidence of mechanisms and regimes in the MBRD models and are not calibrated to a specific disease dataset. • We study nonlinear reaction–diffusion models for two interacting pathogens on multiplex networks. • We show how Turing-type stationary hotspots can amplify monotonically and trigger collapse. • We map parameter bands that separate growth, saturation, and inhibition. • We analyze practical controls: cross-transmission, removal rates, and layer-degree asymmetry. • We show how and why topology matters: lattice, small-world, and scale-free layers alter speed and peaks. • Quantitative summaries (peak size, saturation time) enable comparability across runs. • Methods are interpretable and training-free; results are reproducible and efficient.