The role of time scale in the spreading of asymmetrically interacting diseases

TL;DR

This paper investigates how different time scales affect the spread of asymmetrically interacting diseases, revealing unique stationary behaviors and transient oscillations that enhance understanding of complex contagion processes.

Contribution

It introduces models for asymmetric disease interactions with varying time scales and analyzes their stationary states and oscillatory regimes, advancing the understanding of complex contagion dynamics.

Findings

Stationary prevalences vary with time scale ratios.

Transient oscillations occur in specific regimes.

Different models show distinct phase diagram behaviors.

Abstract

Diseases and other contagion phenomena in nature and society can interact asymmetrically, such that one can benefit from the other, which in turn impairs the first, in analogy with predator-prey systems. Here, we consider two models for interacting disease-like dynamics with asymmetric interactions and different associated time scales. Using rate equations for homogeneously mixed populations, we show that the stationary prevalences and phase diagrams of each model behave differently with respect to variations of the relative time scales. We also characterize in detail the regime where transient oscillations are observed, a pattern that is inherent to asymmetrical interactions but often ignored in the literature. Our results contribute to a better understanding of disease dynamics in particular, and interacting processes in general, and could provide interesting insights for real-world applications, most notably, the interplay between the dynamics of fact-checked and fake news.