Heterogeneous Population Dynamics and Scaling Laws near Epidemic Outbreaks

TL;DR

This paper investigates how heterogeneity and stochastic effects influence epidemic dynamics and warning signs, revealing that while qualitative behavior remains similar, quantitative predictions require careful interpretation due to heterogeneity.

Contribution

It demonstrates the analogous global behavior of homogeneous and heterogeneous SIS models and analyzes how heterogeneity affects early-warning signals for epidemic outbreaks.

Findings

Heterogeneity significantly impacts scaling laws used as early-warning signs.

Qualitative epidemic dynamics are similar in homogeneous and heterogeneous models.

Quantitative predictions for outbreaks must account for heterogeneity to be accurate.

Abstract

In this paper, we focus on the influence of heterogeneity and stochasticity of the population on the dynamical structure of a basic susceptible-infected-susceptible (SIS) model. First we prove that, upon a suitable mathematical reformulation of the basic reproduction number, the homogeneous system and the heterogeneous system exhibit a completely analogous global behaviour. Then we consider noise terms to incorporate the fluctuation effects and the random import of the disease into the population and analyse the influence of heterogeneity on warning signs for critical transitions (or tipping points). This theory shows that one may be able to anticipate whether a bifurcation point is close before it happens. We use numerical simulations of a stochastic fast-slow heterogeneous population SIS model and show various aspects of heterogeneity have crucial influences on the scaling laws that are used as early-warning signs for the homogeneous system. Thus, although the basic structural qualitative dynamical properties are the same for both systems, the quantitative features for epidemic prediction are expected to change and care has to be taken to interpret potential warning signs for disease outbreaks correctly.