Heterogeneity in susceptibility dictates the order of epidemiological models

TL;DR

This paper investigates how population heterogeneity in susceptibility affects epidemic dynamics, revealing that certain distributions remain invariant and influence long-term infection predictions, challenging traditional models.

Contribution

It introduces a framework linking susceptibility heterogeneity with epidemic progression, highlighting the importance of distribution shape on outbreak predictions.

Findings

Certain susceptibility distributions remain unchanged during outbreaks.

Power-law behavior emerges in the force of infection due to distribution shape.

Traditional models may overestimate outbreak severity due to ignoring heterogeneity.

Abstract

The fundamental models of epidemiology describe the progression of an infectious disease through a population using compartmentalized differential equations, but do not incorporate population-level heterogeneity in infection susceptibility. We show that variation strongly influences the rate of infection, while the infection process simultaneously sculpts the susceptibility distribution. These joint dynamics influence the force of infection and are, in turn, influenced by the shape of the initial variability. Intriguingly, we find that certain susceptibility distributions (the exponential and the gamma) are unchanged through the course of the outbreak, and lead naturally to power-law behavior in the force of infection; other distributions often tend towards these "eigen-distributions" through the process of contagion. The power-law behavior fundamentally alters predictions of the long-term infection rate, and suggests that first-order epidemic models that are parameterized in the exponential-like phase may systematically and significantly over-estimate the final severity of the outbreak.