Power-Law Population Heterogeneity Governs Epidemic Waves

TL;DR

This paper extends the classic epidemic model to include population heterogeneity using a power-law distribution, providing exact solutions and showing that heterogeneity significantly impacts herd immunity and epidemic size.

Contribution

The authors introduce a simple yet powerful generalization of the SIR model incorporating a power-law heterogeneity parameter, enabling exact epidemic predictions.

Findings

Heterogeneous populations reach herd immunity at lower levels.

The model accurately fits SARS-CoV-2 data in Germany.

Population heterogeneity influences epidemic progression and mitigation effects.

Abstract

We generalize the Susceptible-Infected-Removed model for epidemics to take into account generic effects of heterogeneity in the degree of susceptibility to infection in the population. We introduce a single new parameter corresponding to a power-law exponent of the susceptibility distribution that characterizes the population heterogeneity. We show that our generalized model is as simple as the original model which is contained as a limiting case. Because of this simplicity, numerical solutions can be generated easily and key properties of the epidemic wave can still be obtained exactly. In particular, we present exact expressions for the herd immunity level, the final size of the epidemic, as well as for the shape of the wave and for observables that can be quantified during an epidemic. We find that in strongly heterogeneous populations the epidemic reaches only a small fraction of the population. This implies that the herd immunity level can be much lower than in commonly used models with homogeneous populations. Using our model to analyze data for the SARS-CoV-2 epidemic in Germany shows that the reported time course is consistent with several scenarios characterized by different levels of immunity. These scenarios differ in population heterogeneity and in the time course of the infection rate, for example due to mitigation efforts or seasonality. Our analysis reveals that quantifying the effects of mitigation requires knowledge on the degree of heterogeneity in the population. Our work shows that key effects of population heterogeneity can be captured without increasing the complexity of the model. We show that information about population heterogeneity will be key to understand how far an epidemic has progressed and what can be expected for its future course.