Herd Immunity with Spatial Adaptation Based on Global Prevalence Information

TL;DR

This paper analyzes how spatially adaptive behaviors based on global prevalence information influence epidemic dynamics, identifying thresholds and conditions for effective outbreak control and oscillation emergence.

Contribution

It introduces and compares three models of spatial adaptation based on global prevalence, deriving analytical thresholds and bounds for epidemic mitigation.

Findings

Linear adaptation offers no advantage over constant response.

Super-linear responses are needed for epidemic suppression.

Sigmoidal adaptation can cause prevalence oscillations.

Abstract

During an epidemic outbreak, individuals often modify their behavior in response to global prevalence cues, using spatially mediated adaptations such as reduced mobility or transmission range. In this work, we investigate the impact of distance-based adaptive behaviors on epidemic dynamics, where a fraction of the population adjusts its transmission range and susceptibility to infection based on global prevalence. We consider three adaptation scenarios: a constant adaptive fraction, a power-law dependence and a sigmoidal dependence of adaptive fraction on global prevalence. In the spatially well-mixed regime, we analytically obtain critical adaptation thresholds necessary for epidemic mitigation and in the spatially static regime, we establish bounds for the thresholds using continuum percolation results. Our results indicate that a linear adaptive response to prevalence provides no additional advantage over a constant adaptive fraction in controlling outbreaks, and a highly super-linear response is required to suppress epidemic spread. For a sigmoidal adaptation, we identify conditions under which oscillations in prevalence can emerge, with peak prevalence exhibiting a non-monotonic dependence on the width of the sigmoidal function, suggesting an optimal parameter range that minimizes epidemic severity. We obtain prevalence, final epidemic size, and peak prevalence as functions of adaptation parameters in all adaptation scenarios considered, providing a comprehensive characterization of the effects of spatial adaptation based on global prevalence information in shaping adaptive epidemic dynamics.