Time-dependent heterogeneity leads to transient suppression of the COVID-19 epidemic, not herd immunity

TL;DR

This paper presents a model that incorporates both short-term and long-term heterogeneity in social behavior, showing that transient collective immunity can occur below the herd immunity threshold and that subsequent COVID-19 waves are possible due to behavioral changes.

Contribution

The authors develop a comprehensive approach to model time-dependent heterogeneity in epidemic spread, integrating empirical data and revealing the transient nature of collective immunity.

Findings

Transient collective immunity (TCI) emerges below HIT during early epidemic stages.

Behavioral changes cause TCI to wane, allowing subsequent waves.

Hardest-hit regions like NYC remain below long-term HIT, risking future waves.

Abstract

Epidemics generally spread through a succession of waves that reflect factors on multiple timescales. On short timescales, super-spreading events lead to burstiness and overdispersion, while long-term persistent heterogeneity in susceptibility is expected to lead to a reduction in the infection peak and the herd immunity threshold (HIT). Here, we develop a general approach to encompass both timescales, including time variations in individual social activity, and demonstrate how to incorporate them phenomenologically into a wide class of epidemiological models through parameterization. We derive a non-linear dependence of the effective reproduction number Re on the susceptible population fraction S. We show that a state of transient collective immunity (TCI) emerges well below the HIT during early, high-paced stages of the epidemic. However, this is a fragile state that wanes over time due to changing levels of social activity, and so the infection peak is not an indication of herd immunity: subsequent waves can and will emerge due to behavioral changes in the population, driven (e.g.) by seasonal factors. Transient and long-term levels of heterogeneity are estimated by using empirical data from the COVID-19 epidemic as well as from real-life face-to-face contact networks. These results suggest that the hardest-hit areas, such as NYC, have achieved TCI following the first wave of the epidemic, but likely remain below the long-term HIT. Thus, in contrast to some previous claims, these regions can still experience subsequent waves.