Optimal periodic closure for minimizing risk in emerging disease outbreaks

TL;DR

This paper develops an analytical theory for optimal periodic closure strategies to minimize disease outbreaks, showing how closure periods depend on disease parameters and identifying a sweet-spot for effectiveness.

Contribution

It introduces a novel analytical framework for predicting optimal periodic closure durations based on epidemiological parameters for SEIR-like diseases.

Findings

Optimal closure periods increase with reproductive number and incubation periods.

A sweet-spot exists where closure is most effective for diseases with similar incubation and recovery times.

The theory aligns well with numerical simulations, including COVID-19 models.

Abstract

Without vaccines and treatments, societies must rely on non-pharmaceutical intervention strategies to control the spread of emerging diseases such as COVID-19. Though complete lockdown is epidemiologically effective, because it eliminates infectious contacts, it comes with significant costs. Several recent studies have suggested that a plausible compromise strategy for minimizing epidemic risk is periodic closure, in which populations oscillate between wide-spread social restrictions and relaxation. However, no underlying theory has been proposed to predict and explain optimal closure periods as a function of epidemiological and social parameters. In this work we develop such an analytical theory for SEIR-like model diseases, showing how characteristic closure periods emerge that minimize the total outbreak, and increase predictably with the reproductive number and incubation periods of a disease, as long as both are within predictable limits. Using our approach we demonstrate a sweet-spot effect in which optimal periodic closure is maximally effective for diseases with similar incubation and recovery periods. Our results compare well to numerical simulations, including in COVID-19 models where infectivity and recovery show significant variability.