Should public health policy exempt cases with low viral load from isolation during an epidemic?: a modelling study

TL;DR

This study uses a multi-scale agent-based model to evaluate the effectiveness of exempting low viral load COVID-19 cases from isolation, revealing that such policies may slightly increase infections and reduce efficiency.

Contribution

It introduces a flexible, multi-scale agent-based model to assess viral load-based isolation strategies during epidemics, providing insights into their potential impacts.

Findings

Exempting low viral load cases post-peak slightly increases infections.

Such exemptions reduce the efficiency of isolation strategies.

Most low viral load cases after the peak are less contagious.

Abstract

During the COVID-19 pandemic, case isolation emerged as a key non-pharmaceutical intervention in pandemic response. Its effectiveness hinges on the timing of isolation, which is often dictated by the onset of contagiousness. While minimizing isolation delays is crucial, the impact of evolving epidemic dynamics, such as changes in viral load distributions among cases, is less understood. These dynamics could inform more efficient isolation strategies. We developed a multi-scale agent-based model to assess isolation policies that consider viral loads. Our model compares the outcomes of universal isolation with strategies that exempt low viral load cases post-peak. We found that most low viral load cases identified after the peak are less contagious, raising the question of their need for isolation. Our analysis reveals that exempting these individuals slightly increases new infections and, unexpectedly, reduces efficiency, defined as infections averted per isolation. These results advise caution with adaptive isolation policies. Our flexible model can evaluate complex strategies, aiding future public health planning.