Effectiveness of isolation measures with app support to contain COVID-19 epidemics: a parametric approach

TL;DR

This paper presents a mathematical model to evaluate how app-supported isolation measures can effectively reduce COVID-19 transmission, emphasizing early detection and contact tracing.

Contribution

It introduces a parametric model quantifying the impact of isolation measures, including app usage, on epidemic suppression, with practical computations and a public tool.

Findings

Early detection and contact tracing significantly enhance suppression.

Higher app adoption rates improve containment effectiveness.

Quantitative analysis of variable impacts guides epidemic control strategies.

Abstract

In this study, we analyze the effectiveness of measures aimed at finding and isolating infected individuals to contain epidemics like COVID-19, as the suppression induced over the effective reproduction number. We develop a mathematical model to compute the relative suppression of the effective reproduction number of an epidemic that such measures produce. This outcome is expressed as a function of a small set of parameters that describe the main features of the epidemic and summarize the effectiveness of the isolation measures. In particular, we focus on the impact when a fraction of the population uses a mobile application for epidemic control. Finally, we apply the model to COVID-19, providing several computations as examples, and a link to a public repository to run custom calculations. These computations display in a quantitative manner the importance of recognizing infected individuals from symptoms and contact-tracing information, and isolating them as early as possible. The computations also assess the impact of each variable on the suppression of the epidemic.