Mobile smartphone tracing can detect almost all SARS-CoV-2 infections

TL;DR

This paper introduces BETIS, a Bayesian algorithm for mobile tracing that can nearly detect all SARS-CoV-2 infections among users and estimate overall virus prevalence, potentially aiding COVID-19 eradication efforts.

Contribution

The paper presents BETIS, a novel Bayesian-based tracing algorithm that improves infection detection and prevalence estimation using self-reports and a hidden Markov model.

Findings

BETIS can detect almost all SARS-CoV-2 infections among users.

The algorithm accurately estimates virus prevalence in the entire population.

Mobile tracing with BETIS could significantly contribute to COVID-19 eradication.

Abstract

Currently, many countries are considering the introduction of tracing software on mobile smartphones with the main purpose to inform and alarm the mobile app user. Here, we demonstrate that, in addition to alarming and informing, mobile tracing can detect nearly all users that are infected by SARS-CoV-2. Our algorithm BETIS (Bayesian Estimation for Tracing Infection States) makes use of self-reports of the user's health status. Then, BETIS guarantees that almost all SARS-CoV-2 infections of the group of users can be detected. Furthermore, BETIS estimates the virus prevalence in the whole population, consisting of users and non-users. BETIS is based on a hidden Markov epidemic model and recursive Bayesian filtering. The potential that mobile tracing apps, in addition to medical testing and quarantining, can eradicate COVID-19 may persuade citizens to trade-off privacy against public health.