Real-time estimation of the effective reproduction number of COVID-19 from behavioral data

TL;DR

This paper introduces a method to estimate the COVID-19 effective reproduction number in real-time using behavioral contact data, providing a more reliable alternative to traditional case-based estimates during biased reporting periods.

Contribution

It presents a novel approach that utilizes daily age-stratified contact matrices from behavioral data to dynamically estimate the reproduction number, complementing existing surveillance methods.

Findings

Behavioral data can improve real-time epidemic monitoring.

The method accurately tracked COVID-19 waves in Hungary.

Behavioral estimates can outperform traditional case-based methods during biases.

Abstract

Near-real time estimations of the effective reproduction number are among the most important tools to track the progression of a pandemic and to inform policy makers and the general public. However, these estimations rely on reported case numbers, commonly recorded with significant biases. The epidemic outcome is strongly influenced by the dynamics of social contacts, which are neglected in conventional surveillance systems as their real-time observation is challenging. Here, we propose a concept using online and offline behavioral data, recording age-stratified contact matrices at a daily rate. Modeling the epidemic using the reconstructed matrices we dynamically estimate the effective reproduction number during the two first waves of the COVID-19 pandemic in Hungary. Our results demonstrate how behavioral data can be used to build alternative monitoring systems complementing the established public health surveillance. They can identify and provide better signals during periods when official estimates appear unreliable due to observational biases.