Monitoring behavioural responses during pandemic via reconstructed contact matrices from online and representative surveys

TL;DR

This study develops a scalable method to reconstruct representative contact matrices from online and survey data, enabling real-time monitoring of social mixing patterns during the COVID-19 pandemic.

Contribution

It introduces a novel reconstruction technique combining online and representative survey data to dynamically estimate contact matrices.

Findings

Successful collection of contact data from over 2.3% of the Hungarian population.

Development of a bias correction method for online data.

Demonstrated the method's potential to inform epidemic models.

Abstract

The unprecedented behavioural responses of societies have been evidently shaping the COVID-19 pandemic, yet it is a significant challenge to accurately monitor the continuously changing social mixing patterns in real-time. Contact matrices, usually stratified by age, summarise interaction motifs efficiently, but their collection relies on conventional representative survey techniques, which are expensive and slow to obtain. Here we report a data collection effort involving over $2.3\%$ of the Hungarian population to simultaneously record contact matrices through a longitudinal online and sequence of representative phone surveys. To correct non-representative biases characterising the online data, by using census data and the representative samples we develop a reconstruction method to provide a scalable, cheap, and flexible way to dynamically obtain closer-to-representative contact matrices. Our results demonstrate the potential of combined online-offline data collections to understand the changing behavioural responses determining the future evolution of the outbreak, and inform epidemic models with crucial data.