%0 Journal Article
%@ 2045-2322
%A  Koltai Júlia Anna
%A  Vásárhelyi Orsolya
%A  Röst Gergely
%A  Karsai Márton
%A Corvinus Institute for Advanced Studies BCE CIAS [2020-],
%A Lendület Recens Kutatócsoport TK Recens Kcs [2012-],
%A Társadalomkutatások Módszertana Tanszék ELTE / TATK [2006-],
%D 2022
%F publicatio:29021
%J SCIENTIFIC REPORTS
%N 1
%T Reconstructing social mixing patterns via weighted contact matrices from online and representative surveys
%U http://publicatio.bibl.u-szeged.hu/29021/
%V 12
%X 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 that although some conventional socio-demographic characters correlate significantly with the change of contact numbers, the strongest predictors can be collected only via surveys techniques and combined with census data for the best reconstruction performance. We demonstrate the potential of combined online-offline data collections to understand the changing behavioural responses determining the future evolution of the outbreak, and to inform epidemic models with crucial data.
%Z Computational Social Science and Research Center for Educational and Network Studies, Centre for Social Sciences, Budapest, 1097, Hungary                         Faculty of Social Sciences, Eötvös Loránd University, Budapest, 1117, Hungary                         Department of Network and Data Science, Central European University, Vienna, 1100, Austria                         Laboratory for Networks, Technology and Innovation, Centre for Advanced Studies, Budapest Corvinus University, Budapest, 1093, Hungary                         Centre for Interdisciplinary Methodologies, University of Warwick, Coventry, United Kingdom                         Bolyai Institute, University of Szeged, Szeged, 6720, Hungary                         Alfréd Rényi Institute of Mathematics, Budapest, 1053, Hungary                         Cited By :2                         Export Date: 21 September 2022                         Correspondence Address: Karsai, M.; Alfréd Rényi Institute of MathematicsHungary; email: karsaim@ceu.edu