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.

中文翻译：

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通过在线和代表性调查中重建的联系矩阵，监测大流行期间的行为反应

社会前所未有的行为反应显然已经在塑造COVID-19大流行，但是，要准确地实时监控不断变化的社会混合模式，这是一个巨大的挑战。接触矩阵通常按年龄分层，可以有效地汇总交互作用图案，但它们的收集依赖于常规的代表性调查技术，这种技术昂贵且获取缓慢。在这里，我们报告了一项数据收集工作，涉及超过2.3％的匈牙利人口，以通过纵向在线和有代表性的电话调查序列来同时记录联系矩阵。为了纠正表征在线数据的非代表性偏见，我们使用普查数据和代表性样本开发了一种重构方法，以提供可扩展，廉价，动态获取更接近代表的联系矩阵的灵活方式。我们的结果表明，结合在线-离线数据收集来理解不断变化的行为反应，确定暴发的未来发展趋势，并为流行病模型提供关键数据的潜力。