ArticleScientific reports2022
Reconstructing social mixing patterns via weighted contact matrices from online and representative surveys.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 31 citations in OpenAlex.
- Health inequalities in risk of SARS-CoV-2 infection in England's second wave: population-based cross-sectional analysis from the REACT-1 study.BMJ public health · 2026Article
- Social inequalities in vaccine coverage and their effects on epidemic spreading.PLoS computational biology · 2025Article
- Quality of evidence in the oral health international data: Contributions for a global profile.PloS one · 2025Article
- Quantifying H5N1 outbreak potential and control effectiveness in high-risk agricultural populations.PLOS global public health · 2025Article
- Optimal vaccination policy to prevent endemicity: a stochastic model.Journal of mathematical biology · 2024Article
- Generalized contact matrices allow integrating socioeconomic variables into epidemic models.Science advances · 2024Article
- Vaccination homophily in ego contact networks during the COVID-19 pandemic.Scientific reports · 2024Article
- Socioeconomic determinants and reasons for non-acceptance to vaccination recommendations during the 3BMC public health · 2024Article
- Article
- Importance of social inequalities to contact patterns, vaccine uptake, and epidemic dynamics.Nature communications · 2024Article
- Review
- Real-time estimation of the effective reproduction number of COVID-19 from behavioral data.Scientific reports · 2023Article
- Epidemic dynamics in census-calibrated modular contact network.Network modeling and analysis in health informatics and bioinformatics · 2023Article
- Understanding hesitancy with revealed preferences across COVID-19 vaccine types.Scientific reports · 2022Article
- Addressing the socioeconomic divide in computational modeling for infectious diseases.Nature communications · 2022Article
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Authors and funding
4 authors at 4 institutions in 3 countries.
Funding
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 [Formula: see text] 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.