Mobility-driven synthetic contact matrices as a scalable solution for real-time pandemic response modeling
Laura Di Domenico1,2, Paolo Bosetti3, Chiara E Sabbatini4,5
1Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
Nature Communications
|January 27, 2026
Summary
Synthetic contact matrices offer a reliable, cost-effective tool for real-time epidemic modeling, outperforming empirical surveys in capturing hospitalization trends during the COVID-19 pandemic.
Area of Science:
- Epidemiology
- Computational biology
- Public health
Background:
- Accurate human behavior data is crucial for real-time epidemic modeling.
- Synthetic contact matrices, derived from mobility data, are a scalable alternative to empirical surveys.
Purpose of the Study:
- To systematically evaluate synthetic versus empirical age-stratified contact matrices.
- To compare their ability to reproduce observed epidemic dynamics in France (March 2020-May 2022).
Main Methods:
- Comparative analysis of synthetic and empirical contact matrices.
- Modeling epidemic dynamics using both matrix types.
- Evaluation of model fit to hospital admissions and serological data.
Main Results:
- Both matrix types showed similar temporal trends, but empirical matrices recorded more contacts for under-19s during school periods.
- Synthetic matrices yielded better model fit for hospital admissions across age groups, particularly adolescents, adults, and seniors.
- Neither matrix fully reproduced pediatric serological trends, indicating challenges in capturing children's contacts.
Conclusions:
- Synthetic contact matrices are a flexible, cost-effective tool for real-time epidemic modeling.
- Routine collection of age-stratified mobility data is needed to improve pandemic response models.
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