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Mobility-driven synthetic contact matrices as a scalable solution for real-time pandemic response modeling
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  • Published: 27 January 2026

Mobility-driven synthetic contact matrices as a scalable solution for real-time pandemic response modeling

  • Laura Di Domenico1 nAff7,
  • Paolo Bosetti  ORCID: orcid.org/0000-0001-7078-71182,
  • Chiara E. Sabbatini3 nAff8,
  • Lulla Opatowski  ORCID: orcid.org/0000-0001-8348-700X4,5 &
  • …
  • Vittoria Colizza  ORCID: orcid.org/0000-0002-2113-23743,6 

Nature Communications , Article number:  (2026) Cite this article

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Subjects

  • Computational models
  • Epidemiology
  • Infectious diseases

Abstract

Accurately capturing time-varying human behavior remains a major challenge for real-time epidemic modeling and response. During the COVID-19 pandemic, synthetic contact matrices derived from mobility and behavioral data emerged as a scalable alternative to empirical contact surveys, yet their comparative performance remained unclear. Here, we systematically evaluate synthetic and empirical age-stratified contact matrices in France from March 2020 to May 2022, comparing contact patterns and their ability to reproduce observed epidemic dynamics. While both sources captured similar temporal trends in contacts, empirical matrices recorded 3.4 times more contacts for individuals under 19 than synthetic matrices during school-open periods. The model parameterized with synthetic matrices provided the best fit to hospital admissions and best captured hospitalization patterns for adolescents, adults, and seniors, whereas deviations remained for children across both models. Neither matrix allowed models to fully reproduce serological trends in children, highlighting the challenges both approaches face in capturing their disease-relevant contacts. The weekly update of synthetic matrices enabled smoother reconstructions of hospitalization trends during transitional phases, while empirical matrices required strong assumptions between survey waves. These findings support synthetic matrices as a reliable, flexible, cost-effective operational tool for real-time epidemic modeling, and highlight the need for routine collection of age-stratified mobility data to improve pandemic response.

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Data availability

Mobility-driven synthetic contact matrices and SocialCov contact matrices are available at https://github.com/EPIcx-lab/COVID-19/tree/master/mobility_driven_synthetic_contact_matrices. All other data used in the analyses are available at the references cited in the Methods: pre-pandemic contact data12, Google mobility data31, CoviPrev behavioral survey data41, Normalcy Index66, Stringency Index67, French population data70, vaccine uptake71, hospital admission data79, seroprevalence data78.

Code availability

Code of the transmission model is publicly available at https://github.com/EPIcx-lab/COVID-19/tree/master/mobility_driven_synthetic_contact_matrices.

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Acknowledgements

This study was partially funded by: ANR grant DATAREDUX (ANR-19-CE46-0008-03) to V.C.; EU Horizon 2020 grant MOOD (H2020-874850) to V.C., L.D.D.; Horizon Europe grants VERDI (101045989) and ESCAPE (101095619) to V.C.

Author information

Author notes

  1. Laura Di Domenico

    Present address: Data Science Institute, Hasselt University, Hasselt, Belgium

  2. Chiara E. Sabbatini

    Present address: Santé publique France, French National Public Health Agency, Saint-Maurice, France

Authors and Affiliations

  1. Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland

    Laura Di Domenico

  2. Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, U1332 INSERM, Paris, France

    Paolo Bosetti

  3. Sorbonne Université, INSERM, Pierre Louis Institute of Epidemiology and Public Health, Paris, France

    Chiara E. Sabbatini & Vittoria Colizza

  4. Institut Pasteur, Epidemiology and Modelling of Antibiotic Evasion, Université Paris Cité, Paris, France

    Lulla Opatowski

  5. UVSQ, INSERM, CESP, Anti-Infective Evasion and Pharmacoepidemiology Team, Université Paris-Saclay, Montigny-Le-Bretonneux, France

    Lulla Opatowski

  6. Department of Biology, Georgetown University, Washington, DC, USA

    Vittoria Colizza

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Contributions

V.C. and L.D.D. conceived and designed the study. V.C., L.D.D., and C.E.S. developed the framework for the synthetic contact matrices. L.D.D. and C.E.S analyzed the data to build the synthetic contact matrices. P.B. and L.O. collected and analyzed the data from the SocialCov survey. L.D.D. developed the code for the comparison. L.D.D. and C.E.S. performed the numerical simulations. L.D.D. analyzed the results. L.D.D., P.B., L.O., and V.C interpreted the results. L.D.D. drafted the article. All authors contributed to and approved the final version of the Article.

Corresponding author

Correspondence to Vittoria Colizza.

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Di Domenico, L., Bosetti, P., Sabbatini, C.E. et al. Mobility-driven synthetic contact matrices as a scalable solution for real-time pandemic response modeling. Nat Commun (2026). https://doi.org/10.1038/s41467-026-68557-3

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  • Received: 12 December 2024

  • Accepted: 07 January 2026

  • Published: 27 January 2026

  • DOI: https://doi.org/10.1038/s41467-026-68557-3

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