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

## TL;DR

This paper compares synthetic and empirical contact matrices for pandemic modeling, finding synthetic ones more effective for real-time tracking and hospitalization predictions.

## Contribution

The study provides the first systematic evaluation of synthetic contact matrices against empirical ones for real-time pandemic modeling.

## Key findings

- Synthetic matrices captured hospitalization trends better than empirical ones for most age groups.
- Synthetic matrices recorded fewer contacts for children under 19 during school-open periods.
- Neither matrix fully reproduced serological trends in children, indicating limitations in capturing their disease-relevant contacts.

## 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.

Contact matrices are used to describe social mixing patterns and inform mathematical models of disease transmission. Here, the authors evaluate the accuracy of synthetic contact matrices generated using mobility data compared to those derived empirically through surveys.