Estimating high-resolution daily encounter networks with activity-based models of travel demand
Résumé
This study introduces a novel approach to estimating human contact patterns in densely populated urban and transportation environments, focusing on Île-de-France. We construct a behavioral synthetic population using activity-based models of travel demand and various mobility data sources. This is followed by developing an efficient algorithm to estimate large-scale, multi-setting contact networks, incorporating a calibration method that integrates aggregate contact statistics from literature. Our results reveal that these networks effectively capture the complex social interactions of the population, aligning with existing contact rates and age-specific matrices, like those in Béraud et al. (2015). This research provides high-resolution, age-specific contact matrices and comprehensive contact networks, offering valuable insights for analyzing infectious disease spread in complex urban areas.