Synthetic Temporal Contact Network: 30-day, 1000 Household Population for Proactive Contact Tracing Simulations

This dataset contains a high-resolution, synthetic temporal contact network (ABM30) generated using Bayesian-optimized human mobility models (HuMMs) integrated into epidemic simulation framework MEmilio. The used framework was introduced in "Integrating Human Mobility Models with Epidemic Modeling: A Framework for Generating Synthetic Temporal Contact Networks". The resulting ABM30 temporal contact network supports the results of the paper “Network-based proactive contact tracing: A pre-emptive, degree-based alerting framework for privacy-preserving COVID-19 apps” which introduces a lightweight intervention scheme for proactive contact tracing. The generated network contains a population size of 2048 individuals over a timespan of 30 days. The network consists of a sequence of contact graphs over time (hourly resolution), capturing the spatiotemporal interaction patterns of agents moving between households, workplaces, schools, supermarkets and social events. The datasets includes temporal edge list.