Dynamic Contact Networks in Confined Spaces: Synthesizing Micro-Level Encounter Patterns Through Human Mobility Models from Real-World Data

• DOI: 10.20944/preprints202404.1998.v1
• Publication Date: 2024-05-07T08:23:10Z
• Authors: Diaoulé Diallo; Jurij Schönfeld; Tessa F. Blanken; Tobias Hecking
• Keywords: Real world data

This study advances the field of infectious disease forecasting by introducing a novel approach to micro-level contact modeling, leveraging human movement patterns generate realistic temporal dynamic networks. Through incorporation mobility models and parameter tuning, this research presents an innovative method for simulating encounters that closely mirror infection dynamics within confined spaces. Central our methodology is application Bayesian optimization selection, which refines emulate both properties real-world curves characteristics network properties. By focusing on distinct aspects propagation spaces, significantly improves realism temporal-dynamic networks, offering powerful tool assessing impact specific locations pandemic dynamics. The resulting shed light role spatial in spread strengthen capability forecast respond outbreaks. work not only contributes scientific understanding transmission but also offers practical insights public health strategies digital tracing efforts, aiming at more effective intervention containment measures during pandemics.