Version 1
: Received: 30 April 2024 / Approved: 30 April 2024 / Online: 30 April 2024 (11:55:18 CEST)
How to cite:
Diallo, D.; Schönfeld, J.; Blanken, T. F.; Hecking, T. Dynamic Contact Networks in Confined Spaces: Synthesizing Micro-Level Encounter Patterns Through Human Mobility Models from Real-World Data. Preprints2024, 2024041998. https://doi.org/10.20944/preprints202404.1998.v1
Diallo, D.; Schönfeld, J.; Blanken, T. F.; Hecking, T. Dynamic Contact Networks in Confined Spaces: Synthesizing Micro-Level Encounter Patterns Through Human Mobility Models from Real-World Data. Preprints 2024, 2024041998. https://doi.org/10.20944/preprints202404.1998.v1
Diallo, D.; Schönfeld, J.; Blanken, T. F.; Hecking, T. Dynamic Contact Networks in Confined Spaces: Synthesizing Micro-Level Encounter Patterns Through Human Mobility Models from Real-World Data. Preprints2024, 2024041998. https://doi.org/10.20944/preprints202404.1998.v1
APA Style
Diallo, D., Schönfeld, J., Blanken, T. F., & Hecking, T. (2024). Dynamic Contact Networks in Confined Spaces: Synthesizing Micro-Level Encounter Patterns Through Human Mobility Models from Real-World Data. Preprints. https://doi.org/10.20944/preprints202404.1998.v1
Chicago/Turabian Style
Diallo, D., Tessa F. Blanken and Tobias Hecking. 2024 "Dynamic Contact Networks in Confined Spaces: Synthesizing Micro-Level Encounter Patterns Through Human Mobility Models from Real-World Data" Preprints. https://doi.org/10.20944/preprints202404.1998.v1
Abstract
This study advances the field of infectious disease forecasting by introducing a novel approach to micro-level contact modeling, leveraging human movement patterns to generate realistic temporal dynamic networks. Through the incorporation of human mobility models and parameter tuning, this research presents an innovative method for simulating micro-level encounters that closely mirror infection dynamics within confined spaces. Central to our methodology is the application of Bayesian optimization for parameter selection, which refines our models to emulate both the properties of real-world infection curves and the characteristics of network properties. By focusing on the distinct aspects of infection propagation within confined spaces, our approach significantly improves the realism of temporal-dynamic contact networks, offering a powerful tool for assessing the impact of specific locations on pandemic dynamics. The resulting models shed light on the role of spatial encounters in disease spread and strengthen the capability to forecast and respond to infectious disease outbreaks. This work not only contributes to the scientific understanding of micro-level transmission patterns but also offers practical insights for public health strategies and digital contact tracing efforts, aiming at more effective intervention and containment measures during pandemics.
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
