Emulation of epidemics via Bluetooth-based virtual safe virus spread: Experimental setup, software, and data.

Area of Science:

• Epidemiology
• Computational Biology
• Social Network Analysis

Background:

• Understanding epidemic spread relies on modeling human interactions.
• Previous models often lack real-world, granular data on physical proximity.
• The COVID-19 pandemic highlighted the need for dynamic, data-driven epidemic models.

Purpose of the Study:

• To experimentally evaluate the impact of physical interactions on epidemic spread.
• To collect real-time data on virtual epidemic evolution within a human population.
• To provide an open-source dataset for further epidemiological research.

Main Methods:

• Utilized the Safe Blues Android app for voluntary participants at The University of Auckland.
• Simulated virtual virus strand spread via Bluetooth based on physical proximity.
• Recorded epidemic evolution and presented data on a real-time dashboard.
• Applied a simulation model for parameter calibration and analyzed data in the context of a COVID-19 lockdown.

Main Results:

• Successfully deployed and ran an experiment tracking virtual epidemic spread.
• Collected anonymized, real-time data on population interactions and virtual disease transmission.
• Observed how a real-world lockdown event influenced experimental dynamics.
• Made 2021 experimental data publicly available as an open-source dataset.

Conclusions:

• Physical proximity and interaction duration are key factors in epidemic transmission.
• The experimental setup provides a novel method for collecting granular interaction data.
• The open-source dataset will facilitate future research in epidemic modeling and control.
• Real-world events, like lockdowns, significantly impact population behavior and epidemic dynamics.