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Summary
This summary is machine-generated.

This study introduces an Internet of Things (IoT) network to anonymously track individuals and predict COVID-19 infection risk. The system uses WiFi and Bluetooth data to estimate exposure, aiding pandemic control.

Keywords:
COVID-19contagious mapmodelingpandemictrackingvirus spread

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Area of Science:

  • Epidemiology
  • Computer Science
  • Public Health

Background:

  • The COVID-19 pandemic caused significant global health and economic crises.
  • Accurate prediction of infection spread is crucial for pandemic control and future outbreak management.

Purpose of the Study:

  • To design an Internet of Things (IoT) sensing network for anonymous tracking of individuals in crowded areas.
  • To develop mathematical models for estimating exposure time and predicting infection probability.

Main Methods:

  • Utilized WiFi and Bluetooth beacons from mobile devices for anonymized location tracking and triangulation within buildings.
  • Developed a mathematical model to calculate expected user exposure time.
  • Proposed a virus spread model and iterative algorithms for infection probability prediction, even with limited data.

Main Results:

  • Successfully designed an IoT network capable of anonymously tracking individual movements in public spaces.
  • Developed a mathematical framework to quantify inter-user exposure duration.
  • Demonstrated a predictive model for infection probability, adaptable to data limitations.

Conclusions:

  • The proposed IoT sensing network and mathematical models offer a privacy-preserving solution for tracking and predicting infectious disease spread.
  • This approach can be vital for controlling current pandemics and preparing for future global health threats.