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- W3121433761 abstract "Knowing the infection status of surrounding people can be a huge advantage for both self-protection and spreading control in an epidemic outbreak or a flu season. Recently, Bluetooth-based mobile apps have been implemented to perform the contacting tracking. However, self-status estimation and tracking are of the same importance yet less investigated. Unfortunately, existing apps cannot enable users to establish the necessary relationship between others’ infection status and their own status. Meanwhile, traditional infection models are either used for statistics in the general population or have strong constraints that cannot be applied for such usage. In this paper, we propose BASE, a Bluetooth App-based Self-Estimated infection model, which can be integrated with this kind of app to calculate individual user’s own infection probability. BASE only requires basic inputs that are acquirable from any smartphone with Bluetooth 2.0 and above. More importantly, BASE can work under practical scenarios in terms of scalability, mobility, and sensing inaccuracy. Furthermore, a learning model is integrated to calibrate the model by studying the propagation characteristics of heterogeneous diseases. The merits of BASE are proved theoretically and validated against extensive simulations. To the best of our knowledge, this is the first work that investigates the individual’s infection model based upon Bluetooth neighbor discovery techniques." @default.
- W3121433761 created "2021-02-01" @default.
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- W3121433761 date "2020-12-01" @default.
- W3121433761 modified "2023-09-25" @default.
- W3121433761 title "A Bluetooth App-Based Self-Estimated Infection Model for Airborne-Based Epidemic Diseases" @default.
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- W3121433761 doi "https://doi.org/10.1109/globecom42002.2020.9322080" @default.
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