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An efficient algorithm to estimate Covid-19 infectiousness risk from BLE-RSSI measurements

Jean-Marie Gorce 1 Malcolm Egan 1 Rémi Gribonval 2
1 MARACAS - Modèle et algorithmes pour des systèmes de communication fiables
CITI - CITI Centre of Innovation in Telecommunications and Integration of services, Inria Grenoble - Rhône-Alpes
2 DANTE - Dynamic Networks : Temporal and Structural Capture Approach
Inria Grenoble - Rhône-Alpes, LIP - Laboratoire de l'Informatique du Parallélisme, IXXI - Institut Rhône-Alpin des systèmes complexes
Abstract : This report describes an algorithm to predict the Covid-19 infectiousness risk from physical contacts through BLE-RSSI measurements. In order to derive a robust risk estimator, the proposed algorithm relies on known physical wireless propagation effects and on technical properties of current BLE interfaces. The proposed algorithm has been tested on the data acquired by the German teams from the Fraunhofer institute in the PEPP-PT European project. We thank them for sharing their data, for their helpful comments and answers. Our algorithm is currently under evaluation on the French data acquired from May 18 to 20, 2020.
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Submitted on : Thursday, May 28, 2020 - 5:32:42 PM
Last modification on : Thursday, January 20, 2022 - 5:31:56 PM


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  • HAL Id : hal-02641630, version 1


Jean-Marie Gorce, Malcolm Egan, Rémi Gribonval. An efficient algorithm to estimate Covid-19 infectiousness risk from BLE-RSSI measurements. [Research Report] RR-9345, Inria Grenoble Rhône-Alpes. 2020. ⟨hal-02641630⟩



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