Risk Estimation in COVID-19 Contact Tracing Apps

In the context of COVID-19, contact tracing has shown its value as a tool for contention of the pandemic. In addition to its paper based form, contact tracing can be carried out in a more scalable and faster way by using digital apps. Mobile phones can record digital signals emitted by communication...

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Detalles Bibliográficos
Autores principales: Bellassai, Juan C., Madoery, Pablo G., Detke, Ramiro, Blanco, Lucas, Comerci, Sandro, Marattin, María S., Fraire, Juan, González Montoro, Aldana, Britos,Grisel, Ojeda, Silvia, Finochietto, Jorge M.
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2021
Materias:
Ciencias Informáticas
COVID-19
Bluetooth Low Energy
Contact tracing
Proximity estimation
Feature selection
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/140142
http://50jaiio.sadio.org.ar/pdfs/agranda/AGRANDA-07.pdf
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-140142
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
COVID-19
Bluetooth Low Energy
Contact tracing
Proximity estimation
Feature selection
spellingShingle Ciencias Informáticas
COVID-19
Bluetooth Low Energy
Contact tracing
Proximity estimation
Feature selection
Bellassai, Juan C.
Madoery, Pablo G.
Detke, Ramiro
Blanco, Lucas
Comerci, Sandro
Marattin, María S.
Fraire, Juan
González Montoro, Aldana
Britos,Grisel
Ojeda, Silvia
Finochietto, Jorge M.
Risk Estimation in COVID-19 Contact Tracing Apps
topic_facet Ciencias Informáticas
COVID-19
Bluetooth Low Energy
Contact tracing
Proximity estimation
Feature selection
description In the context of COVID-19, contact tracing has shown its value as a tool for contention of the pandemic. In addition to its paper based form, contact tracing can be carried out in a more scalable and faster way by using digital apps. Mobile phones can record digital signals emitted by communication and sensing technologies, enabling the identification of risky contacts between users. Factors such as proximity, encounter duration, environment, ventilation, and the use (or not) of protective measures contribute to the probability of contagion. Estimation of these factors from the data collected by phones remains a challenge. In this work in progress we describe some of the challenges of digital contact tracing, the type of data that can be collected with mobile phones and focus particularly on the problem of proximity estimation using Bluetooth Low Energy (BLE) signals. Specifically, we use machine learning models fed with different combinations of statistical features derived from the BLE signal and study how improvements in accuracy can be obtained with respect to reference models currently in use.
format Objeto de conferencia
Objeto de conferencia
author Bellassai, Juan C.
Madoery, Pablo G.
Detke, Ramiro
Blanco, Lucas
Comerci, Sandro
Marattin, María S.
Fraire, Juan
González Montoro, Aldana
Britos,Grisel
Ojeda, Silvia
Finochietto, Jorge M.
author_facet Bellassai, Juan C.
Madoery, Pablo G.
Detke, Ramiro
Blanco, Lucas
Comerci, Sandro
Marattin, María S.
Fraire, Juan
González Montoro, Aldana
Britos,Grisel
Ojeda, Silvia
Finochietto, Jorge M.
author_sort Bellassai, Juan C.
title Risk Estimation in COVID-19 Contact Tracing Apps
title_short Risk Estimation in COVID-19 Contact Tracing Apps
title_full Risk Estimation in COVID-19 Contact Tracing Apps
title_fullStr Risk Estimation in COVID-19 Contact Tracing Apps
title_full_unstemmed Risk Estimation in COVID-19 Contact Tracing Apps
title_sort risk estimation in covid-19 contact tracing apps
publishDate 2021
url http://sedici.unlp.edu.ar/handle/10915/140142
http://50jaiio.sadio.org.ar/pdfs/agranda/AGRANDA-07.pdf
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