Risk Estimation in COVID-19 Contact Tracing Apps

Autores
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.
Año de publicación
2021
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
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.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
COVID-19
Bluetooth Low Energy
Contact tracing
Proximity estimation
Feature selection
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/140142

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spelling Risk Estimation in COVID-19 Contact Tracing AppsBellassai, Juan C.Madoery, Pablo G.Detke, RamiroBlanco, LucasComerci, SandroMarattin, María S.Fraire, JuanGonzález Montoro, AldanaBritos,GriselOjeda, SilviaFinochietto, Jorge M.Ciencias InformáticasCOVID-19Bluetooth Low EnergyContact tracingProximity estimationFeature selectionIn 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.Sociedad Argentina de Informática e Investigación Operativa2021-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf29-35http://sedici.unlp.edu.ar/handle/10915/140142enginfo:eu-repo/semantics/altIdentifier/url/http://50jaiio.sadio.org.ar/pdfs/agranda/AGRANDA-07.pdfinfo:eu-repo/semantics/altIdentifier/issn/2683-8966info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/3.0/Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:35:34Zoai:sedici.unlp.edu.ar:10915/140142Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:35:34.42SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Risk Estimation in COVID-19 Contact Tracing Apps
title Risk Estimation in COVID-19 Contact Tracing Apps
spellingShingle Risk Estimation in COVID-19 Contact Tracing Apps
Bellassai, Juan C.
Ciencias Informáticas
COVID-19
Bluetooth Low Energy
Contact tracing
Proximity estimation
Feature selection
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
dc.creator.none.fl_str_mv 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 Bellassai, Juan C.
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_role author
author2 Madoery, Pablo G.
Detke, Ramiro
Blanco, Lucas
Comerci, Sandro
Marattin, María S.
Fraire, Juan
González Montoro, Aldana
Britos,Grisel
Ojeda, Silvia
Finochietto, Jorge M.
author2_role author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
COVID-19
Bluetooth Low Energy
Contact tracing
Proximity estimation
Feature selection
topic Ciencias Informáticas
COVID-19
Bluetooth Low Energy
Contact tracing
Proximity estimation
Feature selection
dc.description.none.fl_txt_mv 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.
Sociedad Argentina de Informática e Investigación Operativa
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.
publishDate 2021
dc.date.none.fl_str_mv 2021-10
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rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/3.0/
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