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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/140142
Ver los metadatos del registro completo
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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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eng |
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eng |
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http://creativecommons.org/licenses/by-nc-sa/3.0/ Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0) |
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