Estimating COVID-19 cases and outbreaks on-stream through phone calls

Autores
Alvarez, Ezequiel; Obando, Daniela; Crespo, Sebastian; Garcia, Enio; Kreplak, Nicolas; Marsico, Franco
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
One of the main problems in controlling COVID-19 epidemic spread is the delay in confirming cases. Having information on changes in the epidemic evolution or outbreaks rise before laboratory-confirmation is crucial in decision making for Public Health policies. We present an algorithm to estimate on-stream the number of COVID-19 cases using the data from telephone calls to a COVID-line. By modelling the calls as background (proportional to population) plus signal (proportional to infected), we fit the calls in Province of Buenos Aires (Argentina) with coefficient of determination R 2 > 0.85. This result allows us to estimate the number of cases given the number of calls from a specific district, days before the laboratory results are available. We validate the algorithm with real data. We show how to use the algorithm to track on-stream the epidemic, and present the Early Outbreak Alarm to detect outbreaks in advance of laboratory results. One key point in the developed algorithm is a detailed track of the uncertainties in the estimations, since the alarm uses the significance of the observables as a main indicator to detect an anomaly. We present the details of the explicit example in Villa Azul (Quilmes) where this tool resulted crucial to control an outbreak on time. The presented tools have been designed in urgency with the available data at the time of the development, and therefore have their limitations which we describe and discuss. We consider possible improvements on the tools, many of which are currently under development.
Fil: Alvarez, Ezequiel. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Ciencias Fisicas. - Universidad Nacional de San Martin. Instituto de Ciencias Fisicas.; Argentina
Fil: Obando, Daniela. Provincia de Buenos Aires. Ministerio de Salud; Argentina
Fil: Crespo, Sebastian. Provincia de Buenos Aires. Ministerio de Salud; Argentina
Fil: Garcia, Enio. Provincia de Buenos Aires. Ministerio de Salud; Argentina
Fil: Kreplak, Nicolas. Provincia de Buenos Aires. Ministerio de Salud; Argentina
Fil: Marsico, Franco. Provincia de Buenos Aires. Ministerio de Salud; Argentina
Materia
CORRELATION
COVID-19
EARLY-ALARM, LIVE-TRACKING
PHONE CALLS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/133854
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Estimating COVID-19 cases and outbreaks on-stream through phone callsAlvarez, EzequielObando, DanielaCrespo, SebastianGarcia, EnioKreplak, NicolasMarsico, FrancoCORRELATIONCOVID-19EARLY-ALARM, LIVE-TRACKINGPHONE CALLShttps://purl.org/becyt/ford/3.3https://purl.org/becyt/ford/3One of the main problems in controlling COVID-19 epidemic spread is the delay in confirming cases. Having information on changes in the epidemic evolution or outbreaks rise before laboratory-confirmation is crucial in decision making for Public Health policies. We present an algorithm to estimate on-stream the number of COVID-19 cases using the data from telephone calls to a COVID-line. By modelling the calls as background (proportional to population) plus signal (proportional to infected), we fit the calls in Province of Buenos Aires (Argentina) with coefficient of determination R 2 > 0.85. This result allows us to estimate the number of cases given the number of calls from a specific district, days before the laboratory results are available. We validate the algorithm with real data. We show how to use the algorithm to track on-stream the epidemic, and present the Early Outbreak Alarm to detect outbreaks in advance of laboratory results. One key point in the developed algorithm is a detailed track of the uncertainties in the estimations, since the alarm uses the significance of the observables as a main indicator to detect an anomaly. We present the details of the explicit example in Villa Azul (Quilmes) where this tool resulted crucial to control an outbreak on time. The presented tools have been designed in urgency with the available data at the time of the development, and therefore have their limitations which we describe and discuss. We consider possible improvements on the tools, many of which are currently under development.Fil: Alvarez, Ezequiel. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Ciencias Fisicas. - Universidad Nacional de San Martin. Instituto de Ciencias Fisicas.; ArgentinaFil: Obando, Daniela. Provincia de Buenos Aires. Ministerio de Salud; ArgentinaFil: Crespo, Sebastian. Provincia de Buenos Aires. Ministerio de Salud; ArgentinaFil: Garcia, Enio. Provincia de Buenos Aires. Ministerio de Salud; ArgentinaFil: Kreplak, Nicolas. Provincia de Buenos Aires. Ministerio de Salud; ArgentinaFil: Marsico, Franco. Provincia de Buenos Aires. Ministerio de Salud; ArgentinaThe Royal Society2021-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/133854Alvarez, Ezequiel; Obando, Daniela; Crespo, Sebastian; Garcia, Enio; Kreplak, Nicolas; et al.; Estimating COVID-19 cases and outbreaks on-stream through phone calls; The Royal Society; Royal Society Open Science; 8; 3; 3-2021; 1-112054-5703CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://royalsocietypublishing.org/doi/10.1098/rsos.202312info:eu-repo/semantics/altIdentifier/doi/10.1098/rsos.202312info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:05:01Zoai:ri.conicet.gov.ar:11336/133854instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-10 13:05:01.822CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Estimating COVID-19 cases and outbreaks on-stream through phone calls
title Estimating COVID-19 cases and outbreaks on-stream through phone calls
spellingShingle Estimating COVID-19 cases and outbreaks on-stream through phone calls
Alvarez, Ezequiel
CORRELATION
COVID-19
EARLY-ALARM, LIVE-TRACKING
PHONE CALLS
title_short Estimating COVID-19 cases and outbreaks on-stream through phone calls
title_full Estimating COVID-19 cases and outbreaks on-stream through phone calls
title_fullStr Estimating COVID-19 cases and outbreaks on-stream through phone calls
title_full_unstemmed Estimating COVID-19 cases and outbreaks on-stream through phone calls
title_sort Estimating COVID-19 cases and outbreaks on-stream through phone calls
dc.creator.none.fl_str_mv Alvarez, Ezequiel
Obando, Daniela
Crespo, Sebastian
Garcia, Enio
Kreplak, Nicolas
Marsico, Franco
author Alvarez, Ezequiel
author_facet Alvarez, Ezequiel
Obando, Daniela
Crespo, Sebastian
Garcia, Enio
Kreplak, Nicolas
Marsico, Franco
author_role author
author2 Obando, Daniela
Crespo, Sebastian
Garcia, Enio
Kreplak, Nicolas
Marsico, Franco
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv CORRELATION
COVID-19
EARLY-ALARM, LIVE-TRACKING
PHONE CALLS
topic CORRELATION
COVID-19
EARLY-ALARM, LIVE-TRACKING
PHONE CALLS
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.3
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv One of the main problems in controlling COVID-19 epidemic spread is the delay in confirming cases. Having information on changes in the epidemic evolution or outbreaks rise before laboratory-confirmation is crucial in decision making for Public Health policies. We present an algorithm to estimate on-stream the number of COVID-19 cases using the data from telephone calls to a COVID-line. By modelling the calls as background (proportional to population) plus signal (proportional to infected), we fit the calls in Province of Buenos Aires (Argentina) with coefficient of determination R 2 > 0.85. This result allows us to estimate the number of cases given the number of calls from a specific district, days before the laboratory results are available. We validate the algorithm with real data. We show how to use the algorithm to track on-stream the epidemic, and present the Early Outbreak Alarm to detect outbreaks in advance of laboratory results. One key point in the developed algorithm is a detailed track of the uncertainties in the estimations, since the alarm uses the significance of the observables as a main indicator to detect an anomaly. We present the details of the explicit example in Villa Azul (Quilmes) where this tool resulted crucial to control an outbreak on time. The presented tools have been designed in urgency with the available data at the time of the development, and therefore have their limitations which we describe and discuss. We consider possible improvements on the tools, many of which are currently under development.
Fil: Alvarez, Ezequiel. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Ciencias Fisicas. - Universidad Nacional de San Martin. Instituto de Ciencias Fisicas.; Argentina
Fil: Obando, Daniela. Provincia de Buenos Aires. Ministerio de Salud; Argentina
Fil: Crespo, Sebastian. Provincia de Buenos Aires. Ministerio de Salud; Argentina
Fil: Garcia, Enio. Provincia de Buenos Aires. Ministerio de Salud; Argentina
Fil: Kreplak, Nicolas. Provincia de Buenos Aires. Ministerio de Salud; Argentina
Fil: Marsico, Franco. Provincia de Buenos Aires. Ministerio de Salud; Argentina
description One of the main problems in controlling COVID-19 epidemic spread is the delay in confirming cases. Having information on changes in the epidemic evolution or outbreaks rise before laboratory-confirmation is crucial in decision making for Public Health policies. We present an algorithm to estimate on-stream the number of COVID-19 cases using the data from telephone calls to a COVID-line. By modelling the calls as background (proportional to population) plus signal (proportional to infected), we fit the calls in Province of Buenos Aires (Argentina) with coefficient of determination R 2 > 0.85. This result allows us to estimate the number of cases given the number of calls from a specific district, days before the laboratory results are available. We validate the algorithm with real data. We show how to use the algorithm to track on-stream the epidemic, and present the Early Outbreak Alarm to detect outbreaks in advance of laboratory results. One key point in the developed algorithm is a detailed track of the uncertainties in the estimations, since the alarm uses the significance of the observables as a main indicator to detect an anomaly. We present the details of the explicit example in Villa Azul (Quilmes) where this tool resulted crucial to control an outbreak on time. The presented tools have been designed in urgency with the available data at the time of the development, and therefore have their limitations which we describe and discuss. We consider possible improvements on the tools, many of which are currently under development.
publishDate 2021
dc.date.none.fl_str_mv 2021-03
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/133854
Alvarez, Ezequiel; Obando, Daniela; Crespo, Sebastian; Garcia, Enio; Kreplak, Nicolas; et al.; Estimating COVID-19 cases and outbreaks on-stream through phone calls; The Royal Society; Royal Society Open Science; 8; 3; 3-2021; 1-11
2054-5703
CONICET Digital
CONICET
url http://hdl.handle.net/11336/133854
identifier_str_mv Alvarez, Ezequiel; Obando, Daniela; Crespo, Sebastian; Garcia, Enio; Kreplak, Nicolas; et al.; Estimating COVID-19 cases and outbreaks on-stream through phone calls; The Royal Society; Royal Society Open Science; 8; 3; 3-2021; 1-11
2054-5703
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://royalsocietypublishing.org/doi/10.1098/rsos.202312
info:eu-repo/semantics/altIdentifier/doi/10.1098/rsos.202312
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv The Royal Society
publisher.none.fl_str_mv The Royal Society
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.993085

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