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dc.contributor.author
Alvarez, Ezequiel
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dc.contributor.author
Obando, Daniela
dc.contributor.author
Crespo, Sebastian
dc.contributor.author
Garcia, Enio
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Kreplak, Nicolas
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Marsico, Franco
dc.date.available
2021-06-15T12:17:38Z
dc.date.issued
2021-03
dc.identifier.citation
Alvarez, Ezequiel; Obando, Daniela; Crespo, Sebastian; Garcia, Enio; Kreplak, Nicolas; et al.; Estimating COVID-19 cases and outbreaks on-stream through phone calls; Royal Society of Chemistry; Royal Society Open Science; 8; 3; 3-2021; 1-11
dc.identifier.issn
2054-5703
dc.identifier.uri
http://hdl.handle.net/11336/133854
dc.description.abstract
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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Royal Society of Chemistry
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
CORRELATION
dc.subject
COVID-19
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EARLY-ALARM, LIVE-TRACKING
dc.subject
PHONE CALLS
dc.subject.classification
Epidemiología
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dc.subject.classification
Ciencias de la Salud
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dc.subject.classification
CIENCIAS MÉDICAS Y DE LA SALUD
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dc.title
Estimating COVID-19 cases and outbreaks on-stream through phone calls
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2021-06-14T15:10:57Z
dc.journal.volume
8
dc.journal.number
3
dc.journal.pagination
1-11
dc.journal.pais
Reino Unido
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dc.description.fil
Fil: Alvarez, Ezequiel. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Ciencias Fisicas. - Universidad Nacional de San Martin. Instituto de Ciencias Fisicas.; Argentina
dc.description.fil
Fil: Obando, Daniela. Provincia de Buenos Aires. Ministerio de Salud; Argentina
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Fil: Crespo, Sebastian. Provincia de Buenos Aires. Ministerio de Salud; Argentina
dc.description.fil
Fil: Garcia, Enio. Provincia de Buenos Aires. Ministerio de Salud; Argentina
dc.description.fil
Fil: Kreplak, Nicolas. Provincia de Buenos Aires. Ministerio de Salud; Argentina
dc.description.fil
Fil: Marsico, Franco. Provincia de Buenos Aires. Ministerio de Salud; Argentina
dc.journal.title
Royal Society Open Science
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://royalsocietypublishing.org/doi/10.1098/rsos.202312
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1098/rsos.202312
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