Forecasting virus outbreaks with social media data via neural ordinary differential equations

Autores
Nuñez, Matias; Barreiro, Nadia Luisina; Barrio, Rafael Ángel; Rackauckas, Christopher
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
During the Covid-19 pandemic, real-time social media data could in principle be used as an early predictor of a new epidemic wave. This possibility is examined here by employing a neural ordinary differential equation (neural ODE) trained to forecast viral outbreaks in a specific geographic region. It learns from multivariate time series of signals derived from a novel set of large online polls regarding COVID-19 symptoms. Once trained, the neural ODE can capture the dynamics of interconnected local signals and effectively estimate the number of new infections up to two months in advance. In addition, it may predict the future consequences of changes in the number of infected at a certain period, which might be related with the flow of individuals entering or exiting a region. This study provides persuasive evidence for the predictive ability of widely disseminated social media surveys for public health applications.
Fil: Nuñez, Matias. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina
Fil: Barreiro, Nadia Luisina. Ministerio de Defensa. Instituto de Investigaciones Científicas y Técnicas para la Defensa; Argentina
Fil: Barrio, Rafael Ángel. Universidad Nacional Autónoma de México; México
Fil: Rackauckas, Christopher. No especifíca;
Materia
machine learning
neural ode
deep learning
covid 19
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/240737
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Forecasting virus outbreaks with social media data via neural ordinary differential equationsNuñez, MatiasBarreiro, Nadia LuisinaBarrio, Rafael ÁngelRackauckas, Christophermachine learningneural odedeep learningcovid 19https://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1During the Covid-19 pandemic, real-time social media data could in principle be used as an early predictor of a new epidemic wave. This possibility is examined here by employing a neural ordinary differential equation (neural ODE) trained to forecast viral outbreaks in a specific geographic region. It learns from multivariate time series of signals derived from a novel set of large online polls regarding COVID-19 symptoms. Once trained, the neural ODE can capture the dynamics of interconnected local signals and effectively estimate the number of new infections up to two months in advance. In addition, it may predict the future consequences of changes in the number of infected at a certain period, which might be related with the flow of individuals entering or exiting a region. This study provides persuasive evidence for the predictive ability of widely disseminated social media surveys for public health applications.Fil: Nuñez, Matias. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; ArgentinaFil: Barreiro, Nadia Luisina. Ministerio de Defensa. Instituto de Investigaciones Científicas y Técnicas para la Defensa; ArgentinaFil: Barrio, Rafael Ángel. Universidad Nacional Autónoma de México; MéxicoFil: Rackauckas, Christopher. No especifíca;Nature2023-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/240737Nuñez, Matias; Barreiro, Nadia Luisina; Barrio, Rafael Ángel; Rackauckas, Christopher; Forecasting virus outbreaks with social media data via neural ordinary differential equations; Nature; Scientific Reports; 13; 1; 12-2023; 1-112045-2322CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1038/s41598-023-37118-9info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:32:52Zoai:ri.conicet.gov.ar:11336/240737instacron: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:34982025-09-29 09:32:52.95CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Forecasting virus outbreaks with social media data via neural ordinary differential equations
title Forecasting virus outbreaks with social media data via neural ordinary differential equations
spellingShingle Forecasting virus outbreaks with social media data via neural ordinary differential equations
Nuñez, Matias
machine learning
neural ode
deep learning
covid 19
title_short Forecasting virus outbreaks with social media data via neural ordinary differential equations
title_full Forecasting virus outbreaks with social media data via neural ordinary differential equations
title_fullStr Forecasting virus outbreaks with social media data via neural ordinary differential equations
title_full_unstemmed Forecasting virus outbreaks with social media data via neural ordinary differential equations
title_sort Forecasting virus outbreaks with social media data via neural ordinary differential equations
dc.creator.none.fl_str_mv Nuñez, Matias
Barreiro, Nadia Luisina
Barrio, Rafael Ángel
Rackauckas, Christopher
author Nuñez, Matias
author_facet Nuñez, Matias
Barreiro, Nadia Luisina
Barrio, Rafael Ángel
Rackauckas, Christopher
author_role author
author2 Barreiro, Nadia Luisina
Barrio, Rafael Ángel
Rackauckas, Christopher
author2_role author
author
author
dc.subject.none.fl_str_mv machine learning
neural ode
deep learning
covid 19
topic machine learning
neural ode
deep learning
covid 19
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv During the Covid-19 pandemic, real-time social media data could in principle be used as an early predictor of a new epidemic wave. This possibility is examined here by employing a neural ordinary differential equation (neural ODE) trained to forecast viral outbreaks in a specific geographic region. It learns from multivariate time series of signals derived from a novel set of large online polls regarding COVID-19 symptoms. Once trained, the neural ODE can capture the dynamics of interconnected local signals and effectively estimate the number of new infections up to two months in advance. In addition, it may predict the future consequences of changes in the number of infected at a certain period, which might be related with the flow of individuals entering or exiting a region. This study provides persuasive evidence for the predictive ability of widely disseminated social media surveys for public health applications.
Fil: Nuñez, Matias. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina
Fil: Barreiro, Nadia Luisina. Ministerio de Defensa. Instituto de Investigaciones Científicas y Técnicas para la Defensa; Argentina
Fil: Barrio, Rafael Ángel. Universidad Nacional Autónoma de México; México
Fil: Rackauckas, Christopher. No especifíca;
description During the Covid-19 pandemic, real-time social media data could in principle be used as an early predictor of a new epidemic wave. This possibility is examined here by employing a neural ordinary differential equation (neural ODE) trained to forecast viral outbreaks in a specific geographic region. It learns from multivariate time series of signals derived from a novel set of large online polls regarding COVID-19 symptoms. Once trained, the neural ODE can capture the dynamics of interconnected local signals and effectively estimate the number of new infections up to two months in advance. In addition, it may predict the future consequences of changes in the number of infected at a certain period, which might be related with the flow of individuals entering or exiting a region. This study provides persuasive evidence for the predictive ability of widely disseminated social media surveys for public health applications.
publishDate 2023
dc.date.none.fl_str_mv 2023-12
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/11336/240737
Nuñez, Matias; Barreiro, Nadia Luisina; Barrio, Rafael Ángel; Rackauckas, Christopher; Forecasting virus outbreaks with social media data via neural ordinary differential equations; Nature; Scientific Reports; 13; 1; 12-2023; 1-11
2045-2322
CONICET Digital
CONICET
url http://hdl.handle.net/11336/240737
identifier_str_mv Nuñez, Matias; Barreiro, Nadia Luisina; Barrio, Rafael Ángel; Rackauckas, Christopher; Forecasting virus outbreaks with social media data via neural ordinary differential equations; Nature; Scientific Reports; 13; 1; 12-2023; 1-11
2045-2322
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1038/s41598-023-37118-9
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Nature
publisher.none.fl_str_mv Nature
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 13.070432

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