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

Autores
Núñez, Matías; Barreiro, Nadia L.; Barrio, Rafael A.; Rackauckas, Christopher
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión aceptada
Descripción
In the midst of the covid-19 pandemic, social media data collected in real time has the potential of being an early indicator of a new epidemic wave. This possibility is explored here by using a neural ordinary differential equation (neural ODE) that is trained to predict virus outbreaks for a geographic region. It learns from multivariate time series of signals obtained from a novel set of massive online surveys about COVID-19 symptoms. Once trained, the neural ODE is able to capture the dynamics of the interlinked local signals and accurately predict the number of new infections up to two months in advance. Moreover, it can estimate the future effects of changes in the number of infected at a given time, which can be associated with the flow of people entering or leaving a given region or, for instance, with a local vaccination campaign. This work gives compelling preliminary evidence for the predictive power of widely distributed social media surveys for public health application
Fil: Núñez, Matías. Universidad Nacional del Comahue. INIBIOMA. CNEA. CONICET; Argentina.
Fuente
MedRxiv the preprint server for health sciences
Materia
Redes sociales
Datos recopilados
Ecuación diferencial ordinaria neural
Brotes de virus
COVID-19
Ciencias Biomédicas
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
Repositorio Digital Institucional (UNCo)
Institución
Universidad Nacional del Comahue
OAI Identificador
oai:rdi.uncoma.edu.ar:uncomaid/16169
Acceder
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oai_identifier_str oai:rdi.uncoma.edu.ar:uncomaid/16169
network_acronym_str RDIUNCO
repository_id_str 7108
network_name_str Repositorio Digital Institucional (UNCo)
spelling Forecasting virus outbreaks with social media data via neural ordinary differential equationsNúñez, MatíasBarreiro, Nadia L.Barrio, Rafael A.Rackauckas, ChristopherRedes socialesDatos recopiladosEcuación diferencial ordinaria neuralBrotes de virusCOVID-19Ciencias BiomédicasIn the midst of the covid-19 pandemic, social media data collected in real time has the potential of being an early indicator of a new epidemic wave. This possibility is explored here by using a neural ordinary differential equation (neural ODE) that is trained to predict virus outbreaks for a geographic region. It learns from multivariate time series of signals obtained from a novel set of massive online surveys about COVID-19 symptoms. Once trained, the neural ODE is able to capture the dynamics of the interlinked local signals and accurately predict the number of new infections up to two months in advance. Moreover, it can estimate the future effects of changes in the number of infected at a given time, which can be associated with the flow of people entering or leaving a given region or, for instance, with a local vaccination campaign. This work gives compelling preliminary evidence for the predictive power of widely distributed social media surveys for public health applicationFil: Núñez, Matías. Universidad Nacional del Comahue. INIBIOMA. CNEA. CONICET; Argentina.medRxivCold Spring Harbor LaboratoryUniversidad de YaleBMJ2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://rdi.uncoma.edu.ar/handle/uncomaid/16169MedRxiv the preprint server for health sciencesreponame:Repositorio Digital Institucional (UNCo)instname:Universidad Nacional del Comahueenghttps://www.medrxiv.org/content/10.1101/2021.01.27.21250642v1info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/2025-09-29T14:29:05Zoai:rdi.uncoma.edu.ar:uncomaid/16169instacron:UNCoInstitucionalhttp://rdi.uncoma.edu.ar/Universidad públicaNo correspondehttp://rdi.uncoma.edu.ar/oaimirtha.mateo@biblioteca.uncoma.edu.ar; adriana.acuna@biblioteca.uncoma.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:71082025-09-29 14:29:05.787Repositorio Digital Institucional (UNCo) - Universidad Nacional del Comahuefalse
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
Núñez, Matías
Redes sociales
Datos recopilados
Ecuación diferencial ordinaria neural
Brotes de virus
COVID-19
Ciencias Biomédicas
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 Núñez, Matías
Barreiro, Nadia L.
Barrio, Rafael A.
Rackauckas, Christopher
author Núñez, Matías
author_facet Núñez, Matías
Barreiro, Nadia L.
Barrio, Rafael A.
Rackauckas, Christopher
author_role author
author2 Barreiro, Nadia L.
Barrio, Rafael A.
Rackauckas, Christopher
author2_role author
author
author
dc.subject.none.fl_str_mv Redes sociales
Datos recopilados
Ecuación diferencial ordinaria neural
Brotes de virus
COVID-19
Ciencias Biomédicas
topic Redes sociales
Datos recopilados
Ecuación diferencial ordinaria neural
Brotes de virus
COVID-19
Ciencias Biomédicas
dc.description.none.fl_txt_mv In the midst of the covid-19 pandemic, social media data collected in real time has the potential of being an early indicator of a new epidemic wave. This possibility is explored here by using a neural ordinary differential equation (neural ODE) that is trained to predict virus outbreaks for a geographic region. It learns from multivariate time series of signals obtained from a novel set of massive online surveys about COVID-19 symptoms. Once trained, the neural ODE is able to capture the dynamics of the interlinked local signals and accurately predict the number of new infections up to two months in advance. Moreover, it can estimate the future effects of changes in the number of infected at a given time, which can be associated with the flow of people entering or leaving a given region or, for instance, with a local vaccination campaign. This work gives compelling preliminary evidence for the predictive power of widely distributed social media surveys for public health application
Fil: Núñez, Matías. Universidad Nacional del Comahue. INIBIOMA. CNEA. CONICET; Argentina.
description In the midst of the covid-19 pandemic, social media data collected in real time has the potential of being an early indicator of a new epidemic wave. This possibility is explored here by using a neural ordinary differential equation (neural ODE) that is trained to predict virus outbreaks for a geographic region. It learns from multivariate time series of signals obtained from a novel set of massive online surveys about COVID-19 symptoms. Once trained, the neural ODE is able to capture the dynamics of the interlinked local signals and accurately predict the number of new infections up to two months in advance. Moreover, it can estimate the future effects of changes in the number of infected at a given time, which can be associated with the flow of people entering or leaving a given region or, for instance, with a local vaccination campaign. This work gives compelling preliminary evidence for the predictive power of widely distributed social media surveys for public health application
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://rdi.uncoma.edu.ar/handle/uncomaid/16169
url http://rdi.uncoma.edu.ar/handle/uncomaid/16169
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://www.medrxiv.org/content/10.1101/2021.01.27.21250642v1
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 medRxiv
Cold Spring Harbor Laboratory
Universidad de Yale
BMJ
publisher.none.fl_str_mv medRxiv
Cold Spring Harbor Laboratory
Universidad de Yale
BMJ
dc.source.none.fl_str_mv MedRxiv the preprint server for health sciences
reponame:Repositorio Digital Institucional (UNCo)
instname:Universidad Nacional del Comahue
reponame_str Repositorio Digital Institucional (UNCo)
collection Repositorio Digital Institucional (UNCo)
instname_str Universidad Nacional del Comahue
repository.name.fl_str_mv Repositorio Digital Institucional (UNCo) - Universidad Nacional del Comahue
repository.mail.fl_str_mv mirtha.mateo@biblioteca.uncoma.edu.ar; adriana.acuna@biblioteca.uncoma.edu.ar
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score 12.558318

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