Wisdom of crowds detects COVID-19 severity ahead of officially available data

Abstract During the unfolding of a crisis, it is crucial to forecast its severity at an early stage , yet access to reliable data is often challenging early on. The wisdom of crowds has been effective at forecasting in similar scenarios. We investigated whether the initial regional social media reac...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Jeremy Turiel, Delmiro Fernandez-Reyes, Tomaso Aste
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7b460f3ad1e04f0f86b92ef9d4c12202
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:7b460f3ad1e04f0f86b92ef9d4c12202
record_format	dspace
spelling	oai:doaj.org-article:7b460f3ad1e04f0f86b92ef9d4c122022021-12-02T18:18:43ZWisdom of crowds detects COVID-19 severity ahead of officially available data10.1038/s41598-021-93042-w2045-2322https://doaj.org/article/7b460f3ad1e04f0f86b92ef9d4c122022021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93042-whttps://doaj.org/toc/2045-2322Abstract During the unfolding of a crisis, it is crucial to forecast its severity at an early stage , yet access to reliable data is often challenging early on. The wisdom of crowds has been effective at forecasting in similar scenarios. We investigated whether the initial regional social media reaction to the emerging COVID-19 pandemic in three critically affected countries has significant relations with their observed mortality a month later. We obtained COVID-19 related regionally geolocated tweets from Italian, Spanish, and United States regions. We quantified the predictive power of the wisdom of the crowds using correlations and regressions of geolocated Tweet Intensity (TI) during the initial social media attention peak versus the cumulative number of deaths a month ahead. We found that the intensity of initial COVID-19 related tweet attention at the beginning of the pandemic across Italian, Spanish, and United States regions is significantly related (p < 0.001) to the extent to which these regions had been affected by the pandemic a month later. This association is most striking in Italy as when at its peak of TI in late February 2020 only two of its regions had reported mortality. The collective wisdom of the crowds at early stages of the pandemic, when information on the number of infections was not broadly available, strikingly predicted the extent of mortality reflecting the regional severity of the pandemic almost a month later. Our findings could underpin the creation of real-time novelty detection systems aimed at early reporting of the severity of crises impacting a territory leading to early activation of control measures at a stage when available data is extremely limited.Jeremy TurielDelmiro Fernandez-ReyesTomaso AsteNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jeremy Turiel Delmiro Fernandez-Reyes Tomaso Aste Wisdom of crowds detects COVID-19 severity ahead of officially available data
description	Abstract During the unfolding of a crisis, it is crucial to forecast its severity at an early stage , yet access to reliable data is often challenging early on. The wisdom of crowds has been effective at forecasting in similar scenarios. We investigated whether the initial regional social media reaction to the emerging COVID-19 pandemic in three critically affected countries has significant relations with their observed mortality a month later. We obtained COVID-19 related regionally geolocated tweets from Italian, Spanish, and United States regions. We quantified the predictive power of the wisdom of the crowds using correlations and regressions of geolocated Tweet Intensity (TI) during the initial social media attention peak versus the cumulative number of deaths a month ahead. We found that the intensity of initial COVID-19 related tweet attention at the beginning of the pandemic across Italian, Spanish, and United States regions is significantly related (p < 0.001) to the extent to which these regions had been affected by the pandemic a month later. This association is most striking in Italy as when at its peak of TI in late February 2020 only two of its regions had reported mortality. The collective wisdom of the crowds at early stages of the pandemic, when information on the number of infections was not broadly available, strikingly predicted the extent of mortality reflecting the regional severity of the pandemic almost a month later. Our findings could underpin the creation of real-time novelty detection systems aimed at early reporting of the severity of crises impacting a territory leading to early activation of control measures at a stage when available data is extremely limited.
format	article
author	Jeremy Turiel Delmiro Fernandez-Reyes Tomaso Aste
author_facet	Jeremy Turiel Delmiro Fernandez-Reyes Tomaso Aste
author_sort	Jeremy Turiel
title	Wisdom of crowds detects COVID-19 severity ahead of officially available data
title_short	Wisdom of crowds detects COVID-19 severity ahead of officially available data
title_full	Wisdom of crowds detects COVID-19 severity ahead of officially available data
title_fullStr	Wisdom of crowds detects COVID-19 severity ahead of officially available data
title_full_unstemmed	Wisdom of crowds detects COVID-19 severity ahead of officially available data
title_sort	wisdom of crowds detects covid-19 severity ahead of officially available data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7b460f3ad1e04f0f86b92ef9d4c12202
work_keys_str_mv	AT jeremyturiel wisdomofcrowdsdetectscovid19severityaheadofofficiallyavailabledata AT delmirofernandezreyes wisdomofcrowdsdetectscovid19severityaheadofofficiallyavailabledata AT tomasoaste wisdomofcrowdsdetectscovid19severityaheadofofficiallyavailabledata
_version_	1718378225574346752

Wisdom of crowds detects COVID-19 severity ahead of officially available data

Ejemplares similares