Using prediction polling to harness collective intelligence for disease forecasting

Abstract Background The global spread of COVID-19 has shown that reliable forecasting of public health related outcomes is important but lacking. Methods We report the results of the first large-scale, long-term experiment in crowd-forecasting of infectious-disease outbreaks, where a total of 562 vo...

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Autores principales:	Tara Kirk Sell, Kelsey Lane Warmbrod, Crystal Watson, Marc Trotochaud, Elena Martin, Sanjana J. Ravi, Maurice Balick, Emile Servan-Schreiber
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Forecasting Epidemic prediction Crowd-sourced Infectious disease COVID-19 Ebola Public aspects of medicine RA1-1270
Acceso en línea:	https://doaj.org/article/06ef8ed3eaae479c8437604776dff12c
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id	oai:doaj.org-article:06ef8ed3eaae479c8437604776dff12c
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spelling	oai:doaj.org-article:06ef8ed3eaae479c8437604776dff12c2021-11-21T12:11:36ZUsing prediction polling to harness collective intelligence for disease forecasting10.1186/s12889-021-12083-y1471-2458https://doaj.org/article/06ef8ed3eaae479c8437604776dff12c2021-11-01T00:00:00Zhttps://doi.org/10.1186/s12889-021-12083-yhttps://doaj.org/toc/1471-2458Abstract Background The global spread of COVID-19 has shown that reliable forecasting of public health related outcomes is important but lacking. Methods We report the results of the first large-scale, long-term experiment in crowd-forecasting of infectious-disease outbreaks, where a total of 562 volunteer participants competed over 15 months to make forecasts on 61 questions with a total of 217 possible answers regarding 19 diseases. Results Consistent with the “wisdom of crowds” phenomenon, we found that crowd forecasts aggregated using best-practice adaptive algorithms are well-calibrated, accurate, timely, and outperform all individual forecasters. Conclusions Crowd forecasting efforts in public health may be a useful addition to traditional disease surveillance, modeling, and other approaches to evidence-based decision making for infectious disease outbreaks.Tara Kirk SellKelsey Lane WarmbrodCrystal WatsonMarc TrotochaudElena MartinSanjana J. RaviMaurice BalickEmile Servan-SchreiberBMCarticleForecastingEpidemic predictionCrowd-sourcedInfectious diseaseCOVID-19EbolaPublic aspects of medicineRA1-1270ENBMC Public Health, Vol 21, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Forecasting Epidemic prediction Crowd-sourced Infectious disease COVID-19 Ebola Public aspects of medicine RA1-1270
spellingShingle	Forecasting Epidemic prediction Crowd-sourced Infectious disease COVID-19 Ebola Public aspects of medicine RA1-1270 Tara Kirk Sell Kelsey Lane Warmbrod Crystal Watson Marc Trotochaud Elena Martin Sanjana J. Ravi Maurice Balick Emile Servan-Schreiber Using prediction polling to harness collective intelligence for disease forecasting
description	Abstract Background The global spread of COVID-19 has shown that reliable forecasting of public health related outcomes is important but lacking. Methods We report the results of the first large-scale, long-term experiment in crowd-forecasting of infectious-disease outbreaks, where a total of 562 volunteer participants competed over 15 months to make forecasts on 61 questions with a total of 217 possible answers regarding 19 diseases. Results Consistent with the “wisdom of crowds” phenomenon, we found that crowd forecasts aggregated using best-practice adaptive algorithms are well-calibrated, accurate, timely, and outperform all individual forecasters. Conclusions Crowd forecasting efforts in public health may be a useful addition to traditional disease surveillance, modeling, and other approaches to evidence-based decision making for infectious disease outbreaks.
format	article
author	Tara Kirk Sell Kelsey Lane Warmbrod Crystal Watson Marc Trotochaud Elena Martin Sanjana J. Ravi Maurice Balick Emile Servan-Schreiber
author_facet	Tara Kirk Sell Kelsey Lane Warmbrod Crystal Watson Marc Trotochaud Elena Martin Sanjana J. Ravi Maurice Balick Emile Servan-Schreiber
author_sort	Tara Kirk Sell
title	Using prediction polling to harness collective intelligence for disease forecasting
title_short	Using prediction polling to harness collective intelligence for disease forecasting
title_full	Using prediction polling to harness collective intelligence for disease forecasting
title_fullStr	Using prediction polling to harness collective intelligence for disease forecasting
title_full_unstemmed	Using prediction polling to harness collective intelligence for disease forecasting
title_sort	using prediction polling to harness collective intelligence for disease forecasting
publisher	BMC
publishDate	2021
url	https://doaj.org/article/06ef8ed3eaae479c8437604776dff12c
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Using prediction polling to harness collective intelligence for disease forecasting

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