Literature DB >> 17173231

Use of prediction markets to forecast infectious disease activity.

Philip M Polgreen1, Forrest D Nelson, George R Neumann.   

Abstract

Prediction markets have accurately forecasted the outcomes of a wide range of future events, including sales of computer printers, elections, and the Federal Reserve's decisions about interest rates. We propose that prediction markets may be useful for tracking and forecasting emerging infectious diseases, such as severe acute respiratory syndrome and avian influenza, by aggregating expert opinion quickly, accurately, and inexpensively. Data from a pilot study in the state of Iowa suggest that these markets can accurately predict statewide seasonal influenza activity 2-4 weeks in advance by using clinical data volunteered from participating health care workers. Information revealed by prediction markets may help to inform treatment, prevention, and policy decisions. Also, these markets could help to refine existing surveillance systems.

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Year:  2006        PMID: 17173231     DOI: 10.1086/510427

Source DB:  PubMed          Journal:  Clin Infect Dis        ISSN: 1058-4838            Impact factor:   9.079


  21 in total

1.  Influenza mixes its pitches: Lessons learned to date from the influenza A (H1N1) pandemic.

Authors:  David N Fisman; Kevin B Laupland
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2.  Price dynamics in political prediction markets.

Authors:  Saikat Ray Majumder; Daniel Diermeier; Thomas A Rietz; Luís A Nunes Amaral
Journal:  Proc Natl Acad Sci U S A       Date:  2009-01-20       Impact factor: 11.205

3.  Social network sensors for early detection of contagious outbreaks.

Authors:  Nicholas A Christakis; James H Fowler
Journal:  PLoS One       Date:  2010-09-15       Impact factor: 3.240

Review 4.  Human social sensing is an untapped resource for computational social science.

Authors:  Mirta Galesic; Wändi Bruine de Bruin; Jonas Dalege; Scott L Feld; Frauke Kreuter; Henrik Olsson; Drazen Prelec; Daniel L Stein; Tamara van der Does
Journal:  Nature       Date:  2021-06-30       Impact factor: 49.962

5.  The use of Twitter to track levels of disease activity and public concern in the U.S. during the influenza A H1N1 pandemic.

Authors:  Alessio Signorini; Alberto Maria Segre; Philip M Polgreen
Journal:  PLoS One       Date:  2011-05-04       Impact factor: 3.240

6.  Flexible Modeling of Epidemics with an Empirical Bayes Framework.

Authors:  Logan C Brooks; David C Farrow; Sangwon Hyun; Ryan J Tibshirani; Roni Rosenfeld
Journal:  PLoS Comput Biol       Date:  2015-08-28       Impact factor: 4.475

Review 7.  Influenza forecasting in human populations: a scoping review.

Authors:  Jean-Paul Chretien; Dylan George; Jeffrey Shaman; Rohit A Chitale; F Ellis McKenzie
Journal:  PLoS One       Date:  2014-04-08       Impact factor: 3.240

8.  Using clinicians' search query data to monitor influenza epidemics.

Authors:  Mauricio Santillana; Elaine O Nsoesie; Sumiko R Mekaru; David Scales; John S Brownstein
Journal:  Clin Infect Dis       Date:  2014-08-12       Impact factor: 9.079

9.  Predictive validation of an influenza spread model.

Authors:  Ayaz Hyder; David L Buckeridge; Brian Leung
Journal:  PLoS One       Date:  2013-06-03       Impact factor: 3.240

10.  Using prediction markets of market scoring rule to forecast infectious diseases: a case study in Taiwan.

Authors:  Chen-yuan Tung; Tzu-chuan Chou; Jih-wen Lin
Journal:  BMC Public Health       Date:  2015-08-11       Impact factor: 3.295

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