Prediction Market for Disease Surveillance: A Case Study of Influenza Activity

Article Sidebar

Requires Subscription or Fee PDF (GBP 25)
Published: Sep 20, 2016
DOI: https://doi.org/10.5750/jpm.v10i1.1162
Keywords:
Prediction markets Experimental economics Disease forecasting

Main Article Content

Anson T Y Ho
Kansas State University
Philip M Polgreen
University of Iowa
Tim Prendergast

Abstract

We conducted a pilot study on the use of prediction markets to aggregate private information for disease surveillance. Influenza activity in Iowa, North Carolina, and Nebraska between 2008-2010 was forecast through prediction markets operated on the Iowa Electronic health Markets (IEhM). We found that prediction markets were well utilized by participants, and they achieved high level of forecasting accuracy as far as 4 weeks before actual influenza-level outcomes were announced. Trading activities indicate that new information continuously flowed into the markets during the trading window, which further improved prediction accuracy as contracts drew down to expiry. This project demonstrates that a prediction market is a practical infectious disease surveillance mechanism that provides low-cost useful information for public health administration in a timely manner.

Article Details

Issue
Vol. 10 No. 1 (2016)
Section
Articles
Author Biographies

Anson T Y Ho, Kansas State University

Assistant Professor, Department of Economics

Philip M Polgreen, University of Iowa

Associate Professor, Department of Internal Medicine

References

Ran D Balicer, Michael Huerta, Nadav Davidovitch, and Itamar Grotto. Cost-benefit of stockpiling drugs for influenza pandemic. Emerging infectious diseases, 11(8):1280, 2005.

Joyce E Berg, Forrest D Nelson, and Thomas A Rietz. Prediction market accuracy in the long run.InternationalJournal of Forecasting, 24(2):285–300, 2008.

Jacob Cohen. A coefficient of agreement for nominal scales. Educational and Psychological Measure- ment, XX(1), 1960.

NJ Cox and K Subbarao. Global epidemiology of influenza: past and present. Annual review of medicine, 51(1):407–421, 2000.

Joseph L Fleiss, Bruce Levin, and Myunghee Cho Paik. Statistical methods for rates and proportions.John Wiley & Sons, 2013.

Centers for Disease Control and Prevention. Key facts about influenza (flu) & flu vaccine. Technical report, Centers for Disease Control and Prevention, 2014.

Crystal Franco, Tara Kirk Sell, Anson Tai Yat Ho, and Phillip Polgreen. Electronic health markets predict spread of dengue in us. Clinician’s Biosecurity News. UPMC Center for Health Security, November, 5, 2010.

Jeremy Ginsberg, Matthew H. Mohebbi, Rajan S. Patel, Lynnette Brammer, Mark S. Smolinski, and Larry Brilliant. Detecting influenza epidemics using search engine query data. Nature, 457:1012–1014, 2009.

Sharon K Greene, Edward L Ionides, and Mark L Wilson. Patterns of influenza-associated mortality among us elderly by geographic region and virus subtype, 1968–1998. American Journal of Epidemiol- ogy, 163(4):316–326, 2006.

Kilem L Gwet. Handbook of Inter-Rater Reliability: The Definitive Guide to Measuring the Extent of Agreement Among Multiple Raters, 3rd Edition. Advanced Analytics, LLC, 2012.

John L Hick, Dan Hanfling, Jonathan L Burstein, Craig DeAtley, Donna Barbisch, Gregory M Bogdan, and Stephen Cantrill. Health care facility and community strategies for patient care surge capacity. Annals of Emergency Medicine, 44(3):253–261, 2004.

Anson Tai Yat Ho, FD Nelson, GR Neumann, and PM Polgreen. A prediction market for h5n1 influenza compared with statistical forecasting model. In International Society for Disease Surveillance Conference 2010 Track 1: Analytics/Research Methodologies, volume 1, page 29, 2011.

David S. Hui, Nelson Lee, and Paul K. S. Chan. Clinical management of pandemic 2009 influenza a(h1n1) infection. Chest, 137(4):916–925, 2010.

J Richard Landis and Gary G Koch. The measurement of observer agreement for categorical data. Bio- metrics, pages 159–174, 1977.

Charles F Manski. Interpreting the predictions of prediction markets. Economics Letters, 91(3):425–429, 2006.

A. Marshall McBean, J. Daniel Babish, and Joan L. Warren. The impact and cost of influenza in the elderly. Archives of Internal Medicine, 153(18):2105–2111, 1993.

M. I. Meltzer, N. J. Cox, and K. Fukuda. The economic impact of pandemic influenza in the united states: Priorities for intervention. Emerging Infectious Diseases, 5(5):659–671, 1999.

Noelle-Angelique M. Molinari, Ismael R. Ortega-Sanchez, Mark L. Messonnier, William W. Thompson, Pascale M. Wortley, Eric Weintraub, and Carolyn B. Bridges. The annual impact of seasonal influenza in the us: Measuring disease burden and costs. Vaccine, 25(27):5086 – 5096, 2007.

Lionel Page and Robert T. Clemen. Do prediction markets produce well-calibrated probability fore- casts?*. The Economic Journal, 123(568):491–513, 2013.

Lionel Page and Christoph Siemroth. An experimental analysis of information acquisition in prediction markets. Games and Economic Behavior, forthcoming.

J Paton, F Nelson, AM Segre, and PM Polgreen. Samos: A community-driven open-access prediction market system. In 9th Annual Conference of the International Society for Disease Surveillance, 2010.

Philip M Polgreen, Yiling Chen, David M Pennock, Forrest D Nelson, and Robert A Weinstein. Using internet searches for influenza surveillance. Clinical infectious diseases, 47(11):1443–1448, 2008.

Philip M. Polgreen and Forrest D. Nelson. Biosurveillance: Methods and Case Studies, chapter Using Prediction Markets to Forecast Infectious Diseases, pages 145–160. CRC Press, 2010.

Philip M. Polgreen, Forrest D. Nelson, and George R. Neumann. Use of prediction markets to forecast infectious disease activity. Clinical Infectious Diseases, 44:272–279, 2006.

Michael A Riddiough, Jane E Sisk, and John C Bell. Influenza vaccination: cost-effectiveness and public policy. The Journal of the American Medical Association, 249(23):3189–3195, 1983.

Stephen C. Schoenbaum. Economic impact of influenza: The individual’s perspective. The American Journal of Medicine, 82(6, Supplement 1):26 – 30, 1987. Prevention, Management, and Control of Influenza A Mandate for the 1980s.

Jeffrey Shaman, Alicia Karspeck, Wan Yang, James Tamerius, and Marc Lipsitch. Real-time influenza forecasts during the 2012–2013 season. Nature communications, 4, 2013.

Alessio Signorini, Alberto Maria Segre, and Philip M. Polgreen. The use of twitter to track levels of dis- ease activity and public concern in the u.s. during the influenza a h1n1 pandemic. PLoS ONE, 6(5):e19467, 05 2011.

Donna F Stroup, Stephen B Thacker, and Joy L Herndon. Application of multiple time series analy- sis to the estimation of pneumonia and influenza mortality by age 1962–1983. Statistics in medicine, 7(10):1045–1059, 1988.

William W. Thompson, David K. Shay, Eric Weintraub, Lynnette Brammer, Carolyn B. Bridges, Nancy J. Cox, and Keiji Fukuda. Influenza-associated hospitalizations in the united states. The Journal of the American Medical Association, 292(11):1333–1340, 2004.

Cécile Viboud, Pierre-Yves Boëlle, Khashayar Pakdaman, Fabrice Carrat, Alain-Jacques Valleron, and Antoine Flahault. Influenza epidemics in the united states, france, and australia, 1972-1997. Emerging infectious diseases, 10(1):32–39, 2004.

Justin Wolfers and Eric Zitzewitz. Interpreting prediction market prices as probabilities. Information Markets: A New Way of Making Decisions in the Public and Private Sectors, 2006.

World Health Organization. World now at the start of 2009 influenza pandemic, Geneva, Switzerland, 2009.