The wisdom of crowds in action: Forecasting epidemic diseases with a web-based prediction market system.
BACKGROUND
The quest for an effective system capable of monitoring and predicting the trends of epidemic diseases is a critical issue for communities worldwide. With the prevalence of Internet access, more and more researchers today are using data from both search engines and social media to improve the prediction accuracy. In particular, a prediction market system (PMS) exploits the wisdom of crowds on the Internet to effectively accomplish relatively high accuracy.


OBJECTIVE
This study presents the architecture of a PMS and demonstrates the matching mechanism of logarithmic market scoring rules. The system was implemented to predict infectious diseases in Taiwan with the wisdom of crowds in order to improve the accuracy of epidemic forecasting.


METHODS
The PMS architecture contains three design components: database clusters, market engine, and Web applications. The system accumulated knowledge from 126 health professionals for 31 weeks to predict five disease indicators: the confirmed cases of dengue fever, the confirmed cases of severe and complicated influenza, the rate of enterovirus infections, the rate of influenza-like illnesses, and the confirmed cases of severe and complicated enterovirus infection.


RESULTS
Based on the winning ratio, the PMS predicts the trends of three out of five disease indicators more accurately than does the existing system that uses the five-year average values of historical data for the same weeks. In addition, the PMS with the matching mechanism of logarithmic market scoring rules is easy to understand for health professionals and applicable to predict all the five disease indicators.


CONCLUSIONS
The PMS architecture of this study affords organizations and individuals to implement it for various purposes in our society. The system can continuously update the data and improve prediction accuracy in monitoring and forecasting the trends of epidemic diseases. Future researchers could replicate and apply the PMS demonstrated in this study to more infectious diseases and wider geographical areas, especially the under-developed countries across Asia and Africa.
Department of Management Information Systems, National Chengchi University, Taipei City 11605, Taiwan, ROC. Electronic address: eli@nccu.edu.tw.
Eldon Y
EY
Li
Graduate Institute of Development Studies, National Chengchi University, Taipei City 11605, Taiwan, ROC. Electronic address: ctung@nccu.edu.tw.
Chen-Yuan
CY
Tung
Department of Management Information Systems, National Chengchi University, Taipei City 11605, Taiwan, ROC. Electronic address: tommy1426@gmail.com.
Shu-Hsun
SH
Chang
D000818:Animals; D058872:Epidemics / Q000706:statistics & numerical data*; D015991:Epidemiologic Measurements; D004812:Epidemiologic Methods; D005544:Forecasting; D006801:Humans; D007239:Infections / Q000453:epidemiology*; D020407:Internet; D015233:Models, Statistical; D061108:Social Media; D013624:Taiwan / Q000453:epidemiology
Epidemic prediction; Infectious diseases; Logarithmic market scoring rules; Prediction market system; Real-time update; Web-based system; Wisdom of crowds