title: Using prediction markets of market scoring rule to forecast infectious diseases: a case study in Taiwan.
abstract: BACKGROUND
  The Taiwan CDC relied on the historical average number of disease cases or rate (AVG) to depict the trend of epidemic diseases in Taiwan. By comparing the historical average data with prediction markets, we show that the latter have a better prediction capability than the former. Given the volatility of the infectious diseases in Taiwan, historical average is unlikely to be an effective prediction mechanism.
  
  
  METHODS
  We designed and built the Epidemic Prediction Markets (EPM) system based upon the trading mechanism of market scoring rule. By using this system, we aggregated dispersed information from various medical professionals to predict influenza, enterovirus, and dengue fever in Taiwan.
  
  
  RESULTS
  EPM was more accurate in 701 out of 1,085 prediction events than the traditional baseline of historical average and the winning ratio of EPM versus AVG was 64.6 % for the target week. For the absolute prediction error of five diseases indicators of three infectious diseases, EPM was more accurate for the target week than AVG except for dengue fever confirmed cases. The winning ratios of EPM versus AVG for the confirmed cases of severe complicated influenza case, the rate of enterovirus infection, and the rate of influenza-like illness in the target week were 69.6 %, 83.9 and 76.0 %, respectively; instead, for the prediction of the confirmed cases of dengue fever and the confirmed cases of severe complicated enterovirus infection, the winning ratios of EPM were all below 50 %.
  
  
  CONCLUSIONS
  Except confirmed cases of dengue fever, EPM provided accurate, continuous and real-time predictions of four indicators of three infectious diseases for the target week in Taiwan and outperformed the historical average data of infectious diseases.
authors.affiliation: Graduate Institute of Development Studies, National Chengchi University, 64, Zhi-Nan Road, Sec. 2, Wenshan, Taipei, 11605, Taiwan. ctung@nccu.edu.tw.
authors.forename: Chen-yuan
authors.initials: CY
authors.lastname: Tung
authors.affiliation: Center for Prediction Markets, National Chengchi University, Taipei, Taiwan. tzuchuan@gmail.com.
authors.forename: Tzu-chuan
authors.initials: TC
authors.lastname: Chou
authors.affiliation: Institute of Political Science, Academia Sinica, Department of Political Science, National Chengchi University, Taipei, Taiwan. ljw@sinica.edu.tw.
authors.forename: Jih-wen
authors.initials: JW
authors.lastname: Lin
mesh_terms: D003140:Communicable Disease Control / Q000458:organization & administration / Q000639:trends*; D003141:Communicable Diseases / Q000175:diagnosis* / Q000453:epidemiology*; D003715:Dengue / Q000453:epidemiology; D004769:Enterovirus Infections / Q000453:epidemiology; D058872:Epidemics / Q000517:prevention & control / Q000706:statistics & numerical data*; D005544:Forecasting; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology; D015233:Models, Statistical; D019982:Organizational Case Studies; D013624:Taiwan / Q000453:epidemiology