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

Chen-yuan Tung,Jih-wen Lin,Tzu-Chuan Chou

doi:10.1186/s12889-015-2121-7

Chen-yuan Tung, Jih-wen Lin + Show 1 more

Open Access

https://doi.org/10.1186/s12889-015-2121-7

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Abstract

BackgroundThe 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.MethodsWe 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.ResultsEPM 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 %.ConclusionsExcept 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.

Highlights

The Taiwan Centers for Disease Control (CDC) relied on the historical average number of disease cases or rate (AVG) to depict the trend of epidemic diseases in Taiwan
A popular approach has been the stochastic models, in which the Bayesian models and simulations are some of the advanced methods. These methods are all based on data of infectious diseases collected in a specific time and space, and their reliability is determined by the sample size and their theoretical assumptions
Except the papers with the prediction markets system written by Polgreen, Nelson, Neumann [2, 3], most forecasting models are based upon the past data and not real-time forecasting models

Summary

Introduction

The Taiwan CDC relied on the historical average number of disease cases or rate (AVG) to depict the trend of epidemic diseases in Taiwan. Given the volatility of the infectious diseases in Taiwan, historical average is unlikely to be an effective prediction mechanism. A popular approach has been the stochastic models, in which the Bayesian models and simulations are some of the advanced methods. These methods are all based on data of infectious diseases collected in a specific time and space, and their reliability is determined by the sample size and their theoretical assumptions. As Nsoesie, Beckman, Shashaani, Nagarai and Marathe [4] pointed out, those forecasting models could perform well as long as their assumptions are right and there are good surveillance data. It is difficult to make right assumptions according to different regions around the world and acquire real-time good surveillance data in the real world

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Results

Discussion

Conclusion