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Using prediction polling to harness collective intelligence for disease forecasting
Tara Kirk Sell1,2, Kelsey Lane Warmbrod3,4, Crystal Watson3,4
1Johns Hopkins Center for Health Security, Baltimore, USA. tksell@jhu.edu.
Background:
The global spread of COVID-19 has shown that reliable forecasting of public health related outcomes is important but lacking.
Methods:
We report the results of the first large-scale, long-term experiment in crowd-forecasting of infectious-disease outbreaks, where a total of 562 volunteer participants competed over 15 months to make forecasts on 61 questions with a total of 217 possible answers regarding 19 diseases.
Results:
Consistent with the "wisdom of crowds" phenomenon, we found that crowd forecasts aggregated using best-practice adaptive algorithms are well-calibrated, accurate, timely, and outperform all individual forecasters.
Conclusions:
Crowd forecasting efforts in public health may be a useful addition to traditional disease surveillance, modeling, and other approaches to evidence-based decision making for infectious disease outbreaks.
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