Using prediction polling to harness collective intelligence for disease forecasting

BMC Public Health. 2021 Nov 20;21(1):2132. doi: 10.1186/s12889-021-12083-y.


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.

Keywords: COVID-19; Crowd-sourced; Ebola; Epidemic prediction; Forecasting; Infectious disease; Influenza.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • COVID-19*
  • Disease Outbreaks
  • Forecasting
  • Humans
  • Intelligence
  • Models, Statistical
  • SARS-CoV-2