Using prediction polling to harness collective intelligence for disease forecasting

Tara Kirk Sell, Kelsey Lane Warmbrod, Crystal Watson, Marc Trotochaud, Elena Martin, Sanjana J. Ravi, Maurice Balick, Emile Servan-Schreiber

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article number2132
JournalBMC public health
Volume21
Issue number1
DOIs
StatePublished - Dec 2021
Externally publishedYes

Keywords

  • COVID-19
  • Crowd-sourced
  • Ebola
  • Epidemic prediction
  • Forecasting
  • Infectious disease
  • Influenza

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health

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