Using prediction polling to harness collective intelligence for disease forecasting

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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-021-12083-y.