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.
【저자키워드】 COVID-19, Influenza, Infectious disease, Forecasting, Ebola, Epidemic prediction, Crowd-sourced, 【초록키워드】 public health, Diseases, adaptive, outcome, Outbreaks, Algorithm, Disease surveillance, experiment, supplementary material, effort, participant, crowd, approach, spread of COVID-19, Result, shown, addition, question, 【제목키워드】 disease,