BMC public health · 2021
Using prediction polling to harness collective intelligence for disease forecasting.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Sell Tara Kirk, Warmbrod Kelsey Lane, Watson Crystal, Trotochaud Marc, Martin Elena, Ravi Sanjana J, Balick Maurice, Servan-Schreiber Emile
Аннотация
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.
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.