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- W3215194225 abstract "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." @default.
- W3215194225 created "2021-12-06" @default.
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- W3215194225 date "2021-11-20" @default.
- W3215194225 modified "2023-10-16" @default.
- W3215194225 title "Using prediction polling to harness collective intelligence for disease forecasting" @default.
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- W3215194225 doi "https://doi.org/10.1186/s12889-021-12083-y" @default.
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