Using prediction polling to harness collective intelligence for disease forecasting.
BMC Public Health
; 21(1): 2132, 2021 11 20.
Article
in English
| MEDLINE | ID: covidwho-1526611
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.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
COVID-19
Type of study:
Prognostic study
Limits:
Humans
Language:
English
Journal:
BMC Public Health
Journal subject:
Public Health
Year:
2021
Document Type:
Article
Affiliation country:
S12889-021-12083-y
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