Johns Hopkins Center for Health Security, Baltimore, USA.
Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA.
BMC Public Health. 2021 Nov 20;21(1):2132. doi: 10.1186/s12889-021-12083-y.
The global spread of COVID-19 has shown that reliable forecasting of public health related outcomes is important but lacking.
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.
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.
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.
COVID-19 在全球范围内的传播表明，可靠地预测与公共卫生相关的结果非常重要，但目前还缺乏这种预测能力。
我们报告了首次大规模、长期的传染病疫情人群预测实验的结果，共有 562 名志愿者参与了此次实验，他们在 15 个月的时间里对 61 个问题进行了预测，总共有 217 个可能的答案涉及 19 种疾病。
与“群体智慧”现象一致，我们发现使用最佳实践自适应算法聚合的群体预测具有良好的校准性、准确性、及时性，并且优于所有个体预测者。
在公共卫生领域，群体预测工作可能是对传统疾病监测、建模以及其他基于证据的传染病爆发决策方法的有益补充。
