嵌合预测：结合计算模型和人类判断的概率预测
Chimeric forecasting: combining probabilistic predictions from computational models and human judgment
摘要：
对传染源的轨迹进行预测可以帮助指导公共卫生决策。传统的预测方法将计算模型与结构化数据拟合，并生成预测分布。然而，人类判断可以访问与计算模型相同的数据，加上经验，直觉和主观数据。我们提出了一个嵌合集成-计算和人类的判断预测相结合-作为一种新的方法来预测的传染性病原体的轨迹。从2021年1月至2021年6月，我们每个月都要求两个通才群体，使用与COVID-19 Forecast Hub相同的标准，提交未来两周或三周美国国家层面的事件病例和死亡的预测分布，并将这些人类判断预测与提交给COVID-19 ForecastHub的计算模型的预测组合成嵌合集合。我们发现一个嵌合合奏相比，合奏包括只计算模型提高了预测的事件的情况下，并显示出类似的性能预测的事件死亡。嵌合系综是一种灵活的，支持性的公共卫生工具，并显示出有希望的结果预测的传染性病原体的传播。
英文摘要：
Forecasts of the trajectory of an infectious agent can help guide public health decision making. A traditional approach to forecasting fits a computational model to structured data and generates a predictive distribution. However, human judgment has access to the same data as computational models plus experience, intuition, and subjective data. We propose a chimeric ensemble -- a combination of computational and human judgment forecasts -- as a novel approach to predicting the trajectory of an infectious agent. Each month from January, 2021 to June, 2021 we asked two generalist crowds, using the same criteria as the COVID-19 Forecast Hub, to submit a predictive distribution over incident cases and deaths at the US national level either two or three weeks into the future and combined these human judgment forecasts with forecasts from computational models submitted to the COVID-19 Forecasthub into a chimeric ensemble. We find a chimeric ensemble compared to an ensemble including only computational models improves predictions of incident cases and shows similar performance for predictions of incident deaths. A chimeric ensemble is a flexible, supportive public health tool and shows promising results for predictions of the spread of an infectious agent.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
