Title: Mixture distributions for probabilistic forecasts of disease outbreaks Show Chinese title

Title: 疾病爆发概率预测的混合分布

Abstract: Collaboration among multiple teams has played a major role in probabilistic forecasting events of influenza outbreaks, the COVID-19 pandemic, other disease outbreaks, and in many other fields. When collecting forecasts from individual teams, ensuring that each team's model represents forecast uncertainty according to the same format allows for direct comparison of forecasts as well as methods of constructing multi-model ensemble forecasts. This paper outlines several common probabilistic forecast representation formats including parametric distributions, sample distributions, bin distributions, and quantiles and compares their use in the context of collaborative projects. We propose the use of a discrete mixture distribution format in collaborative forecasting in place of other formats. The flexibility in distribution shape, the ease for scoring and building ensemble models, and the reasonably low level of computer storage required to store such a forecast make the discrete mixture distribution an attractive alternative to the other representation formats. Show Chinese abstract

Abstract: 多支团队之间的协作在流感爆发、新冠疫情、其他疾病爆发以及许多其他领域的概率预测中发挥了重要作用。 在收集各独立团队的预测时，确保每个团队的模型以相同的格式表示预测不确定性，使得预测结果以及构建多模型集成预测的方法可以直接进行比较。 本文概述了几种常见的概率预测表示格式，包括参数分布、样本分布、区间分布和分位数，并比较了它们在协作项目中的应用。 我们建议在协作预测中使用离散混合分布格式，而不是其他格式。 分布形状的灵活性、评分和构建集成模型的便捷性，以及存储此类预测所需的相对较低的计算机存储量，使得离散混合分布成为其他表示格式的有吸引力的替代方案。