流感季节的特点每年有很大差异,对公共卫生准备和应对构成挑战。流感预测用于通知季节性爆发反应,这反过来又有可能减少流行病的影响。美国疾病控制和预防中心,与外部研究人员合作,进行了年度前瞻性流感预测工作,被称为FluSight挑战。将预测文献的理论结果与流感爆发的特定领域预测相结合,我们应用参数预测组合方法,同时优化模型权重,并通过β变换校准集合,并对方法进行调整,以降低其复杂性。我们使用了β变换线性池,有限β混合模型,以及他们的等权重调整,以对美国2016/2017、2017/2018和2018/2019流感季节进行回顾性预测我们将它们的性能与FluSight挑战中使用的方法进行了比较,以产生FluSight网络集成,即平均加权线性池和线性池。根据平均对数分数,在测试季节中,通过采用beta变换的方法产生的集合预测在所有一周前目标中都优于同等加权线性池和线性池的预测。尽管在所有目标和季节中都采用了β转换的线性池或β混合方法,但我们观察到总体准确性的提高。应考虑对线性汇集中的已知校准问题进行明确调整的组合技术,以提高爆发设置中的概率得分。
The characteristics of influenza seasons vary substantially from year to year, posing challenges for public health preparation and response. Influenza forecasting is used to inform seasonal outbreak response, which can in turn potentially reduce the impact of an epidemic. The United States Centers for Disease Control and Prevention, in collaboration with external researchers, has run an annual prospective influenza forecasting exercise, known as the FluSight challenge. Uniting theoretical results from the forecasting literature with domain-specific forecasts from influenza outbreaks, we applied parametric forecast combination methods that simultaneously optimize model weights and calibrate the ensemble via a beta transformation and made adjustments to the methods to reduce their complexity. We used the beta-transformed linear pool, the finite beta mixture model, and their equal weight adaptations to produce ensemble forecasts retrospectively for the 2016/2017, 2017/2018, and 2018/2019 influenza seasons in the U.S. We compared their performance to methods that were used in the FluSight challenge to produce the FluSight Network ensemble, namely the equally weighted linear pool and the linear pool. Ensemble forecasts produced from methods with a beta transformation were shown to outperform those from the equally weighted linear pool and the linear pool for all week-ahead targets across in the test seasons based on average log scores. We observed improvements in overall accuracy despite the beta-transformed linear pool or beta mixture methods\' modest under-prediction across all targets and seasons. Combination techniques that explicitly adjust for known calibration issues in linear pooling should be considered to improve probabilistic scores in outbreak settings.