Coroneo Laura, Iacone Fabrizio, Paccagnini Alessia, Santos Monteiro Paulo
University of York, United Kingdom.
Università degli Studi di Milano, Italy.
Int J Forecast. 2023 Apr-Jun;39(2):606-622. doi: 10.1016/j.ijforecast.2022.01.005. Epub 2022 Jan 31.
We test the predictive accuracy of forecasts of the number of COVID-19 fatalities produced by several forecasting teams and collected by the United States Centers for Disease Control and Prevention for the epidemic in the United States. We find three main results. First, at the short horizon (1 week ahead) no forecasting team outperforms a simple time-series benchmark. Second, at longer horizons (3 and 4 week ahead) forecasters are more successful and sometimes outperform the benchmark. Third, one of the best performing forecasts is the Ensemble forecast, that combines all available predictions using uniform weights. In view of these results, collecting a wide range of forecasts and combining them in an ensemble forecast may be a superior approach for health authorities, rather than relying on a small number of forecasts.
我们测试了几个预测团队对美国疾病控制与预防中心收集的美国新冠肺炎死亡人数预测的准确性。我们发现了三个主要结果。第一，在短期（提前1周），没有预测团队的表现优于简单的时间序列基准。第二，在较长时间范围（提前3周和4周），预测者更为成功，有时表现优于基准。第三，表现最佳的预测之一是综合预测，即使用统一权重将所有可用预测结合起来。鉴于这些结果，对于卫生当局而言，收集广泛的预测并将它们组合成综合预测可能是一种更好的方法，而不是依赖少数预测。
