Even a good influenza forecasting model can benefit from internet-based nowcasts, but those benefits are limited
Even a good influenza forecasting model can benefit from internet-based nowcasts, but those benefits are limited
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DOI：
10.1371/journal.pcbi.1006599
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发表时间：
2019-02-01
影响因子：
4.3
通讯作者：
Priedhorsky, Reid
中科院分区：
文献类型：
--
作者：
Osthus, Dave;Daughton, Ashlynn R.;Priedhorsky, Reid
在美国，及时准确地预测流感的能力会对公共卫生产生重大影响。用互联网数据增强预测已经显示出在受控环境中提高预测准确性和及时性的希望，但实际结果不太令人信服，因为用互联网数据增强的模型并没有始终优于没有互联网数据的模型。在本文中，我们进行了一个对照实验，考虑到数据回填，以提高清晰度的好处和局限性，增加一个已经很好的流感预测模型与基于互联网的nowcast。我们的研究结果表明，一个好的流感预测模型可以受益于增强基于互联网的即时预报在实践中所有考虑的公共卫生相关的预测目标。然而，由于临近预报，预报改善的程度在预报目标之间是不均匀的，短期预报目标的改善最大，季节性目标，如高峰时间和强度的改善相对较小。即使使用了完美的即时预报，目标之间的预测改进也不均衡。这些发现表明，流感预测的进一步改进，特别是季节性目标，将需要从其他非即时预报方法中获得。
The ability to produce timely and accurate flu forecasts in the United States can significantly impact public health. Augmenting forecasts with internet data has shown promise for improving forecast accuracy and timeliness in controlled settings, but results in practice are less convincing, as models augmented with internet data have not consistently outperformed models without internet data. In this paper, we perform a controlled experiment, taking into account data backfill, to improve clarity on the benefits and limitations of augmenting an already good flu forecasting model with internet-based nowcasts. Our results show that a good flu forecasting model can benefit from the augmentation of internet-based nowcasts in practice for all considered public health-relevant forecasting targets. The degree of forecast improvement due to nowcasting, however, is uneven across forecasting targets, with short-term forecasting targets seeing the largest improvements and seasonal targets such as the peak timing and intensity seeing relatively marginal improvements. The uneven forecasting improvements across targets hold even when perfect nowcasts are used. These findings suggest that further improvements to flu forecasting, particularly seasonal targets, will need to derive from other, non-nowcasting approaches.