Near-term forecasts of influenza-like illness An evaluation of autoregressive time series approaches
Near-term forecasts of influenza-like illness An evaluation of autoregressive time series approaches
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DOI：
10.1016/j.epidem.2019.01.002
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发表时间：
2019-06-01
期刊：
影响因子：
3.8
通讯作者：
Shaman, Jeffrey
中科院分区：
文献类型：
--
作者：
Kandula, Sasikiran;Shaman, Jeffrey
据估计，美国的季节性流感每年造成900万至3500万人患病，由此产生的经济负担达470亿至1500亿美元。可靠的流感实时预测可以帮助公共卫生机构更好地管理这些疫情。在这里，我们研究了三种自回归方法用于短期预测的可行性：具有时变阶的自回归综合移动平均（ARIMA）模型;适合季节性调整发病率的ARIMA模型（ARIMA-STL）;以及具有单隐层的前馈自回归人工神经网络（AR-NN）。我们对美国国家和10个地区在5个流感季节中未来1至4周的流感发病率进行了回顾性预测。我们比较了这三个模型的点和概率预测的相对准确性，并与两个大型外部验证集（每个验证集至少包括20个其他模型）进行了比较，发现AR-NN的概率和点预测总体上比其他两个模型更准确。一个额外的子分析发现，这三个模型大大受益于使用基于搜索趋势的“临近预报”作为监测数据的代理，并且发现使用临近预报的这三个模型是两个验证数据集中排名最高的模型。当nowcast被扣留时，这三个模型相对于验证集中的模型仍然具有竞争力。这三个模型之间的准确性差异，相对于模型的验证集，被发现在很大程度上是统计显着的。我们的研究结果表明，自回归模型，即使没有配备捕捉传播动态可以提供合理准确的近期预测流感。开放源代码库中的现有支持使它们成为模型比较研究和在资源有限的情况下进行业务预测的合适的非幼稚基线，在这种情况下，更复杂的方法可能不可行。
Seasonal influenza in the United States is estimated to cause 9-35 million illnesses annually, with resultant economic burden amounting to $47-$150 billion. Reliable real-time forecasts of influenza can help public health agencies better manage these outbreaks. Here, we investigate the feasibility of three autoregressive methods for near-term forecasts: an Autoregressive Integrated Moving Average (ARIMA) model with time-varying order; an ARIMA model fit to seasonally adjusted incidence rates (ARIMA-STL); and a feed-forward autoregressive artificial neural network with a single hidden layer (AR-NN). We generated retrospective forecasts for influenza incidence one to four weeks in the future at US National and 10 regions in the US during 5 influenza seasons. We compared the relative accuracy of the point and probabilistic forecasts of the three models with respect to each other and in relation to two large external validation sets that each comprise at least 20 other models.Both the probabilistic and point forecasts of AR-NN were found to be more accurate than those of the other two models overall. An additional sub-analysis found that the three models benefitted considerably from the use of search trends based 'nowcast as a proxy for surveillance data, and these three models with use of nowcasts were found to be the highest ranked models in both validation datasets. When the nowcasts were withheld, the three models remained competitive relative to models in the validation sets. The difference in accuracy among the three models, and relative to models of the validation sets, was found to be largely statistically significant.Our results suggest that autoregressive models even when not equipped to capture transmission dynamics can provide reasonably accurate near-term forecasts for influenza. Existing support in open-source libraries make them suitable non-naive baselines for model comparison studies and for operational forecasts in resource constrained settings where more sophisticated methods may not be feasible.