Department of Emergency Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America.
PLoS One. 2013;8(2):e56176. doi: 10.1371/journal.pone.0056176. Epub 2013 Feb 14.
We developed a practical influenza forecast model based on real-time, geographically focused, and easy to access data, designed to provide individual medical centers with advanced warning of the expected number of influenza cases, thus allowing for sufficient time to implement interventions. Secondly, we evaluated the effects of incorporating a real-time influenza surveillance system, Google Flu Trends, and meteorological and temporal information on forecast accuracy.
Forecast models designed to predict one week in advance were developed from weekly counts of confirmed influenza cases over seven seasons (2004-2011) divided into seven training and out-of-sample verification sets. Forecasting procedures using classical Box-Jenkins, generalized linear models (GLM), and generalized linear autoregressive moving average (GARMA) methods were employed to develop the final model and assess the relative contribution of external variables such as, Google Flu Trends, meteorological data, and temporal information.
A GARMA(3,0) forecast model with Negative Binomial distribution integrating Google Flu Trends information provided the most accurate influenza case predictions. The model, on the average, predicts weekly influenza cases during 7 out-of-sample outbreaks within 7 cases for 83% of estimates. Google Flu Trend data was the only source of external information to provide statistically significant forecast improvements over the base model in four of the seven out-of-sample verification sets. Overall, the p-value of adding this external information to the model is 0.0005. The other exogenous variables did not yield a statistically significant improvement in any of the verification sets.
Integer-valued autoregression of influenza cases provides a strong base forecast model, which is enhanced by the addition of Google Flu Trends confirming the predictive capabilities of search query based syndromic surveillance. This accessible and flexible forecast model can be used by individual medical centers to provide advanced warning of future influenza cases.
我们开发了一种实用的流感预测模型，该模型基于实时、有针对性且易于获取的数据，旨在为各个医疗中心提供流感病例预期数量的预警，从而有足够的时间实施干预措施。其次，我们评估了在预测中纳入实时流感监测系统（Google Flu Trends）以及气象和时间信息对预测准确性的影响。
我们根据七个季节（2004-2011 年）每周确诊流感病例的数量，设计了用于提前一周预测的预测模型，并将其分为七个训练集和外推验证集。我们采用经典的 Box-Jenkins、广义线性模型（GLM）和广义自回归移动平均（GARMA）方法进行预测程序，以开发最终模型并评估外部变量（如 Google Flu Trends、气象数据和时间信息）的相对贡献。
使用具有负二项分布的 GARMA（3,0）预测模型并整合 Google Flu Trends 信息，可提供最准确的流感病例预测。该模型平均可在 7 个外推爆发中的 7 个样本中，准确预测 83%的每周流感病例。在七个外推验证集中，Google Flu Trends 数据是唯一提供统计上显著预测改进的外部信息来源，在其中四个验证集中超过了基础模型。总体而言，将该外部信息添加到模型中的 p 值为 0.0005。其他外生变量在任何验证集中都没有产生统计学上显著的改进。
流感病例的整数自回归为基础预测模型提供了有力的基础，而添加 Google Flu Trends 则证实了基于搜索查询的综合征监测的预测能力，从而进一步增强了预测能力。这种易于访问和灵活的预测模型可被各个医疗中心用于提前预警未来的流感病例。
