Seasonal influenza epidemics cause consistent, considerable, widespread loss
annually in terms of economic burden, morbidity, and mortality. With access to
accurate and reliable forecasts of a current or upcoming influenza epidemic's
behavior, policy makers can design and implement more effective
countermeasures. We developed a framework for in-season forecasts of epidemics
using a semiparametric Empirical Bayes framework, and applied it to predict the
weekly percentage of outpatient doctors visits for influenza-like illness, as
well as the season onset, duration, peak time, and peak height, with and
without additional data from Google Flu Trends, as part of the CDC's 2013--2014
"Predict the Influenza Season Challenge". Previous work on epidemic modeling
has focused on developing mechanistic models of disease behavior and applying
time series tools to explain historical data. However, these models may not
accurately capture the range of possible behaviors that we may see in the
future. Our approach instead produces possibilities for the epidemic curve of
the season of interest using modified versions of data from previous seasons,
allowing for reasonable variations in the timing, pace, and intensity of the
seasonal epidemics, as well as noise in observations. Since the framework does
not make strict domain-specific assumptions, it can easily be applied to other
diseases as well. Another important advantage of this method is that it
produces a complete posterior distribution for any desired forecasting target,
rather than mere point predictions. We report prospective
influenza-like-illness forecasts that were made for the 2013--2014 U.S.
influenza season, and compare the framework's cross-validated prediction error
on historical data to that of a variety of simpler baseline predictors.

使用半参数经验贝叶斯框架，结合 Google Flu Trends 数据，开发了一种预测流感季节流行病情况的框架，可针对流感样病毒构建有合理变化的流行曲线，对抗历史数据进行了交叉验证，结果表明该方法更好地预测了未来的病情。