Flexible Modeling of Epidemics with an Empirical Bayes Framework.
Flexible Modeling of Epidemics with an Empirical Bayes Framework.
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DOI：
10.1371/journal.pcbi.1004382
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发表时间：
2015-08
影响因子：
4.3
通讯作者：
Rosenfeld R
中科院分区：
文献类型：
--
作者：
Brooks LC;Farrow DC;Hyun S;Tibshirani RJ;Rosenfeld R
季节性流感流行每年在经济负担、发病率和死亡率方面造成持续的、相当大的、广泛的损失。有了对当前或即将到来的流感流行行为的准确和可靠的预测，决策者就可以设计和实施更有效的对策。去年，美国疾病控制与预防中心（Centers for Disease Control and Prevention）举办了“预测流感季节挑战”，其任务是借助数字监测数据预测2013-2014年美国流感季节的主要流行病学措施。我们开发了一个使用半参数经验贝叶斯框架的流行病季节预测框架，并将其应用于预测每周门诊医生对流感样疾病的访问百分比，以及季节发作，持续时间，高峰时间和高峰高度，有无使用谷歌流感趋势数据。以前在流行病建模方面的工作主要集中在开发疾病行为的机制模型和应用时间序列工具来解释历史数据。然而，将这些模型定制为特定类型的监测数据可能具有挑战性，并且具有许多参数的过于复杂的模型可能会损害预测能力。相反，我们的方法利用来自前几个季节的数据的修改版本，考虑到季节性流行病的时间、速度和强度的合理变化，以及观测中的噪声，从而产生感兴趣季节的流行病曲线的可能性。由于该框架没有做出严格的特定领域假设，因此可以很容易地将其应用于其他一些具有季节性流行的疾病。这种方法产生流行病曲线上的完整后验分布，而不是，例如，对预测目标的单点预测。我们报告了2013-2014年美国流感季节的流感样疾病预测，并将该框架在历史数据上的交叉验证预测误差与各种简单基线预测误差进行了比较。流感流行每年都会发生，造成生产力损失、疾病和死亡等重大损失。决策者采用诸如疫苗接种运动等对策来对抗传染病的发生和传播，但流行病表现出广泛的行为，这使得设计和规划这些努力变得困难。对流行病将如何发展的准确和可靠的数字预测，以及对关键事件的提前通知，可以使决策者能够进一步针对特定季节采取专门的对策。虽然在过去季节的流行病建模方面已经有了大量的工作，但在预测方面的工作相对较少。专门为历史数据量身定制的模型可能过于严格，无法产生与当前季节相似的行为。我们设计了一个预测流行病的框架，没有对疾病如何传播做出强有力的假设，而是依靠对过去流行病的轻微修改来形成当前季节的可能性。我们报告了2013-2014年美国疾病控制与预防中心（CDC）“预测流感季节挑战”的预测结果，并对其准确性进行了回顾性评估。
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. This past year, the Centers for Disease Control and Prevention hosted the “Predict the Influenza Season Challenge”, with the task of predicting key epidemiological measures for the 2013–2014 U.S. influenza season with the help of digital surveillance data. 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, and the season onset, duration, peak time, and peak height, with and without using Google Flu Trends data. Previous work on epidemic modeling has focused on developing mechanistic models of disease behavior and applying time series tools to explain historical data. However, tailoring these models to certain types of surveillance data can be challenging, and overly complex models with many parameters can compromise forecasting ability. 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 some other diseases with seasonal epidemics. This method produces a complete posterior distribution over epidemic curves, rather than, for example, solely point predictions of forecasting targets. We report prospective influenza-like-illness forecasts 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. Influenza epidemics occur annually, and incur significant losses in terms of lost productivity, sickness, and death. Policy makers employ countermeasures, such as vaccination campaigns, to combat the occurrence and spread of infectious diseases, but epidemics exhibit a wide range of behavior, which makes designing and planning these efforts difficult. Accurate and reliable numerical forecasts of how an epidemic will behave, as well as advance notice of key events, could enable policy makers to further specialize countermeasures for a particular season. While a large amount of work already exists on modeling epidemics in past seasons, work on forecasting is relatively sparse. Specially tailored models for historical data may be overly strict and fail to produce behavior similar to the current season. We designed a framework for predicting epidemics without making strong assumptions about how the disease propagates by relying on slightly modified versions of past epidemics to form possibilities for the current season. We report forecasts generated for the 2013–2014 Centers for Disease Control and Prevention (CDC) “Predict the Influenza Season Challenge”, and assess its accuracy retrospectively.