Buczak Anna L, Baugher Benjamin, Moniz Linda J, Bagley Thomas, Babin Steven M, Guven Erhan
Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland, United States of America.
Duke University, Durham, North Carolina, United States of America.
PLoS One. 2018 Jan 3;13(1):e0189988. doi: 10.1371/journal.pone.0189988. eCollection 2018.
In the 2015 NOAA Dengue Challenge, participants made three dengue target predictions for two locations (Iquitos, Peru, and San Juan, Puerto Rico) during four dengue seasons: 1) peak height (i.e., maximum weekly number of cases during a transmission season; 2) peak week (i.e., week in which the maximum weekly number of cases occurred); and 3) total number of cases reported during a transmission season. A dengue transmission season is the 12-month period commencing with the location-specific, historical week with the lowest number of cases. At the beginning of the Dengue Challenge, participants were provided with the same input data for developing the models, with the prediction testing data provided at a later date.
Our approach used ensemble models created by combining three disparate types of component models: 1) two-dimensional Method of Analogues models incorporating both dengue and climate data; 2) additive seasonal Holt-Winters models with and without wavelet smoothing; and 3) simple historical models. Of the individual component models created, those with the best performance on the prior four years of data were incorporated into the ensemble models. There were separate ensembles for predicting each of the three targets at each of the two locations.
Our ensemble models scored higher for peak height and total dengue case counts reported in a transmission season for Iquitos than all other models submitted to the Dengue Challenge. However, the ensemble models did not do nearly as well when predicting the peak week.
The Dengue Challenge organizers scored the dengue predictions of the Challenge participant groups. Our ensemble approach was the best in predicting the total number of dengue cases reported for transmission season and peak height for Iquitos, Peru.
在2015年美国国家海洋和大气管理局登革热挑战赛中，参与者针对四个登革热季节对两个地点（秘鲁伊基托斯和波多黎各圣胡安）进行了三次登革热目标预测：1）峰值高度（即传播季节中每周病例数的最大值）；2）峰值周（即每周病例数达到最大值的那一周）；3）传播季节报告的病例总数。登革热传播季节是从特定地点历史上病例数最少的那一周开始的12个月期间。在登革热挑战赛开始时，为参与者提供了用于建立模型的相同输入数据，预测测试数据在之后提供。
我们的方法使用了通过组合三种不同类型的组件模型创建的集成模型：1）结合登革热和气候数据的二维相似模型；2）带和不带小波平滑的加法季节性霍尔特-温特斯模型；3）简单的历史模型。在创建的各个组件模型中，那些在前四年数据上表现最佳的模型被纳入集成模型。在两个地点的每一个地点，针对三个目标中的每一个都有单独的集成模型。
对于伊基托斯，我们的集成模型在预测传播季节报告的峰值高度和登革热病例总数方面的得分高于提交给登革热挑战赛的所有其他模型。然而，在预测峰值周时，集成模型的表现就没那么好了。
登革热挑战赛组织者对挑战赛参与者组的登革热预测进行了评分。我们的集成方法在预测秘鲁伊基托斯传播季节报告的登革热病例总数和峰值高度方面是最佳的。
