Yamana Teresa K, Kandula Sasikiran, Shaman Jeffrey
Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, US
Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, US.
J R Soc Interface. 2016 Oct;13(123). doi: 10.1098/rsif.2016.0410.
In recent years, a number of systems capable of predicting future infectious disease incidence have been developed. As more of these systems are operationalized, it is important that the forecasts generated by these different approaches be formally reconciled so that individual forecast error and bias are reduced. Here we present a first example of such multi-system, or superensemble, forecast. We develop three distinct systems for predicting dengue, which are applied retrospectively to forecast outbreak characteristics in San Juan, Puerto Rico. We then use Bayesian averaging methods to combine the predictions from these systems and create superensemble forecasts. We demonstrate that on average, the superensemble approach produces more accurate forecasts than those made from any of the individual forecasting systems.
近年来，已经开发出了一些能够预测未来传染病发病率的系统。随着越来越多的此类系统投入使用，重要的是要对这些不同方法所产生的预测进行正式协调，以减少个体预测误差和偏差。在此，我们展示了这种多系统或超级集合预测的首个示例。我们开发了三种不同的登革热预测系统，并将其应用于回顾性预测波多黎各圣胡安的疫情特征。然后，我们使用贝叶斯平均方法来合并这些系统的预测结果，从而创建超级集合预测。我们证明，平均而言，超级集合方法比任何单个预测系统所做出的预测都更准确。
