Predicting a dengue outbreak well ahead of time is of immense importance to healthcare personnel. In this study, an ensemble method based on three different types of models has been developed. The proposed approach combines negative binomial regression, autoregressive integrated moving average model and generalized linear autoregressive moving average model through a vector autoregressive structure. Lagged values of terrain and climate covariates are used as regressors. Real-life application using data from San Juan and Iquitos shows that the proposed method usually incurs a mean absolute error of less than 10 cases when the predictions are made 8 weeks in advance. Furthermore, using model confidence set procedure, it is also shown that the proposed method always outperforms other candidate models in providing early prediction for a dengue epidemic.

中文翻译：

---

一种早期预测登革热暴发的集成方法

提前预测登革热疫情对医护人员来说非常重要。在这项研究中，开发了一种基于三种不同类型模型的集成方法。所提出的方法通过向量自回归结构结合了负二项式回归、自回归集成移动平均模型和广义线性自回归移动平均模型。地形和气候协变量的滞后值用作回归量。使用来自圣胡安和伊基托斯的数据的实际应用表明，当提前 8 周进行预测时，所提出的方法通常会产生小于 10 个案例的平均绝对误差。此外，使用模型置信度集程序，