Short-term disease forecasting at specific discrete spatial resolutions has become a high-impact decision-support tool in health planning. However, when the number of areas is very large obtaining predictions can be computationally intensive or even unfeasible using standard spatio-temporal models. The purpose of this paper is to provide a method for short-term predictions in high-dimensional areal data based on a newly proposed ``divide-and-conquer" approach. We assess the predictive performance of this method and other classical spatio-temporal models in a validation study that uses cancer mortality data for the 7907 municipalities of continental Spain. The new proposal outperforms traditional models in terms of mean absolute error, root mean square error and interval score when forecasting cancer mortality one, two and three years ahead. Models are implemented in a fully Bayesian framework using the well-known integrated nested Laplace (INLA) estimation technique.

翻译：在特定离散空间分辨率下的短期疾病预测已成为健康规划中具有高影响力的决策支持工具。然而，当区域数量非常庞大时，使用标准时空模型进行预测可能计算强度过高甚至不可行。本文旨在基于新提出的“分而治之”方法，为高维区域数据中的短期预测提供一种方案。我们利用西班牙本土7907个市镇的癌症死亡率数据开展验证研究，评估了该方法及其他经典时空模型的预测性能。新提出的方法在预测未来一、二、三年的癌症死亡率时，在平均绝对误差、均方根误差和区间得分方面均优于传统模型。模型在完全贝叶斯框架下采用广为人知的集成嵌套拉普拉斯（INLA）估计技术实现。