Scalable prediction

Supplementary data of the paper entitled "A scalable approach for short-term disease forecasting in high spatial resolution areal data" (Orozco-Acosta et al., 2023).This repository contains the final model (1st-order neighbourhood + Type IV interaction) fitted using INLA for both lung cancer and overall cancer mortality data.See https://github.com/spatialstatisticsupna/Scalable_Prediction for further details.本数据集为题为《高空间分辨率面域数据下的短期疾病预测可扩展方法》（奥罗斯科-阿科斯塔等人，2023）的论文的补充数据。本仓库收录了针对肺癌与全癌死亡率数据、通过集成嵌套拉普拉斯近似（INLA）拟合得到的最终模型，该模型采用一阶邻域（1st-order neighbourhood）+ IV型交互作用（Type IV interaction）结构。更多详情请访问 https://github.com/spatialstatisticsupna/Scalable_Prediction。