Relative-distance Calibration for Spatial Relative-risk Forecasting: A Predictor–corrector Approach

Abstract

We investigate relative absolute distance as a calibration criterion for the spatial relative-risk predictor–corrector framework developed for COVID-19 morbidity data. The analysis retains seven observation intervals, the original regression and exponential-smoothing components, and the temporal training and forecast structure. Replacing cumulative squared-error loss with cumulative absolute relative distance changes the optimal smoothing weight from 0.38 to 0.35. The proposed method gives R² = 0.955787, compared with R² = 0.915 for the earlier method. This is an increase of 0.040787, or approximately 4.08 percentage points.
The relative-distance calibration achieved R² = 0.955787, compared with R² = 0.915 for the previously published squared-error calibration. Both methods were evaluated using the same observations and transformation. These findings suggest that the choice of calibration criterion influences predictive performance. The findings indicate that proportional-error calibration can alter the fitted predictor–corrector balance and improve agreement. Further validation on additional periods would strengthen the assessment of predictive performance.