An Outlier-Robust Fit for Generalized Additive Models With Applications to Disease Outbreak Detection
用于疾病暴发检测的广义可加模型异常值稳健拟合
We are interested in a class of unsupervised methods to detect possible disease outbreaks, that is, rapid increases in the number of cases of a particular disease that deviate from the pattern observed in the past. The motivating application for this article deals with detecting outbreaks using generalized additive models (GAMs) to model weekly counts of certain infectious diseases. We can use the distance between the predicted and observed counts for a specific week to…