Graph-based Prediction of Spatio-Temporal Vaccine Hesitancy from Insurance Claims Data
Webinar
The VaxHesSTL framework combines Graph and Recurrent Neural Networks to predict vaccine hesitancy at the ZIP code level by capturing both spatial relationships and historical trends, outperforming existing models when trained on a six-year, five-million-person insurance claims dataset from Virginia. To address the high cost of such data, the research also explores an active learning approach to optimize which ZIP codes are selected for training.
