Dynamic Prediction of Vaccination Rates and Regional Differences Based on LSTM Time Series Model

Abstract

Modeling spatiotemporal propagation and dynamic regional interactions is essential for intelligent decision-making in large-scale interconnected systems and communication-aware engineering applications. To overcome the limitations of conventional vaccination prediction methods that neglect dynamic spatial dependencies, this study proposes an LSTM-based forecasting framework integrating spatiotemporal graph convolution, dynamic spatial attention, and spatial regularization mechanisms. A dynamic spatial weight matrix combining geographic adjacency and population mobility is first constructed to characterize inter-regional coupling, after which graph convolution and adaptive attention are employed to capture time-varying spillover effects and evolving influence patterns. Experiments conducted on weekly vaccination data from 90 districts and counties in Zhejiang Province demonstrate that the proposed model reduces MAE and RMSE by 19.0% and 17.2%, respectively, compared with the conventional LSTM while significantly improving the spatial consistency of prediction residuals. Furthermore, the framework reveals hierarchical propagation characteristics from core urban clusters to transportation hubs and peripheral regions through interpretable dynamic attention and spillover analysis. By explicitly modeling spatial interaction and propagation mechanisms rather than isolated temporal sequences, the proposed approach provides a generalized methodology for spatiotemporal signal evolution analysis, network-aware prediction, and adaptive information propagation in complex distributed systems.