Dynamic Prediction of Vaccination Rates and Regional Differences Based on LSTM Time Series Model
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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.
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References
J. Charron, A. Gautier, and C. Jestin, “Influence of information sources on vaccine hesitancy and practices,” Medecine et maladies infectieuses, vol. 50, no. 8, pp. 727-733, 2020, doi: 10.1016/j.medmal.2020.01.010.
V. Lazarus J, C. Ratzan S, A. Palayew, et al., “A global survey of potential acceptance of a COVID-19 vaccine,” Nature medicine, vol. 27, no. 2, pp. 225-228, 2021, doi: 10.1038/s41591-020-1124-9.
T. Das, “Unveiling spatial heterogeneity: a study of diverse child immunization indicators across Indian districts,” Child Indicators Research, vol. 17, no. 4, pp. 1615-1659, 2024, doi: 10.1007/s12187-024-10135-4.
H. Chang M and T. Tassier, “Spatially heterogeneous vaccine coverage and externalities in a computational model of epidemics,” Computational Economics, vol. 58, no. 1, pp. 27-55, 2021, doi: 10.1007/s10614-019-09918-7.
V. Carrieri, R. Lagravinese, and G. Resce, “Predicting vaccine hesitancy from area-level indicators: A machine learning approach,” Health Economics, vol. 30, no. 12, pp. 3248-3256, 2021, doi: 10.1002/hec.4430.
C. Wheeler D, A. Miller C, K. Do E, et al., “Identifying area-level disparities in human papillomavirus vaccination coverage using geospatial analysis,” Cancer Epidemiology, Biomarkers & Prevention, vol. 30, no. 9, pp. 1689-1696, 2021, doi: 10.1158/1055-9965.EPI-21-0331.
D. Dayaratna K, D. Gonshorowski, and M. Kolesar, “Hierarchical Bayesian spatio-temporal modeling of COVID-19 in the United States,” Journal of Applied Statistics, vol. 50, no. 11-12, pp. 2663-2680, 2023, doi: 10.1080/02664763.2022.2069232.
C. Jentsch P, M. Anand, and T. Bauch C, “Prioritising COVID-19 vaccination in changing social and epidemiological landscapes: a mathematical modelling study,” The Lancet Infectious Diseases, vol. 21, no. 8, pp. 1097-1106, 2021, doi: 10.1016/S1473-3099(21)00057-8.
L. Altarawneh, H. Wang, and Y. Jin, “COVID-19 vaccine prediction based on an interpretable CNN-LSTM model with three-stage feature engineering,” Health and Technology, vol. 14, no. 6, pp. 1241-1261, 2024, doi: 10.1007/s12553-023-00797-5.
M. He, W. Zhu W, Z. Chen H, et al., “Application of optimized LSTM in prediction of the cumulative confirmed cases of COVID-19,” Computer Methods in Biomechanics and Biomedical Engineering, vol. 27, no. 13, pp. 1893-1905, 2024, doi: 10.1080/10255842.2023.2264438.
A. Kumar, P. Singh J, and K. Singh A, “Explainable BERT-LSTM stacking for sentiment analysis of COVID-19 vaccination,” IEEE Transactions on Computational Social Systems, vol. 12, no. 3, pp. 1296-1306, 2023, doi: 10.1109/TCSS.2023.3329664.
M. Burukanli and N. Yumu¸sak, “COVID-19 virus mutation prediction with LSTM and attention mechanisms,” The Computer Journal, vol. 67, no. 10, pp. 2934-2944, 2024, doi: 10.1093/comjnl/bxae058.
R. Westerholt, “A simulation study to explore inference about global Moran’s I with random spatial indexes,” Geographical Analysis, vol. 55, no. 4, pp. 621-650, 2023, doi: 10.1111/gean.12349.
P. Phang, S. Aslam, J. Labadin, et al., “Spatial autocorrelation analysis of infectious disease incidence rates at state and district level using supra-adjacency weights matrix,” Health, vol. 13, no. 2, pp. 456-470, 2025, doi: 10.13189/ujph.2025.130217.
Z. Tao, Q. Xu, X. Liu, et al., “An integrated approach implementing sliding window and DTW distance for time series forecasting tasks,” Applied Intelligence, vol. 53, no. 17, pp. 20614-20625, 2023, doi: 10.1007/s10489-023-04590-9.
C. Holder, M. Middlehurst, and A. Bagnall, “A review and evaluation of elastic distance functions for time series clustering,” Knowledge and Information Systems, vol. 66, no. 2, pp. 765-809, 2024, doi: 10.1007/s10115-023-01952-0.
M. Kaselimi, N. Doulamis, A. Voulodimos, et al., “Context aware energy disaggregation using adaptive bidirectional LSTM models,” IEEE Transactions on Smart Grid, vol. 11, no. 4, pp. 3054-3067, 2020, doi: 10.1109/TSG.2020.2974347.
T. Zhou, A. Tao, L. Sun, et al., “Behavior recognition based on the improved density clustering and context-guided Bi-LSTM model,” Multimedia Tools and Applications, vol. 82, no. 29, pp. 45471-45488, 2023, doi: 10.1007/s11042-023-15501-y.
H. Guo M, X. Xu T, J. Liu J, et al., “Attention mechanisms in computer vision: A survey,” Computational visual media, vol. 8, no. 3, pp. 331-368, 2022, doi: 10.1007/s41095-022-0271-y.
G. Brauwers and F. Frasincar, “A general survey on attention mechanisms in deep learning,” IEEE transactions on knowledge and data engineering, vol. 35, no. 4, pp. 3279-3298, 2021, doi: 10.1109/TKDE.2021.3126456.
D. Zou, Y. Cao, D. Zhou, et al., “Gradient descent optimizes over-parameterized deep ReLU networks,” Machine learning, vol. 109, no. 3, pp. 467-492, 2020, doi: 10.1007/s10994-019-05839-6.
P. Blanchard, J. Higham D, and J. Higham N, “Accurately computing the log-sum-exp and softmax functions,” IMA Journal of Numerical Analysis, vol. 41, no. 4, pp. 2311-2330, 2021, doi: 10.1093/imanum/draa038.
Q. Wang, Y. Ma, K. Zhao, et al., “A comprehensive survey of loss functions in machine learning,” Annals of Data Science, vol. 9, no. 2, pp. 187-212, 2022, doi: 10.1007/s40745-020-00253-5.
R. Moradi, R. Berangi, and B. Minaei, “A survey of regularization strategies for deep models,” Artificial Intelligence Review, vol. 53, no. 6, pp. 3947-3986, 2020, doi: 10.1007/s10462-019-09784-7.
T. Burzykowski, M. Geubbelmans, A. J. Roussea, et al., “Validation of machine learning algorithms,” American journal of orthodontics and dentofacial orthopedics, vol. 164, no. 2, pp. 295-297, 2023, doi: 10.1016/j.ajodo.2023.05.007.
K. Albeladi, B. Zafar, and A. Mueen, “Time series forecasting using LSTM and ARIMA,” International Journal of Advanced Computer Science and Applications, vol. 14, no. 1, pp. 313-320, 2023, doi: 10.14569/ijacsa.2023.0140133.
M. Lovanshi and V. Tiwari, “Human skeleton pose and spatio-temporal feature-based activity recognition using ST-GCN,” Multimedia Tools and Applications, vol. 83, no. 5, pp. 12705-12730, 2024, doi: 10.1007/s11042-023-16001-9.
H. Bai, H. Wang, D. Li, et al., “Information consistency-based measures for spatial stratified heterogeneity,” Annals of the American Association of Geographers, vol. 113, no. 10, pp. 2512-2524, 2023, doi: 10.1080/24694452.2023.2223700.
X. Ran, Y. Xi, Y. Lu, et al., “Comprehensive survey on hierarchical clustering algorithms and the recent developments,” Artificial Intelligence Review, vol. 56, no. 8, pp. 8219-8264, 2023, doi: 10.1007/s10462-022-10366-3.
Y. Komaru, T. Yoshida, Y. Hamasaki, et al., “Hierarchical clustering analysis for predicting 1-year mortality after starting hemodialysis,” Kidney international reports, vol. 5, no. 8, pp. 1188-1195, 2020, doi: 10.15276/imms.v13.no1-2.5.
T. Shi, Y. Qiao, and Q. Zhou, “Spatiotemporal evolution and spatial relevance of urban resilience: Evidence from cities of China,” Growth and Change, vol. 52, no. 4, pp. 2364-2390, 2021, doi: 10.1111/grow.12554.
Z. Wu, D. Zheng, S. Pan, et al., “TraverseNet: Unifying space and time in message passing for traffic forecasting,” IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 2, pp. 2003-2013, 2022, doi: 10.1109/tnnls.2022.3186103.
G. Spadon, S. Hong, B. Brandoli, et al., “Pay attention to evolution: Time series forecasting with deep graph-evolution learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 9, pp. 5368-5384, 2021, doi: 10.1109/TPAMI.2021.3076155.
X. Yuan, L. Li, Y. A. W. Shardt, et al., “Deep learning with spatiotemporal attention-based LSTM for industrial soft sensor model development,” IEEE Transactions on Industrial Electronics, vol. 68, no. 5, pp. 4404-4414, 2020, doi: 10.1109/TIE.2020.2984443.
S. Chaudhari, V. Mithal, G. Polatkan, et al., “An attentive survey of attention models,” ACM Transactions on Intelligent Systems and Technology (TIST), vol. 12, no. 5, pp. 1-32, 2021, doi: 10.1145/3465055.