Multiplicative Gate State Space Models with Skip-Net for High Accuracy COVID-19 Time-Series Prediction

The rapid propagation of the COVID-19 pandemic has placed unprecedented strain on global healthcare systems, creating an urgent need for accurate forecasting to optimize resource allocation and policy implementation. However, the highly non-linear and chaotic behavior of infection rates poses significant challenges for traditional statistical and standard deep learning models. This study proposes the Multiplicative Gating State Space Model with skip connection (MG-SSM-s), a novel architecture designed to capture complex temporal dependencies in epidemiological time series. Drawing inspiration from partial autocorrelation, the model extends modern State Space Models (SSMs) by incorporating a learnable multiplicative side channel to dynamically modulating input processing. We evaluated the efficacy of MG-SSM-s using the Google COVID-19 Open Data repository, analyzing daily confirmed cases across 40 countries, including major epicenters such as the USA, India, and Brazil. Using a 30-day look-back window, the proposed model was benchmarked against four baseline architectures: LSTM, Bi-LSTM, GRU, and standard SSM. Performance verification based on Mean Squared Error (MSE) demonstrates that MG-SSM-s outperforms all deep learning baselines and achieves competitive accuracy with a tuned ARIMA model, demonstrating comparable statistical performance to the latter. These results highlight the framework’s robustness and potential as a versatile tool for time-series forecasting.

@article{Sinapiromsaran2026,
  title     = {Multiplicative Gate State Space Models with Skip-Net for High Accuracy COVID-19 Time-Series Prediction},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Krung Sinapiromsaran and Supakit Sroynam},
  doi       = {10.14569/IJACSA.2026.0170677},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170677}
}