Published May 30, 2025
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A Comparative Analysis of NNAR and LSTM Models for Short-Term COVID-19 Forecasting in Saudi Arabia
Authors/Creators
- 1. Assistant Professor, Department of Mathematics, College of Science and Arts, Al-Baha University, KSA, Department of Medical Statistics, Faculty of Health Sciences, University of Health Sciences, Blue Nile State Branch, Sudan.
Description
Abstract: The COVID-19 pandemic has posed an ongoing challenge for public health systems around the globe. Accurate forecasting of daily confirmed COVID-19 cases in Saudi Arabia has remained critical for informed planning and timely interventions. This research explores and compares the predictive performance of two artificial neural network models—Nonlinear Autoregressive Neural Network (NNAR) and Long Short-Term Memory (LSTM)—applied to Saudi Arabia’s COVID-19 case data from March 2020 through December 2021. Using standard evaluation metrics, including MAE, RMSE, MAPE, and Theil’s U, the study demonstrates that the NNAR model provides slightly more stable and accurate predictions in short-term horizons than LSTM. While LSTM models are known for capturing complex temporal patterns, our findings suggest that NNAR may offer a more robust option in volatile epidemiological conditions. These insights contribute to the growing field of epidemic forecasting and provide practical considerations for health policymakers in the region.
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Additional details
Identifiers
- DOI
- 10.35940/ijsce.B3657.15020525/
- EISSN
- 2231-2307
Dates
- Accepted
-
2025-05-15Manuscript Received on 16 April 2025 | First Revised Manuscript Received on 24 April 2025 | Second Revised Manuscript Received on 05 May 2025 | Manuscript Accepted on 15 May 2025 | Manuscript published on 30 May 2025.
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