A hybrid STL–LightGBM framework with probabilistic forecasting for Influenza A incidence in the post-pandemic Saudi Arabia
= 0.987). The framework is further extended to probabilistic forecasting via quantile regression, resulting in accurately calibrated 95% prediction intervals. The uncertainty in the predictions increases appropriately during periods of epidemiological disruption, highlighting the importance of uncertainty-aware prediction under structural change. The proposed STL-LightGBM architecture is a resilient and comprehensible instrument for monitoring influenza in post-pandemic contexts, facilitating early warning systems and expeditious public health decision-making in Saudi Arabia and analogous regions.
Authors
- Huda M. Alshanbari (ORCID: https://orcid.org/0000-0001-5154-7477)
- Reham M. Alahmadi (ORCID: https://orcid.org/0009-0003-8446-7168)
- Maaweya Awadalla (ORCID: https://orcid.org/0000-0002-1270-2216)
- Bandar Alosaimi (ORCID: https://orcid.org/0000-0003-4719-2655)
- Alshaikh A. Shokeralla (ORCID: https://orcid.org/0000-0002-1518-475X)
- Bashayer Saeed
- Fathelrhman EL Guma
- Ali Atif Yassin
Institutions
- Princess Nourah bint Abdulrahman University (SA)
- King Fahd Medical City (SA)
- King Saud University (SA)
- Al Baha University (SA)
- University of Tabuk (SA)
Publication Details
- Journal
- Frontiers in Public Health
- Published
- 2026-04-10
- DOI
- https://doi.org/10.3389/fpubh.2026.1803353
- Primary Topic
- Influenza Virus Research Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- King Fahad Medical City