{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T21:36:33Z","timestamp":1793482593125,"version":"4.5.0"},"reference-count":47,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T00:00:00Z","timestamp":1775779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:p>\n                    Influenza A outbreaks in Saudi Arabia exhibit different seasonal patterns, influenced by significant changes, including a near-total halt during the COVID-19 pandemic (2020-2021) and a substantial rebound, as evidenced by national surveillance data, until the end of 2023. Traditional time-series models rely on stationarity and stable seasonal patterns; however, these assumptions are significantly undermined by regime shifts. This study introduces a forecasting method that uses light gradient boost machine (LightGBM) regression, along with Seasonal-Trend decomposition using LOESS (STL), to better track influenza in changing contexts. The proposed method adapts to the evolving epidemiological dynamics shaped by policy and behavioral changes by decomposing the incidence series into long-term trends, stable annual seasonal components, and irregular residual fluctuations prior to nonlinear learning. Exploratory analysis supports strong winter seasonality, linear correlations with meteorological variables, and major structural disruptions linked to pandemic-related interventions. shows how standard SARIMAX and seasonal baseline models cannot be used across all epidemiological regimes. The hybrid model, when evaluated during the test window, shows strong out-of-sample performance, substantially outperforming the benchmark models (\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    = 0.831, MAE = 89.0). In-sample fitting throughout the study period indicates a high degree of representational capacity (\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    = 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.\n                  <\/jats:p>","DOI":"10.3389\/fpubh.2026.1803353","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T06:03:25Z","timestamp":1775801005000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["A hybrid STL\u2013LightGBM framework with probabilistic forecasting for Influenza A incidence in the post-pandemic Saudi Arabia"],"prefix":"10.3389","volume":"14","author":[{"given":"Reham M.","family":"Alahmadi","sequence":"first","affiliation":[{"name":"Department of Botany and Microbiology, College of Science, King Saud University","place":["Riyadh, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maaweya","family":"Awadalla","sequence":"additional","affiliation":[{"name":"Research Center, King Fahad Medical City, Riyadh Second Health Cluster","place":["Riyadh, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bashayer","family":"Saeed","sequence":"additional","affiliation":[{"name":"Research Center, King Fahad Medical City, Riyadh Second Health Cluster","place":["Riyadh, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huda M.","family":"Alshanbari","sequence":"additional","affiliation":[{"name":"Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University","place":["Riyadh, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alshaikh A.","family":"Shokeralla","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Faculty of Science, Al-Baha University","place":["Al-Aqiq, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali Atif","family":"Yassin","sequence":"additional","affiliation":[{"name":"Department of Statistics, Faculty of Science, University of Tabuk","place":["Tabuk, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bandar","family":"Alosaimi","sequence":"additional","affiliation":[{"name":"Research Center, King Fahad Medical City, Riyadh Second Health Cluster","place":["Riyadh, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fathelrhman El","family":"Guma","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Faculty of Science, Al-Baha University","place":["Al-Aqiq, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,4,10]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"108173","DOI":"10.1016\/j.ijid.2025.108173","article-title":"Changes of dynamics of influenza before and after COVID-19 pandemic: a global study in eight regions","volume":"162","author":"Zhang","year":"2025","journal-title":"Int J Infect Dis"},{"key":"B2","doi-asserted-by":"publisher","first-page":"3481","DOI":"10.1111\/ajt.16049","article-title":"Changes in influenza and other respiratory virus activity during the COVID-19 pandemic\u2013United States, 2020-2021","volume":"21","author":"Olsen","year":"2021","journal-title":"Am J Transplantat"},{"key":"B3","doi-asserted-by":"publisher","first-page":"1989","DOI":"10.1016\/j.jiph.2023.09.018","article-title":"Assessing the influence of COVID-19 on influenza prevalence: a multicenter time series study","volume":"16","author":"Saleh","year":"2023","journal-title":"J Infect Public Health"},{"key":"B4","doi-asserted-by":"publisher","first-page":"1354","DOI":"10.1186\/s12879-025-11794-6","article-title":"Impact of non-pharmaceutical interventions of the COVID-19 pandemic on the trend of seasonal influenza in Kingdom of Saudi Arabia","volume":"25","author":"Alqarny","year":"2025","journal-title":"BMC Infect Dis"},{"key":"B5","doi-asserted-by":"publisher","first-page":"547884","DOI":"10.2147\/JMDH.S379150","article-title":"The influence of COVID-19 pandemic on influenza immunization in Saudi Arabia: cross-sectional study","volume":"19","author":"Minshawi","year":"2022","journal-title":"J Multidiscipl Healthc"},{"key":"B6","doi-asserted-by":"publisher","first-page":"432","DOI":"10.1186\/s12879-024-09301-4","article-title":"Seasonality of influenza-like illness and short-term forecasting model in Chongqing from 2010 to (2022)","volume":"24","author":"Chen","year":"2024","journal-title":"BMC Infect Dis"},{"key":"B7","doi-asserted-by":"publisher","first-page":"562","DOI":"10.3390\/ijerph22040562","article-title":"Predictive modeling for pandemic forecasting: a COVID-19 study in New Zealand and partner countries","volume":"22","author":"Baker","year":"2025","journal-title":"Int J Environ Res Public Health"},{"key":"B8","doi-asserted-by":"publisher","first-page":"270","DOI":"10.24171\/j.phrp.2025.0080","article-title":"Analysis of influenza-like illness trends in Saudi Arabia: a comparative study of statistical and deep learning techniques","volume":"16","author":"Guma","year":"2025","journal-title":"Osong Public Health Res Perspect"},{"key":"B9","doi-asserted-by":"publisher","first-page":"30195","DOI":"10.55267\/iadt.07.15132","article-title":"Improving seasonal influenza forecasting using time series machine learning techniques","volume":"9","author":"Alzahrani","year":"2024","journal-title":"J Inform Syst Eng Managem"},{"key":"B10","doi-asserted-by":"publisher","first-page":"e12566","DOI":"10.7717\/peerj.12566","article-title":"Estimating the impact of influenza on the epidemiological dynamics of SARS-CoV-2","volume":"9","author":"de Cell\u00e8s","year":"2021","journal-title":"PeerJ"},{"key":"B11","doi-asserted-by":"publisher","first-page":"6379","DOI":"10.29020\/nybg.ejpam.v18i3.6379","article-title":"Stochastic modelling of seasonal influenza dynamics: integrating random perturbations and behavioural factors","volume":"18","author":"Saadeh","year":"2025","journal-title":"Eur J Pure Appl Mathem"},{"key":"B12","doi-asserted-by":"publisher","first-page":"2550127","DOI":"10.1142\/S179352452550127X","article-title":"Fractional-order modeling of influenza dynamics: impact of memory effect and severity-based variations","volume":"16","author":"El Guma","year":"2025","journal-title":"Int J Biomathem"},{"key":"B13","doi-asserted-by":"publisher","first-page":"100886","DOI":"10.1016\/j.padiff.2024.100886","article-title":"Stochastic modeling of influenza transmission: Insights into disease dynamics and epidemic management","volume":"11","author":"Ali","year":"2024","journal-title":"Part Different Equat Appl Mathem"},{"key":"B14","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.3390\/app9071345","article-title":"Mart\u00ednez B, Sang Y. Wavelet transform application for\/in non-stationary time-series analysis: a review","volume":"9","author":"Rhif","year":"2019","journal-title":"Appl Sci"},{"key":"B15","doi-asserted-by":"publisher","first-page":"109367","DOI":"10.1016\/j.compbiomed.2024.109367","article-title":"Gaussian process regression and wavelet transform time series approaches to modeling influenza A","volume":"184","author":"Agyemang","year":"2025","journal-title":"Comput Biol Med"},{"key":"B16","doi-asserted-by":"publisher","first-page":"103093","DOI":"10.1016\/j.mex.2024.103093","article-title":"Hybrid neural network models for time series disease prediction confronted by spatiotemporal dependencies","volume":"14","author":"Furkan","year":"2025","journal-title":"MethodsX"},{"key":"B17","doi-asserted-by":"publisher","first-page":"5","DOI":"10.14569\/IJACSA.2025.0160535","article-title":"Early detection and forecasting of influenza epidemics using a hybrid ARIMA-GRU model","volume":"16","author":"Annadurai","year":"2025","journal-title":"Int J Adv Comp Sci Appl"},{"key":"B18","doi-asserted-by":"publisher","first-page":"e33848","DOI":"10.1016\/j.heliyon.2024.e33848","article-title":"Comparative performance of hybrid model based on discrete wavelet transform and ARIMA models in prediction incidence of COVID-19","volume":"10","author":"Holakouie-Naieni","year":"2024","journal-title":"Heliyon"},{"key":"B19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3708549","article-title":"Disease outbreak detection and forecasting: a review of methods and data sources","volume":"6","author":"Babanejaddehaki","year":"2025","journal-title":"ACM Trans Comp Healthc"},{"key":"B20","doi-asserted-by":"publisher","first-page":"e1010100","DOI":"10.1371\/journal.pcbi.1010100","article-title":"Enhancing long-term forecasting: learning from COVID-19 models","volume":"18","author":"Rahmandad","year":"2022","journal-title":"PLoS Comput Biol"},{"key":"B21","doi-asserted-by":"publisher","first-page":"24","DOI":"10.3390\/math13010024","article-title":"Forecasting influenza trends using decomposition technique and LightGBM optimized by grey wolf optimizer algorithm","volume":"13","author":"Duan","year":"2024","journal-title":"Mathematics"},{"key":"B22","doi-asserted-by":"publisher","first-page":"136","DOI":"10.3390\/computation13060136","article-title":"Smoothing techniques for improving COVID-19 time series forecasting across countries","volume":"13","author":"Zbezhkhovska","year":"2025","journal-title":"Computation"},{"key":"B23","doi-asserted-by":"publisher","first-page":"52","DOI":"10.3390\/forecast7030052","article-title":"Short-term prediction in an emergency healthcare unit: comparison between ARIMA, ANN, and logistic map models","volume":"7","author":"Ackermann","year":"2025","journal-title":"Forecasting"},{"key":"B24","doi-asserted-by":"publisher","first-page":"1726819","DOI":"10.3389\/fpubh.2025.1726819","article-title":"Deep recurrent models for forecasting infectious diseases","volume":"13","author":"Alzamel","year":"2025","journal-title":"Front Public Health"},{"key":"B25","doi-asserted-by":"publisher","first-page":"1172","DOI":"10.3390\/healthcare9091172","article-title":"Forecast of the COVID-19 epidemic based on RF-BOA-LightGBM","volume":"9","author":"Li","year":"2021","journal-title":"Healthcare"},{"key":"B26","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1186\/s12889-025-22106-7","article-title":"Forecasting dengue across Brazil with LSTM neural networks and SHAP-driven lagged climate and spatial effects","volume":"25","author":"Chen","year":"2025","journal-title":"BMC Public Health"},{"key":"B27","doi-asserted-by":"publisher","first-page":"106303","DOI":"10.1016\/j.rineng.2025.106303","article-title":"Accurate hourly aqi prediction using temporal CNN-LSTM-MHA+ GRU: a case study of seasonal variations and pollution extremes in Visakhapatnam, India","volume":"2025","author":"Sreenivasulu","year":"2025","journal-title":"Results Eng"},{"key":"B28","unstructured":"The FluNet Database\n          \n          2024"},{"key":"B29","article-title":"Data Access Viewer (DAV)","author":"Power","year":"2025","journal-title":"Par\u00e1metros en Santo Domingo Tehuantepec"},{"key":"B30","doi-asserted-by":"publisher","first-page":"200067","DOI":"10.1016\/j.sasc.2023.200067","article-title":"Application of harmony search algorithm in optimizing autoregressive integrated moving average: a study on a data set of Coronavirus Disease (2019)","volume":"6","author":"Karmakar","year":"2024","journal-title":"Syst Soft Comp"},{"key":"B31","doi-asserted-by":"publisher","first-page":"125","DOI":"10.18576\/amis\/180113","article-title":"Comparative analysis of time series prediction models for visceral leishmaniasis: based on SARIMA and LSTM","volume":"18","author":"Guma","year":"2024","journal-title":"Appl Math"},{"key":"B32","first-page":"39408","article-title":"Prediction the daily number of confirmed cases of COVID-19 in Sudan with ARIMA and Holt Winter exponential smoothing","volume":"10","author":"Bezerra","year":"2020","journal-title":"Int J Dev Res"},{"key":"B33","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1007\/978-3-319-52452-8_3","article-title":"ARIMA models","volume-title":"Time Series Analysis and its Applications: With R Examples","author":"Shumway","year":"2017"},{"key":"B34","doi-asserted-by":"publisher","first-page":"e02189","DOI":"10.1016\/j.sciaf.2024.e02189","article-title":"Generalised space-time seasonal autoregressive integrated moving average seemingly unrelated regression modelling of seasonal and non-stationary data","volume":"24","author":"Ajobo","year":"2024","journal-title":"Sci African"},{"key":"B35","author":"Rathi","year":"2025","journal-title":"Introduction to Time Series Analysis"},{"key":"B36","doi-asserted-by":"publisher","first-page":"94","DOI":"10.3390\/inventions7040094","article-title":"seasonal autoregressive integrated moving average with exogenous factors (SARIMAX) forecasting model-based time series approach","volume":"7","author":"Alharbi","year":"2022","journal-title":"Inventions"},{"key":"B37","doi-asserted-by":"publisher","first-page":"105336","DOI":"10.1016\/j.rineng.2025.105336","article-title":"Data-driven hybrid SARIMAX-MLP framework for energy consumption prediction in residential micro-grid","volume":"29","author":"Kachalla","year":"2025","journal-title":"Results Eng"},{"key":"B38","doi-asserted-by":"publisher","first-page":"105","DOI":"10.3390\/e25010105","article-title":"Partial autocorrelation diagnostics for count time series","volume":"25","author":"Wei\u00dfCH","year":"2023","journal-title":"Entropy"},{"key":"B39","doi-asserted-by":"publisher","first-page":"80","DOI":"10.47194\/ijgor.v3i3.168","article-title":"Time series model analysis using autocorrelation function (ACF) and partial autocorrelation function (PACF) for E-wallet transactions during a pandemic","volume":"3","author":"Yakubu","year":"2022","journal-title":"Int J Glob Oper Res"},{"key":"B40","doi-asserted-by":"publisher","first-page":"1540912","DOI":"10.3389\/fmars.2025.1540912","article-title":"Using a seasonal and trend decomposition algorithm to improve machine learning prediction of inflow from the Yellow River, China, into the sea","volume":"12","author":"Wang","year":"2025","journal-title":"Front Mar Sci"},{"key":"B41","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1109\/IC2ECS57645.2022.10088145","article-title":"Research on short-term and medium-term power load forecasting based on STL-LightGBM","volume-title":"2022 2nd International Conference on Electrical Engineering and Control Science (IC2ECS)","author":"Fang","year":"2022"},{"key":"B42","doi-asserted-by":"publisher","first-page":"222","DOI":"10.3390\/act13060222","article-title":"Fault detection of flow control valves using online lightgbm and stl decomposition","volume":"13","author":"Liu","year":"2024","journal-title":"Actuators"},{"key":"B43","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1007\/s41060-025-00975-w","article-title":"Time-series forecasting for political violence targeting women","volume":"21","author":"Thida","year":"2026","journal-title":"Int J Data Sci Analyt"},{"key":"B44","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1186\/s12963-025-00433-6","article-title":"Modeling COVID-19 response in Cuba: a hybrid approach combining agent-based modeling and time series analysis","volume":"23","author":"Orlando","year":"2025","journal-title":"Popul Health Metr"},{"key":"B45","first-page":"1","article-title":"Modeling climate-driven cholera outbreaks: A negative binomial regression framework with improved handling of overdispersion and extreme events","volume":"12","author":"Shokeralla","year":"2025","journal-title":"Lett Biomathem"},{"key":"B46","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1186\/s12879-017-2365-1","article-title":"A framework for evaluating epidemic forecasts","volume":"17","author":"Tabataba","year":"2017","journal-title":"BMC Infect Dis"},{"key":"B47","doi-asserted-by":"publisher","first-page":"299","DOI":"10.6000\/1929-6029.2025.14.29","article-title":"hybrid time series-regression model for tuberculosis forecasting in resource-limited settings","volume":"14","author":"Shokeralla","year":"2025","journal-title":"Int J Statist Med Res"}],"container-title":["Frontiers in Public Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2026.1803353\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T06:03:29Z","timestamp":1775801009000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2026.1803353\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,10]]},"references-count":47,"alternative-id":["10.3389\/fpubh.2026.1803353"],"URL":"https:\/\/doi.org\/10.3389\/fpubh.2026.1803353","relation":{},"ISSN":["2296-2565"],"issn-type":[{"value":"2296-2565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,10]]},"article-number":"1803353"}}