{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T04:34:20Z","timestamp":1777869260804,"version":"3.51.4"},"reference-count":25,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T00:00:00Z","timestamp":1777507200000},"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:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The COVID-19 pandemic has reshaped the global epidemiology of respiratory infectious diseases, posing new challenges for influenza forecasting. Existing studies are often limited by reliance on single data sources, poor interpretability, or failure to account for nonlinear relationships among variables, which restricts their ability to balance prediction accuracy and practical utility for public health decision-making. This study aimed to develop and validate a multisource data-integrated generalized additive model (GAM) to forecast influenza activity in Shenzhen, China.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Using surveillance and auxiliary data from 2023 to 2025, we developed GAM models incorporating local and Hong Kong influenza surveillance, cross-boundary mobility metric, meteorological factors, and Baidu Search Index data. The predictive performance of the GAM was compared with Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) model. Model accuracy was evaluated using root mean square error (RMSE), mean absolute percentage error (MAPE), and R\n                      <jats:sup>2<\/jats:sup>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The multisource data-driven GAM exhibited high predictive accuracy across short-term forecasting horizons. For 1-week ahead forecasts, the model achieved an R\n                      <jats:sup>2<\/jats:sup>\n                      of 0.85 (95% CI: 0.74\u20130.92). Notably, performance remained robust for 2- and 3-week forecasts, with R\n                      <jats:sup>2<\/jats:sup>\n                      values of 0.80 (95% CI: 0.69\u20130.87) and 0.74 (95% CI: 0.62\u20130.83), respectively. The GAM demonstrated superior overall performance compared with SARIMAX.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The multisource data-integrated GAM provides robust and stable influenza forecasts for Shenzhen up to 3\u202fweeks in advance. This approach provides a valuable tool to support cross-boundary public health collaboration between Hong Kong and Shenzhen, and might serve as a reference for the development of broader regional public health strategies in future research.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1811040","type":"journal-article","created":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T13:23:55Z","timestamp":1777555435000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Generalized additive model integrating multi-source data for short-term influenza forecasting in Shenzhen, China (2023\u20132025)"],"prefix":"10.3389","volume":"14","author":[{"given":"Xing","family":"Li","sequence":"first","affiliation":[{"name":"Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention","place":["Guangzhou, Guangdong, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiuying","family":"Lv","sequence":"additional","affiliation":[{"name":"Shenzhen Center for Disease Control and Prevention","place":["Shenzhen, Guangdong, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianpeng","family":"Xiao","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention","place":["Guangzhou, Guangdong, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhigao","family":"Chen","sequence":"additional","affiliation":[{"name":"Shenzhen Center for Disease Control and Prevention","place":["Shenzhen, Guangdong, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aiping","family":"Deng","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Center for Disease Control and Prevention","place":["Guangzhou, Guangdong, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zuhua","family":"Rong","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention","place":["Guangzhou, Guangdong, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huiyang","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Public Health, University of South China","place":["Hengyang, Hunan, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shu","family":"Xiao","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention","place":["Guangzhou, Guangdong, China"]},{"name":"Department of Public Health and Preventive Medicine, School of Medicine, Jinan University","place":["Guangzhou, Guangdong, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shisong","family":"Fang","sequence":"additional","affiliation":[{"name":"Shenzhen Center for Disease Control and Prevention","place":["Shenzhen, Guangdong, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,4,30]]},"reference":[{"key":"ref1","year":"2025"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"974","DOI":"10.1016\/s1473-3099(24)00176-2","article-title":"Global, regional, and National Incidence and mortality burden of non-Covid-19 lower respiratory infections and Aetiologies, 1990\u20132021: A systematic analysis from the global burden of disease study 2021","volume":"24","author":"Bender","year":"2024","journal-title":"Lancet Infect Dis"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"1093","DOI":"10.1186\/s12879-024-09996-5","article-title":"Study of the driving factors of the abnormal influenza a (H3n2) epidemic in 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