{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T22:16:03Z","timestamp":1789164963361,"version":"build-2803163510"},"reference-count":24,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Form Res"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Respiratory infectious disease surveillance increasingly requires timely integration of routine case reporting, laboratory pathogen surveillance, and digital behavioral signals. However, the incremental predictive value of spatially stratified regional pathogen activity and internet search data for short-term urban respiratory disease prediction remains unclear.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aimed to develop and initially validate an exploratory multisource prediction framework for short-term prediction and early warning of respiratory infectious disease activity in Beijing by integrating reported case data, spatially stratified regional pathogen surveillance indicators, and Baidu index search data.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n We conducted a retrospective time-series modeling study using weekly reported respiratory infectious disease cases in Beijing, regional pathogen surveillance data from 31 provincial-level administrative regions in China, and Baidu index search data from January 2023 to December 2025. Pathogen indicators were aggregated by spatial strata and lagged by 0 to 4 weeks. Ten candidate LASSO (least absolute shrinkage and selection operator)\u2013Poisson models were constructed to compare historical case terms, seasonal terms, local and regional pathogen indicators, and Baidu index indicators. One-week-ahead prediction was the primary task. Models were evaluated using a chronological train-test split and rolling-origin cross-validation for penalty parameter selection. Performance was assessed using root mean squared error (RMSE), mean absolute error (MAE), symmetric mean absolute percentage error (SMAPE), and RMSE reduction relative to the baseline historical case model.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n The M10 core multisource model achieved the lowest overall RMSE among all candidate models, with an RMSE of 16,658.24; an MAE of 10,303.07; and an SMAPE of 0.766, corresponding to a 73.5% RMSE reduction relative to the M1 baseline model. The M3 regional pathogen model ranked second by RMSE, whereas the M7 Baidu index model achieved the lowest SMAPE, indicating better relative performance during low-burden periods. Selected predictors included regional overall pathogen positivity, influenza A positivity, coinfection-related indicators, historical case terms, and Baidu index disease name search indicators.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n The core multisource model improved the 1-week-ahead prediction of respiratory infectious disease case counts in Beijing, although relative prediction performance differed across models. Spatially stratified regional pathogen activity indicators, particularly overall pathogen positivity, influenza A positivity, and coinfection-related indicators, served as important supplementary signals, while Baidu index data provided complementary digital information. These findings support the feasibility of an exploratory multisource prediction framework in Beijing. Further prospective temporal validation and spatial validation in other megacities are needed before broader application. The core multisource model improved 1-week-ahead absolute case count prediction of respiratory infectious diseases in Beijing according to RMSE but did not achieve the lowest relative error according to SMAPE. These findings support the feasibility of an exploratory multisource prediction framework in Beijing. Further prospective temporal validation and spatial validation in other megacities or urban settings are needed before broader application.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/100492","type":"journal-article","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T21:41:41Z","timestamp":1789162901000},"page":"e100492-e100492","source":"Crossref","is-referenced-by-count":0,"title":["Multisource Early Warning Framework Integrating Regional Pathogen Surveillance and Baidu Index Data for Respiratory Infectious Disease Prediction in Beijing: Retrospective Time-Series Modeling Study"],"prefix":"10.2196","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-1827-3453","authenticated-orcid":false,"given":"Yang","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanlin","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9005-9046","authenticated-orcid":false,"given":"Jing","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunping","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2108-5456","authenticated-orcid":false,"given":"Gang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,9,11]]},"reference":[{"key":"R1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-publhealth-040119-094402","article-title":"Social media- and internet-based disease surveillance for public health","volume":"41","author":"Aiello","journal-title":"Annu Rev Public Health"},{"issue":"2","key":"R2","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1016\/j.idm.2024.12.001","article-title":"Global infectious disease early warning models: an updated review and lessons from the COVID-19 pandemic","volume":"10","author":"Hu","journal-title":"Infect Dis Model"},{"issue":"37","key":"R3","doi-asserted-by":"publisher","first-page":"939","DOI":"10.46234\/ccdcw2024.195","article-title":"Mapping the characteristics of respiratory infectious disease epidemics in China based on the Baidu index from November 2022 to January 2023","volume":"6","author":"Huo","journal-title":"China CDC Wkly"},{"key":"R4","unstructured":"Global Influenza Surveillance and Response System (GISRS). 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