{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T20:59:29Z","timestamp":1790197169131,"version":"4.1.0"},"reference-count":32,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Traditional laboratory-confirmed influenza surveillance involves a 1- to 2-week reporting delay and captures only patients who have sought care and received a diagnosis, limiting early warning. Digital prescription data have been shown to signal influenza activity early, but their predictive value for forecasting remains unclear.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n We aimed to evaluate the predictive value of digital prescription data for influenza forecasting by assessing lead times, contributions across forecasting horizons, and added value when combined with other real-time sources.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n Using daily data for Jiangxi Province, China (2022-2024), we characterized temporal relationships for 13 multisource indicators using prewhitened cross-correlation and performed Granger-based predictor screening restricted to the 2022 to 2023 development period. Incidence was then forecast 1 to 14 days ahead through expanding-window rolling-origin validation trained on 2022 to 2023 data and tested on 2024 data. Six models (autoregressive integrated moving average [ARIMA], seasonal ARIMA [SARIMA], Prophet, least absolute shrinkage and selection operator [LASSO], random forest, and explainable boosting machine) plus a persistence baseline were compared across 3 exogenous-input scenarios (no external input, digital prescriptions alone, and selected sources). Each indicator was also forecast from its own history and compared with observed incidence.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n After prewhitening, the digital prescription rate led influenza incidence by 14 days, whereas the online search index lagged incidence by 1 day, indicating near-synchronous tracking. In the development period Granger analysis, digital prescription rate, online search index, and temperature showed significant predictor-to-incidence associations after false discovery rate (FDR) correction (FDR-adjusted\n P<\/jats:italic>\n <.001,\n P<\/jats:italic>\n =.003, and\n P<\/jats:italic>\n =.008, respectively), whereas nitrogen dioxide\n \n showed a marginal association (FDR-adjusted\n P<\/jats:italic>\n =.11) and was additionally included as an environmental covariate. At 1 day, performance was similar across models and input scenarios (\n R<\/jats:italic>\n 2<\/jats:sup>\n =0.925\u20100.961; persistence\n R<\/jats:italic>\n 2<\/jats:sup>\n =0.948), but differences emerged at longer horizons. SARIMA performed best with the digital prescription rate as the sole exogenous input, and LASSO performed best with 4-source input, both reaching a mean\n R<\/jats:italic>\n 2<\/jats:sup>\n of 0.766 and root mean squared error (RMSE) of 0.240 across the 14 horizons. At 14 days, single-source SARIMA achieved an\n R<\/jats:italic>\n 2<\/jats:sup>\n of 0.510 (RMSE 0.318), and multisource LASSO achieved an\n R<\/jats:italic>\n 2<\/jats:sup>\n of 0.551 (RMSE 0.305), a gain of 0.064 in\n R<\/jats:italic>\n 2<\/jats:sup>\n and a 6.2% reduction in RMSE over LASSO without exogenous input. Forecasts of the digital prescription rate showed the closest temporal consistency with observed influenza incidence (mean\n r<\/jats:italic>\n =0.897 vs 0.866 for the online search index).\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Digital prescriptions provide an early signal of provincial influenza activity, preceding routine reported incidence by approximately 2 weeks in this setting. Multisource input can further improve forecasting when paired with models capable of selecting informative signals, but indiscriminate integration does not necessarily help. This early signal may support earlier preparedness, including antiviral planning and health care resource allocation.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/96131","type":"journal-article","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T20:00:23Z","timestamp":1790193623000},"page":"e96131-e96131","source":"Crossref","is-referenced-by-count":0,"title":["Digital Prescription\u2013Based Influenza Activity Forecasting in Jiangxi Province, China: Comparative Modeling Analysis Using Multisource Data"],"prefix":"10.2196","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4868-5985","authenticated-orcid":false,"given":"Rongrong","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2882-7470","authenticated-orcid":false,"given":"Zilu","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8368-1208","authenticated-orcid":false,"given":"Chi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6661-1849","authenticated-orcid":false,"given":"Liping","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7527-9005","authenticated-orcid":false,"given":"Jie","family":"Qian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1856-8564","authenticated-orcid":false,"given":"Jie","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9134-5642","authenticated-orcid":false,"given":"Yonghai","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0247-676X","authenticated-orcid":false,"given":"Zhili","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5200-9255","authenticated-orcid":false,"given":"Rui","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5206-5995","authenticated-orcid":false,"given":"Luzhao","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,9,23]]},"reference":[{"key":"R1","unstructured":"Influenza (seasonal). World Health Organization. 2025. URL: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/influenza-(seasonal) [Accessed 25-01-2026]"},{"issue":"11","key":"R2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1007486","article-title":"Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U.S","volume":"15","author":"Reich","journal-title":"PLoS Comput Biol"},{"issue":"1","key":"R3","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-50601-9","article-title":"Evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizations","volume":"15","author":"Mathis","journal-title":"Nat Commun"},{"issue":"7","key":"R4","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1002616","article-title":"Digital epidemiology","volume":"8","author":"Salath\u00e9","journal-title":"PLoS Comput Biol"},{"issue":"7232","key":"R5","doi-asserted-by":"publisher","first-page":"1012","DOI":"10.1038\/nature07634","article-title":"Detecting influenza epidemics using search engine query data","volume":"457","author":"Ginsberg","journal-title":"Nature"},{"issue":"6176","key":"R6","doi-asserted-by":"publisher","first-page":"1203","DOI":"10.1126\/science.1248506","article-title":"Big data. The parable of Google Flu: traps in big data analysis","volume":"343","author":"Lazer","journal-title":"Science"},{"key":"R7","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2025.1661753","article-title":"Monitoring OTC drug sales for early detection of respiratory infectious disease outbreaks","volume":"13","author":"Li","journal-title":"Front Public Health"},{"issue":"1","key":"R8","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1038\/s44482-026-00014-w","article-title":"Anticipating influenza-like illness outbreaks via syndromic surveillance using over-the-counter drug sales and primary health care data","volume":"1","author":"Oliveira","journal-title":"NPJ Digit Public Health"},{"issue":"1","key":"R9","doi-asserted-by":"publisher","DOI":"10.1038\/s43856-024-00714-5","article-title":"Bias in mobility datasets drives divergence in modeled outbreak dynamics","volume":"5","author":"Chin","journal-title":"Commun Med (Lond)"},{"issue":"3","key":"R10","doi-asserted-by":"publisher","first-page":"111","DOI":"10.17929\/tqs.5.111","article-title":"Prediction of influenza outbreaks using pharmacy sales data","volume":"5","author":"Sunamura","journal-title":"Total Qual Sci"},{"issue":"4","key":"R11","doi-asserted-by":"publisher","first-page":"e172","DOI":"10.1093\/cid\/ciaf178","article-title":"Patterns in prescribing and dispensing influenza antivirals among adults with influenza presenting to urgent care and emergency department settings: VISION Network, 2023-2024","volume":"81","author":"Adams","journal-title":"Clin Infect Dis"},{"key":"R12","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijid.2024.107195","article-title":"Pathogen characteristics of respiratory infections in the season after the COVID-19 pandemic between August and December 2023: evidence from direct-to-consumer testing-based surveillance in Guangzhou and Beijing, China","volume":"147","author":"Sun","journal-title":"Int J Infect Dis"},{"issue":"5","key":"R13","doi-asserted-by":"publisher","first-page":"1863","DOI":"10.1111\/all.70203","article-title":"Digital prescription surveillance reveals seasonal patterns and environmental triggers of allergic disease activity across China","volume":"81","author":"Shen","journal-title":"Allergy"},{"issue":"1","key":"R14","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-026-02513-9","article-title":"Real-time digital prescriptions unlock influenza dynamics: evidence from 21 million transactions","volume":"9","author":"Shen","journal-title":"NPJ Digit Med"},{"key":"R15","unstructured":"Measures for the supervision and administration of online drug sales [Article in Chinese]. Ministry of Justice of the People\u2019s Republic of China. 2022. URL: https:\/\/www.moj.gov.cn\/pub\/sfbgw\/flfggz\/flfggzbmgz\/202305\/t20230509_478415.html [Accessed 25-07-2026]"},{"key":"R16","unstructured":"Baidu Index. URL: https:\/\/index.baidu.com\/v2\/index.html [Accessed 25-07-2026]"},{"issue":"4","key":"R17","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1016\/j.imed.2025.02.003","article-title":"Enhancing spatiotemporal influenza prediction in China: a multi-output least absolute shrinkage and selection operator machine learning model integrating web-based search data","volume":"5","author":"Shen","journal-title":"Intell Med"},{"key":"R18","unstructured":"Nationwide population migration details-Baidu Map migration big data platform. Baidu Maps. URL: https:\/\/qianxi.baidu.com [Accessed 25-07-2026]"},{"key":"R19","volume":"50","author":"Wang","journal-title":"J Univ Electron Sci Technol China"},{"key":"R20","unstructured":"Hyndman RJ Athanasopoulos G . Dynamic harmonic regression. In: Forecasting: Principles and Practice. 3rd ed. OTexts; 2021."},{"key":"R21","unstructured":"China Meteorological Data Network. URL: http:\/\/data.cma.cn [Accessed 25-07-2026]"},{"key":"R22","unstructured":"China National Environmental Monitoring Centre. URL: https:\/\/www.cnemc.cn [Accessed 25-07-2026]"},{"key":"R23","unstructured":"Box GE Jenkins GM Reinsel GC Ljung GM . Time Series Analysis: Forecasting and Control. 5th ed. John Wiley & Sons; 2015. ISBN: 9781118675021"},{"issue":"3","key":"R24","doi-asserted-by":"publisher","first-page":"424","DOI":"10.2307\/1912791","article-title":"Investigating causal relations by econometric models and cross-spectral methods","volume":"37","author":"Granger","journal-title":"Econometrica"},{"issue":"12","key":"R25","doi-asserted-by":"publisher","DOI":"10.1289\/EHP12146","article-title":"Short-term exposure to ambient air pollution and influenza: a multicity study in China","volume":"131","author":"Yu","journal-title":"Environ Health Perspect"},{"key":"R26","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2022.104421","article-title":"Influence of air pollution on influenza-like illness in China: a nationwide time-series analysis","volume":"87","author":"Yang","journal-title":"EBioMedicine"},{"key":"R27","doi-asserted-by":"publisher","DOI":"10.1186\/s12879-014-0604-2","article-title":"Drug sales data analysis for outbreak detection of infectious diseases: a systematic literature review","volume":"14","author":"Pivette","journal-title":"BMC Infect Dis"},{"issue":"1","key":"R28","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Tibshirani","journal-title":"J R Stat Soc Series B Stat Methodol"},{"issue":"1","key":"R29","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-026-72655-7","article-title":"A real-time early warning system to anticipate respiratory disease outbreaks using transfer learning","volume":"17","author":"Garrido-Garcia","journal-title":"Nat Commun"},{"issue":"1","key":"R30","doi-asserted-by":"publisher","DOI":"10.1038\/s43856-023-00424-4","article-title":"Forecasting influenza hospital admissions within English sub-regions using hierarchical generalised additive models","volume":"3","author":"Mellor","journal-title":"Commun Med (Lond)"},{"issue":"1","key":"R31","doi-asserted-by":"publisher","DOI":"10.1186\/s12889-025-23338-3","article-title":"Internet-based healthcare services use patterns and barriers among middle-aged and older adults in China: a cross-sectional study","volume":"25","author":"Wang","journal-title":"BMC Public Health"},{"issue":"1","key":"R32","doi-asserted-by":"publisher","DOI":"10.1186\/s12889-021-11200-1","article-title":"The impact of COVID-19 pandemic on the health-seeking behaviour of an Asian population with acute respiratory infections in a densely populated community","volume":"21","author":"Tan","journal-title":"BMC Public Health"}],"container-title":["Journal of Medical Internet Research"],"original-title":[],"language":"en","deposited":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T20:00:26Z","timestamp":1790193626000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jmir.org\/2026\/1\/e96131"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,23]]},"references-count":32,"URL":"https:\/\/doi.org\/10.2196\/96131","relation":{},"ISSN":["1438-8871"],"issn-type":[{"value":"1438-8871","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,23]]},"article-number":"v28i11e96131"}}