Li, JingweiJingweiLiHuang, WeiWeiHuangCHOON LING SIAChen, ZhuoZhuoChenWu, TailaiTailaiWuWang, QingnanQingnanWang2023-03-162023-03-162022-06-162369-2960https://www.scopus.com/record/display.uri?eid=2-s2.0-85132455883&origin=resultslist&sort=plf-fhttps://scholars.lib.ntu.edu.tw/handle/123456789/629336The SARS-COV-2 virus and its variants pose extraordinary challenges for public health worldwide. Timely and accurate forecasting of the COVID-19 epidemic is key to sustaining interventions and policies and efficient resource allocation. Internet-based data sources have shown great potential to supplement traditional infectious disease surveillance, and the combination of different Internet-based data sources has shown greater power to enhance epidemic forecasting accuracy than using a single Internet-based data source. However, existing methods incorporating multiple Internet-based data sources only used real-time data from these sources as exogenous inputs but did not take all the historical data into account. Moreover, the predictive power of different Internet-based data sources in providing early warning for COVID-19 outbreaks has not been fully explored.enCOVID 19; SARS-CoV-2; autoregression model; disease surveillance; epidemic forecasting; infectious disease epidemiology; online news; search query; social medialjournal article10.2196/35266355079212-s2.0-85132455883https://api.elsevier.com/content/abstract/scopus_id/85132455883