https://socictopen.socict.org/files/original/db5e7acc0e1edc90663b771066f2aa03.pdf 13f0da4afc9ab0f7cb74e313daa71138 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index Creator An entity primarily responsible for making the resource Lei Qin, Qiang Sun, Yidan Wang, Kefei Wu, Ming-Chih Chen, Ben-Chang Shia, Szu-Yuan Wu Description An account of the resource Predicting the number of new suspected or confirmed cases of novel coronavirus disease 2019 (COVID-19) is crucial in the prevention and control of the COVID-19 outbreak. Social media search indexes (SMSI) for dry cough, fever, chest distress, coronavirus, and pneumonia were collected from 31 December 2019 to 9 February 2020. The new suspected cases of COVID-19 data were collected from 20 January 2020 to 9 February 2020. We used the lagged series of SMSI to predict new suspected COVID-19 case numbers during this period. To avoid overfitting, five methods, namely subset selection, forward selection, lasso regression, ridge regression, and elastic net, were used to estimate coefficients. We selected the optimal method to predict new suspected COVID-19 case numbers from 20 January 2020 to 9 February 2020. We further validated the optimal method for new confirmed cases of COVID-19 from 31 December 2019 to 17 February 2020. The new suspected COVID-19 case numbers correlated significantly with the lagged series of SMSI. SMSI could be detected 6–9 days earlier than new suspected cases of COVID-19. The optimal method was the subset selection method, which had the lowest estimation error and a moderate number of predictors. The subset selection method also significantly correlated with the new confirmed COVID-19 cases after validation. SMSI findings on lag day 10 were significantly correlated with new confirmed COVID-19 cases. SMSI could be a significant predictor of the number of COVID-19 infections. SMSI could be an effective early predictor, which would enable governments’ health departments to locate potential and high-risk outbreak areas. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource social media, COVID-19, predictor, Outbreak, new case Identifier An unambiguous reference to the resource within a given context DOI: 10.3390/ijerph17072365 Source A related resource from which the described resource is derived International Journal of Environmental Research and Public Health Publisher An entity responsible for making the resource available MDPI AG Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Medicine Language A language of the resource EN