About: Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index
Creator	Sun, Qiang Qin, Lei Wang, Yidan Chen, Mingchih Shia, Ben-Chang Wu, Ke-Fei Wu, Szu-Yuan
Source	Medline; PMC
abstract	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.
has issue date	2020-03-31(xsd:dateTime)
bibo:doi	10.3390/ijerph17072365
bibo:pmid	32244425
has license	cc-by
sha1sum (hex)	92aaba948157ce4fc8ea81f66b3ce3de731809e9
schema:url	https://doi.org/10.3390/ijerph17072365
resource representing a document's title	Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index
has PubMed Central identifier	PMC7177617
has PubMed identifier	32244425
schema:publication	Int J Environ Res Public Health
resource representing a document's body	covid:92aaba948157ce4fc8ea81f66b3ce3de731809e9#body_text
is schema:about of	named entity 'CHEST' named entity 'coronavirus disease 2019' named entity 'COVID-19 outbreak' named entity '2019 Novel Coronavirus' named entity 'fever' named entity 'fever' named entity 'disease surveillance' named entity 'SMSI' named entity 'intra-class correlation coefficient' named entity 'urinary incontinence' named entity 'nose and mouth' named entity 'COVID' named entity 'current situation' named entity 'correlation coefficients' named entity 'least-squares method' named entity 'standard precautions' named entity 'high-risk' named entity 'COVID' named entity 'infection' named entity 'COVID' named entity 'time series' named entity 'SMSI' named entity 'pneumonia' named entity 'dizziness' named entity 'sterilization' named entity 'China' named entity 'error term' named entity 'statistically significant' named entity 'urinating' named entity 'SMSI' named entity 'COVID' named entity 'infectious disease' named entity 'COVID' named entity 'positively correlated' named entity 'SMSI' named entity 'NHC' named entity 'keyword search' named entity 'SMSI' named entity 'SMSI' named entity '2000 in China' named entity 'administrative controls' named entity 'COVID-19' named entity 'People's Republic of China' named entity 'COVID' named entity 'early warning systems' named entity 'SMSI' named entity '2019-nCoV' named entity 'SMSI' named entity 'true series' named entity 'COVID' named entity 'incubation period' named entity 'RMSE' named entity 'pneumonia' named entity 'urinary incontinence' named entity 'public policies' named entity 'COVID' named entity 'health policies' named entity 'Baidu' named entity 'SMSI' named entity 'COVID-19' named entity 'China' named entity 'Ridge regression' named entity 'COVID' named entity 'infection' named entity 'COVID' named entity 'viral pneumonia' named entity 'Iran' named entity 'public health policies' named entity 'hand hygiene' named entity 'COVID-19' named entity 'crowded places' named entity 'COVID' named entity 'coronavirus' named entity 'wash their hands' named entity 'MAE'

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