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WeightNameValue
1000 Titel
  • Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index
1000 Autor/in
  1. Qin,Lei |
  2. Sun, Qiang |
  3. Wang, Yidan |
  4. Wu, Ke-Fei |
  5. Chen, Mingchih |
  6. Shia, Ben-Chang |
  7. Wu, Szu-Yuan |
1000 Erscheinungsjahr 2020
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2020-03-31
1000 Erschienen in
1000 Quellenangabe
  • 17(7):2365
1000 Copyrightjahr
  • 2020
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.3390/ijerph17072365 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • 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.
1000 Sacherschließung
gnd 1206347392 COVID-19
lokal predictor
lokal social media
lokal new case
lokal outbreak
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/uri/UWluLExlaQ==|https://frl.publisso.de/adhoc/uri/U3VuLCBRaWFuZw==|https://frl.publisso.de/adhoc/uri/V2FuZywgWWlkYW4=|https://frl.publisso.de/adhoc/uri/V3UsIEtlLUZlaQ==|https://frl.publisso.de/adhoc/uri/Q2hlbiwgTWluZ2NoaWg=|https://frl.publisso.de/adhoc/uri/U2hpYSwgQmVuLUNoYW5n|https://orcid.org/0000-0001-5637-558X
1000 Label
1000 Förderer
  1. Lo-Hsu Medical Foundation |
  2. University of International Business and Economics Huiyuan |
  3. Fundamental Research Funds for the central Universities |
1000 Fördernummer
  1. 10908 and 10909
  2. 17YQ15
  3. CXTD10-10
1000 Förderprogramm
  1. -
  2. outstanding young scholars research funding
  3. -
1000 Dateien
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer Lo-Hsu Medical Foundation |
    1000 Förderprogramm -
    1000 Fördernummer 10908 and 10909
  2. 1000 joinedFunding-child
    1000 Förderer University of International Business and Economics Huiyuan |
    1000 Förderprogramm outstanding young scholars research funding
    1000 Fördernummer 17YQ15
  3. 1000 joinedFunding-child
    1000 Förderer Fundamental Research Funds for the central Universities |
    1000 Förderprogramm -
    1000 Fördernummer CXTD10-10
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6420339.rdf
1000 Erstellt am 2020-04-22T12:17:54.745+0200
1000 Erstellt von 21
1000 beschreibt frl:6420339
1000 Bearbeitet von 21
1000 Zuletzt bearbeitet Wed Apr 22 12:25:53 CEST 2020
1000 Objekt bearb. Wed Apr 22 12:25:42 CEST 2020
1000 Vgl. frl:6420339
1000 Oai Id
  1. oai:frl.publisso.de:frl:6420339 |
1000 Sichtbarkeit Metadaten public
1000 Sichtbarkeit Daten public
1000 Gegenstand von

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