Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index
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 Decembe...
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| Veröffentlicht in: | International journal of environmental research and public health 2020-03, Vol.17 (7), p.2365 |
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| container_title | International journal of environmental research and public health |
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| creator | Qin, Lei Sun, Qiang Wang, Yidan Wu, Ke-Fei Chen, Mingchih Shia, Ben-Chang Wu, Szu-Yuan |
| description | 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. |
| doi_str_mv | 10.3390/ijerph17072365 |
| format | Article |
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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.</description><identifier>ISSN: 1660-4601</identifier><identifier>ISSN: 1661-7827</identifier><identifier>EISSN: 1660-4601</identifier><identifier>DOI: 10.3390/ijerph17072365</identifier><identifier>PMID: 32244425</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Betacoronavirus ; Computer Simulation ; Coronaviridae ; Coronavirus ; Coronavirus Infections - complications ; Coronavirus Infections - epidemiology ; Coronavirus Infections - prevention & control ; Coronaviruses ; Correlation ; Cough ; Cough - epidemiology ; Cough - etiology ; COVID-19 ; Data Mining ; Digital media ; Disease Outbreaks - prevention & control ; Dyspnea - epidemiology ; Dyspnea - etiology ; Epidemics ; Fever ; Fever - epidemiology ; Fever - etiology ; Forecasting ; Humans ; Outbreaks ; Pandemics - prevention & control ; Pneumonia - epidemiology ; Pneumonia - etiology ; Pneumonia, Viral - complications ; Pneumonia, Viral - epidemiology ; Pneumonia, Viral - prevention & control ; Risk Assessment ; SARS-CoV-2 ; Search Engine ; Social Media - statistics & numerical data ; Social networks</subject><ispartof>International journal of environmental research and public health, 2020-03, Vol.17 (7), p.2365</ispartof><rights>2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). 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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. 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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.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>32244425</pmid><doi>10.3390/ijerph17072365</doi><orcidid>https://orcid.org/0000-0001-5637-558X</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Betacoronavirus Computer Simulation Coronaviridae Coronavirus Coronavirus Infections - complications Coronavirus Infections - epidemiology Coronavirus Infections - prevention & control Coronaviruses Correlation Cough Cough - epidemiology Cough - etiology COVID-19 Data Mining Digital media Disease Outbreaks - prevention & control Dyspnea - epidemiology Dyspnea - etiology Epidemics Fever Fever - epidemiology Fever - etiology Forecasting Humans Outbreaks Pandemics - prevention & control Pneumonia - epidemiology Pneumonia - etiology Pneumonia, Viral - complications Pneumonia, Viral - epidemiology Pneumonia, Viral - prevention & control Risk Assessment SARS-CoV-2 Search Engine Social Media - statistics & numerical data Social networks |
| title | Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index |
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