Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index
1
School of Statistics, University of International Business and Economics, Beijing 100029, China
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Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, New Taipei City 242, Taiwan
3
Research Center of Big Data, College of management, Taipei Medical University, Taipei 110, Taiwan
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College of Management, Taipei Medical University, Taipei 110, Taiwan
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Executive Master Program of Business Administration in Biotechnology, College of management, Taipei Medical University, Taipei 110, Taiwan
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Department of Food Nutrition and Health Biotechnology, College of Medical and Health Science, Asia University, Taichung 41354, Taiwan
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Division of Radiation Oncology, Lo-Hsu Medical Foundation, Lotung Poh-Ai Hospital, Yilan 265, Taiwan
8
Big Data Center, Lo-Hsu Medical Foundation, Lotung Poh-Ai Hospital, Yilan 265, Taiwan
9
Department of Healthcare Administration, College of Medical and Health Science, Asia University, Taichung 41354, Taiwan
10
School of Dentistry, College of Oral Medicine, Taipei Medical University, Taipei 110, Taiwan
*
Author to whom correspondence should be addressed.
†
These authors have contributed equally to this study (joint primary authors).
Int. J. Environ. Res. Public Health 2020, 17(7), 2365; https://doi.org/10.3390/ijerph17072365
Received: 28 February 2020
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Revised: 29 March 2020
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Accepted: 30 March 2020
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Published: 31 March 2020
(This article belongs to the Collection Outbreak of a Novel Coronavirus: A Global Health Threat)
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.
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Keywords:
social media; COVID-19; predictor; outbreak; new case
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MDPI and ACS Style
Qin, L.; Sun, Q.; Wang, Y.; Wu, K.-F.; Chen, M.; Shia, B.-C.; Wu, S.-Y. Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index. Int. J. Environ. Res. Public Health 2020, 17, 2365. https://doi.org/10.3390/ijerph17072365
AMA Style
Qin L, Sun Q, Wang Y, Wu K-F, Chen M, Shia B-C, Wu S-Y. Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index. International Journal of Environmental Research and Public Health. 2020; 17(7):2365. https://doi.org/10.3390/ijerph17072365
Chicago/Turabian StyleQin, Lei, Qiang Sun, Yidan Wang, Ke-Fei Wu, Mingchih Chen, Ben-Chang Shia, and Szu-Yuan Wu. 2020. "Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index" International Journal of Environmental Research and Public Health 17, no. 7: 2365. https://doi.org/10.3390/ijerph17072365
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