IJERPH, Vol. 17, Pages 2365: Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index

IJERPH, Vol. 17, Pages 2365: Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index International Journal of Environmental Research and Public Health doi: 10.3390/ijerph17072365 Authors: Qin Sun Wang Wu Chen Shia Wu 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 ...
Source: International Journal of Environmental Research and Public Health - Category: Environmental Health Authors: Tags: Article Source Type: research

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Source: Indian Journal of Medical Microbiology - Category: Microbiology Authors: Source Type: research
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