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Assessing Internet Search Models in Predicting Daily New COVID-19 Cases and Deaths in South Korea
Atina Husnayain
*
,
Emily Chia Yu Su
*
此作品的通信作者
生物醫學資訊研究所
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Keyphrases
Search Data
100%
Search Model
100%
Root Mean Square Error
100%
Magnitude Error
100%
Web Search
100%
South Korea
100%
State-space Model
100%
Error Magnitude
100%
Peak Day
100%
COVID-19 Cases
100%
COVID-19 Mortality
100%
Peak Magnitude
100%
Loss Function
50%
Principal Coordinate Analysis (PCoA)
50%
Linear Regression Model
50%
Generalized Linear Model
50%
Model-driven Development
50%
Error Value
50%
Composite Features
50%
Trend Prediction
50%
COVID-19 Trends
50%
Engineering
Error Magnitude
100%
Root-Mean-Squared Error
100%
Peak Magnitude
100%
Loss Function
50%
Principal Components
50%
Component Analysis
50%
Error Value
50%
Psychology
Generalized Linear Model
100%
Linear Regression Model
100%
Principal Component Analysis
100%
Medicine and Dentistry
COVID-19
100%
Linear Regression Analysis
50%
Principal Component Analysis
50%
Earth and Planetary Sciences
South Korea
100%
Principal Component Analysis
100%