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Modeling COVID-19 incidence with Google Trends
Lateef Babatunde Amusa
, Hossana Twinomurinzi
, Chinedu Wilfred Okonkwo
University of Johannesburg
Research output
:
Contribution to journal
›
Article
›
peer-review
23
Citations (Scopus)
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Keyphrases
COVID-19
100%
Google Trends
100%
Coronavirus Disease (COVID)
50%
Root Mean Square Error
37%
ARIMA Model
37%
Nigerians
25%
Prediction Accuracy
25%
Search Terms
25%
Expanded Model
25%
Smell Loss
25%
Diebold-Mariano Test
25%
Mean Average Error
25%
Mean Absolute Error
12%
Statistical Significance
12%
Order of Magnitude
12%
Performance Prediction
12%
Regressor
12%
Data-centric
12%
Predictive Ability
12%
Fever
12%
Model Forecast
12%
Training Set
12%
Traditional Data
12%
Disease Model
12%
Incidence Data
12%
Corrected Akaike Information Criterion
12%
Taste Loss
12%
Modeling Infectious Diseases
12%
Contemporaneous Correlation
12%
Suitable Method
12%
Data Augment
12%
Internet Search Data
12%
Medicine and Dentistry
COVID-19
100%
Infection
14%
Infectious Disease
14%
Akaike Information Criterion
14%
Trophoblastic Disease
14%