Identifying Significant Predictors of COVID-19 Mortality Rate in the United States
Research Mentor(s)
Noguchi, Kimihiro
Description
Identifying useful predictors of COVID-19 mortality rate is of critical importance to fight the ongoing pandemic. Based on the Lasso regression and linear discriminant analysis (LDA), hospitalization rate and incident rate seem to be more significant as predictors of COVID-19 mortality rate than latitude, longitude, and testing rate. We further discuss possible causes and implications of the results above by analyzing associations between testing rate, incident rate, and mortality rate.
Document Type
Event
Start Date
18-5-2020 12:00 AM
End Date
22-5-2020 12:00 AM
Department
Mathematics
Genre/Form
student projects, posters
Subjects – Topical (LCSH)
COVID-19 Pandemic, 2020-; COVID-19 (Disease)--United States--Forecasting; COVID-19 (Disease)--Mortality--United States
Geographic Coverage
United States
Type
Image
Rights
Copying of this document in whole or in part is allowable only for scholarly purposes. It is understood, however, that any copying or publication of this document for commercial purposes, or for financial gain, shall not be allowed without the author’s written permission.
Language
English
Format
application/pdf
Identifying Significant Predictors of COVID-19 Mortality Rate in the United States
Identifying useful predictors of COVID-19 mortality rate is of critical importance to fight the ongoing pandemic. Based on the Lasso regression and linear discriminant analysis (LDA), hospitalization rate and incident rate seem to be more significant as predictors of COVID-19 mortality rate than latitude, longitude, and testing rate. We further discuss possible causes and implications of the results above by analyzing associations between testing rate, incident rate, and mortality rate.