Event Title

Identifying Significant Predictors of COVID-19 Mortality Rate in the United States

Co-Author(s)

Jon Garber, Jocelyn Black, Noah Prime, Will Murray

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

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

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May 18th, 12:00 AM May 22nd, 12:00 AM

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.