Pseudo-likelihood based logistic regression for estimating COVID-19 infection and case fatality rates by gender, race, and age in California

Pseudo-likelihood based logistic regression for estimating COVID-19 infection and case fatality rates by gender, race, and age in California

In emerging epidemics, early estimates of key epidemiological characteristics of the disease are critical for guiding public policy. In particular, identifying high-risk population subgroups aids policymakers and health officials in combating the epidemic. This has been challenging during the corona...

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Bibliographic Details
Main Authors: Di Xiong, Lu Zhang, Gregory L. Watson, Phillip Sundin, Teresa Bufford, Joseph A. Zoller, John Shamshoian, Marc A. Suchard, Christina M. Ramirez
Format: Article
Language:English
Published: Elsevier 2020-12-01
Series:Epidemics
Subjects:
COVID-19
Infection rate
Case fatality rate
California Health Interview Survey
Logistic regression
Online Access:http://www.sciencedirect.com/science/article/pii/S1755436520300396

Internet

http://www.sciencedirect.com/science/article/pii/S1755436520300396

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