Assessing COVID-19 risk with temporal indices and geographically weighted ordinal logistic regression in US counties.

ABSTRACT:

Purpose

Research on the novel coronavirus diseases 2019 (COVID-19) mainly relies on cross-sectional data, but this approach fails to consider the temporal dimension of the pandemic. This study assesses three temporal dimensions of the COVID-19 infection risk in US counties, namely probability of occurrence, duration of the pandemic, and intensity of transmission, and investigate local patterns of the factors associated with these risks.

Methods

Analyzing daily data between January 22 and September 11, 2020, we categorize the contiguous US counties into four risk groups-High-Risk, Moderate-Risk, Mild-Risk, and Low-Risk-and then apply both conventional (i.e., non-spatial) and geographically weighted (i.e., spatial) ordinal logistic regression model to understand the county-lev

SUBMITTER: Chen VY 

PROVIDER: S-EPMC8985941 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature