JMIR Publications

ABSTRACT

Background:

Socially vulnerable communities are at an increased risk for adverse health outcomes during a pandemic. While this association has been established for H1N1, MERS and COVID-19 outbreaks, understanding the factors influencing the outbreak pattern for different communities remains limited.

Objective:

Our three objectives are to determine how many distinct clusters of time series there are for COVID deaths in the 3,108 counties in the contiguous US, how the clusters are geographically distributed, and what factors influence the probability of cluster membership.

Methods:

We propose a two-stage data analytic framework that can account for different levels of temporal aggregation for the pandemic outcomes and community-level predictors. Specifically, we use time-series clustering to identify clusters with similar outcome patterns for the 3,108 contiguous U.S. counties. Multinomial logistic regression is used to explain the relationship between community-level predictors and cluster assignment. We analyzed county-level confirmed COVID-19 deaths from Sunday March 1, 2020 to Saturday February 27, 2021.

Results:

Four distinct patterns of deaths were observed across the contiguous U.S. The multinomial regression model correctly classified 61.25\% of the counties’ outbreak patterns/clusters.

Conclusions:

Our results provide evidence that county-level patterns of COVID-19 deaths are different, and can be explained in part by social and political predictors.