TY  - JOUR
AU  - Megahed, Fadel M
AU  - Jones-Farmer, L Allison
AU  - Ma, Yinjiao
AU  - Rigdon, Steven E
PY  - 2022
DA  - 2022/7/19
TI  - Explaining the Varying Patterns of COVID-19 Deaths Across the United States: 2-Stage Time Series Clustering Framework
JO  - JMIR Public Health Surveill
SP  - e32164
VL  - 8
IS  - 7
KW  - explanatory modeling
KW  - multinomial regression
KW  - SARS-CoV-2
KW  - COVID-19
KW  - socioeconomic analyses
KW  - time series analysis
AB  - Background: Socially vulnerable communities are at increased risk for adverse health outcomes during a pandemic. Although this association has been established for H1N1, Middle East respiratory syndrome (MERS), and COVID-19 outbreaks, understanding the factors influencing the outbreak pattern for different communities remains limited. Objective: Our 3 objectives are to determine how many distinct clusters of time series there are for COVID-19 deaths in 3108 contiguous counties in the United States, how the clusters are geographically distributed, and what factors influence the probability of cluster membership. Methods: We proposed a 2-stage data analytic framework that can account for different levels of temporal aggregation for the pandemic outcomes and community-level predictors. Specifically, we used time-series clustering to identify clusters with similar outcome patterns for the 3108 contiguous US counties. Multinomial logistic regression was 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 US counties. The multinomial regression model correctly classified 1904 (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. 
SN  - 2369-2960
UR  - https://publichealth.jmir.org/2022/7/e32164
UR  - https://doi.org/10.2196/32164
UR  - http://www.ncbi.nlm.nih.gov/pubmed/35476722
DO  - 10.2196/32164
ID  - info:doi/10.2196/32164
ER  -