Explaining the Varying Patterns of COVID-19 Deaths Across the United States: 2-Stage Time Series Clustering Framework

• Main Authors: Fadel M Megahed (Author), L Allison Jones-Farmer (Author), Yinjiao Ma (Author), Steven E Rigdon (Author)
• Format: Book
• Published: JMIR Publications, 2022-07-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2369-2960; 10.2196/32164
• Keywords: Public aspects of medicine; RA1-1270
• Source: JMIR Public Health and Surveillance, Vol 8, Iss 7, p e32164 (2022)

BackgroundSocially 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. ObjectiveOur 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. MethodsWe 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. ResultsFour 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. ConclusionsOur results provide evidence that county-level patterns of COVID-19 deaths are different and can be explained in part by social and political predictors.