15: Clustering County-Level COVID-19 Death Trends Using INAR Models

Music City Center 

This project applies the integer-valued autoregressive (INAR) clustering method by Roick et al. (2021) to analyze daily COVID-19 deaths in US counties. Unlike national and state data, county-level death counts are often low (0–5 daily deaths), making traditional time series clustering methods unreliable. We test whether INAR-based mixtures, designed for autocorrelated integer-valued data, can better group counties with similar mortality patterns. Using CDC data, we cluster counties based on daily death trajectories. We then compare clusters to demographic factors (e.g., vaccination rates, population density), identifying shared trends. For example, rural counties with low healthcare access form distinct clusters compared to urban areas. Preliminary results suggest that INAR models outperform distance-based methods (e.g., DTW) for low-count data. This approach highlights the importance of tailored statistical methods for discrete health data, common in disease tracking and local policy evaluations. The poster will present visualizations of cluster patterns, model diagnostics, and insights into how INAR methods can address challenges in analyzing sparse county-level data.

Time series

INAR models

COVID-19 mortality

county-level clustering

low-count data 

Section on Statistics in Epidemiology