Dynamical clustering of U.S. states reveals four distinct infection patterns that predict SARS-CoV-2 pandemic behavior
Joseph L. Natale, Varun Viswanath, Oscar Trujillo Acevedo, Sophia, P\'erez Giottonini, Sandy Ihuiyan Romero Hern\'andez, Diana G. Cruz Mill\'an,, A. Montserrat Palacios-Puga, Ammar Mandvi, Brian M. Khan, Martin Lilik, Jay, Park, Benjamin L. Smarr

TL;DR
This study identifies four distinct infection patterns across U.S. states during COVID-19, revealing how these clusters relate to geographical, intervention, and vaccination factors, and predicting future infection trends.
Contribution
The paper introduces a robust clustering method that summarizes COVID-19 wave patterns across U.S. states and links these patterns to vaccination and intervention variables.
Findings
Four infection clusters correlate with geographic and intervention factors.
Vaccination rates negatively correlate with case variability within clusters.
Clusters predict the timing and magnitude of the fifth COVID-19 wave.
Abstract
The SARS-CoV-2 pandemic has so far unfolded diversely across the fifty United States of America, reflected both in different time progressions of infection "waves" and in magnitudes of local infection rates. Despite a marked diversity of presentations, most U.S. states experienced their single greatest surge in daily new cases during the transition from Fall 2020 to Winter 2021. Popular media also cite additional similarities between states -- often despite disparities in governmental policies, reported mask-wearing compliance rates, and vaccination percentages. Here, we identify a set of robust, low-dimensional clusters that 1) summarize the timings and relative heights of four historical COVID-19 "wave opportunities" accessible to all 50 U.S. states, 2) correlate with geographical and intervention patterns associated with those groups of states they encompass, and 3) predict aspects…
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Taxonomy
TopicsCOVID-19 epidemiological studies · Data-Driven Disease Surveillance · Influenza Virus Research Studies
