Vector quantisation and partitioning of COVID-19 temporal dynamics in the United States

TL;DR

This paper applies advanced time series clustering techniques to COVID-19 case data in the US to identify patterns and inform public health strategies based on temporal dynamics.

Contribution

It introduces a novel combination of soft-DTW k-means and k-shape clustering for analyzing COVID-19 temporal case patterns in the US.

Findings

Identification of distinct temporal clusters of COVID-19 cases

Characterization of typical case trajectory patterns

Potential to predict future infection dynamics

Abstract

The statistical dynamics of a pathogen within a population depend on a range of factors: population density, the effectiveness and investment into social distancing, public policy measures and non-pharmaceutical interventions (NPIs) are only some examples of factors that influence the number of cases over time by state. This paper outlines an analysis of time series vector quantisation and paritioning of COVID-19 cases in the United States, using a soft-DTW (Dynamic Time Warping) k-means clustering and a k-shape based clustering algorithm to identify internally consistent clusters of case counts over time. The identification of characteristic types of time-dependent variations can lead to the identification of patterns within sets of time series. This, in turn, can help discern the future of infectious dynamics in an area and, through identifying the most likely cluster-wise trajectory by calculating the cluster barycenter, inform public health decision-making.