English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

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