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 COVID-19 pandemic so far has caused huge negative impacts on differentareas all over the world, and the United States (US) is one of the mostaffected countries. In this paper, we use methods from the functional dataanalysis to look into the COVID-19 data in the US. We explore the modes ofvariation of the data through a functional principal component analysis (FPCA),and study the canonical correlation between confirmed and death cases. Inaddition, we run a cluster analysis at the state level so as to investigate therelation between geographical locations and the clustering structure. Lastly,we consider a functional time series model fitted to the cumulative confirmedcases in the US, and make forecasts based on the dynamic FPCA. Both point andinterval forecasts are provided, and the methods for assessing the accuracy ofthe forecasts are also included.

Functional data analysis: An application to COVID-19 data in the United States