The Similarities and Distances of Growth Rates Related to COVID-19 Between Different Countries Based on Spectral Analysis

Copyright © 2021 Chen..

The COVID-19 pandemic has taken more than 1.78 million of lives across the globe. Identifying the underlying evolutive patterns between different countries would help us single out the mutated paths and behavior of this virus. I devise an orthonormal basis which would serve as the features to relate the evolution of one country's cases and deaths to others another's via coefficients from the inner product. Then I rank the coefficients measured by the inner product via the featured frequencies. The distances between these ranked vectors are evaluated by Manhattan metric. Afterwards, I associate each country with its nearest neighbor which shares the evolutive pattern via the distance matrix. Our research shows such patterns is are not random at all, i.e., the underlying pattern could be contributed to by some factors. In the end, I perform the typical cosine similarity on the time-series data. The comparison shows our mechanism differs from the typical one, but is also related to each it in some way. These findings reveal the underlying interaction between countries with respect to cases and deaths of COVID-19.

Medienart:

E-Artikel

Erscheinungsjahr:

2021

Erschienen:

2021

Enthalten in:

Zur Gesamtaufnahme - volume:9

Enthalten in:

Frontiers in public health - 9(2021) vom: 20., Seite 695141

Sprache:

Englisch

Beteiligte Personen:

Chen, Ray-Ming [VerfasserIn]

Links:

Volltext

Themen:

COVID-19
Growth rate
Journal Article
Manhattan metric
Research Support, Non-U.S. Gov't
Similarity measures
Spectral analysis

Anmerkungen:

Date Completed 13.10.2021

Date Revised 13.10.2021

published: Electronic-eCollection

Citation Status MEDLINE

doi:

10.3389/fpubh.2021.695141

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM331725614