Topological data analysis model for the spread of the coronavirus.
PLoS One
; 16(8): e0255584, 2021.
Article
in English
| MEDLINE | ID: covidwho-1341505
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
ABSTRACT
We apply topological data analysis, specifically the Mapper algorithm, to the U.S. COVID-19 data. The resulting Mapper graphs provide visualizations of the pandemic that are more complete than those supplied by other, more standard methods. They allow for easy comparisons of the features of the pandemic across time and space and encode a variety of geometric features of the data cloud created from geographic information, time progression, and the number of COVID-19 cases. The Mapper graphs reflect the development of the pandemic across all of the U.S. and capture the growth rates as well as the regional prominence of hot-spots.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Algorithms
/
Models, Statistical
/
COVID-19
Type of study:
Observational study
Limits:
Humans
Country/Region as subject:
North America
Language:
English
Journal:
PLoS One
Journal subject:
Science
/
Medicine
Year:
2021
Document Type:
Article
Affiliation country:
Journal.pone.0255584
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