Archetypal analysis of COVID-19 in Montana, USA, March 13, 2020 to April 26, 2022

Emily Stone, Sebastian Coombs and Erin Landguth

PLOS ONE, 2024, vol. 19, issue 1, 1-21

Abstract: Infectious disease data can often involve complex spatial patterns intermixed with temporal trends. Archetypal Analysis is a method to mine complex spatio-temporal data, and can be used to discover the dynamics of spatial patterns. The application of Archetypal Analysis to epidemiological data is relatively new, and here we present one of the first applications on COVID-19 data from March 13, 2020 to April 26, 2022, for the counties of Montana, USA. We present three views of the data set decomposed with Archetypal Analysis. First, we evaluate the entire 56 county data set. Second, we use a mutual information calculation to remove counties whose dynamics are mainly independent from the other counties, reducing the set to 17 counties. Finally, we analyze the top ten counties in terms of population size to focus on the dynamics in the large cities in the state. For each data set, we analyze four significant disease outbreaks across Montana. Archetypal Analysis uncovers distinct spatial patterns for each outbreak and demonstrates that each has a unique trajectory across the state.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0283265 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 83265&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0283265

DOI: 10.1371/journal.pone.0283265

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().