TY - JOUR
T1 - Archetypal analysis of COVID-19 in Montana, USA, March 13, 2020 to April 26, 2022
AU - Stone, Emily
AU - Coombs, Sebastian
AU - Landguth, Erin
N1 - Copyright: © 2024 Stone et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2024/1/3
Y1 - 2024/1/3
N2 - 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.
AB - 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.
KW - Animals
KW - COVID-19/epidemiology
KW - Cities
KW - Disease Outbreaks
KW - Humans
KW - Montana/epidemiology
KW - Orthoptera
KW - Population Density
UR - http://www.scopus.com/inward/record.url?scp=85181626862&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/707eb841-0731-3cef-88e9-a7e5e80f1d40/
U2 - 10.1371/journal.pone.0283265
DO - 10.1371/journal.pone.0283265
M3 - Article
C2 - 38170725
AN - SCOPUS:85181626862
SN - 1932-6203
VL - 19
SP - e0283265
JO - PLoS ONE
JF - PLoS ONE
IS - 1
M1 - e0283265
ER -
