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

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.

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

Given the potential consequences of infectious diseases, it is important to understand how broad scale incidence variability influences the probability of localized outbreaks. Often, these infectious disease data can involve complex spatial patterns intermixed with temporal trends. Archetypal Analysis is a method to mine complex spatiotemporal epidemiological data, and can be used to discover the dynamics of spatial patterns. The application of Archetypal Analysis to epistemological data is relatively new, and here we present one of the first applications using COVID-19 data from March 13, 2020 to April 26, 2022, in the counties of Montana, USA. We present three views of the data set with Archetypal Analysis. First, we evaluate the entire 56 county data set. Second, we compute mutual information of the 56 counties' time series to remove counties whose dynamics are mainly independent from most of the other counties. We choose the top 17 counties ranked in terms of increasing total mutual information. Finally, to compare how population size might influence results, we conducted an analysis with 10 of the largest counties. Using the Archetypal Analysis results, we analyze the disease outbreaks across Montana, comparing and contrasting the three different cases and showing how certain counties can be found in distinct sets of archetypes. Using the reconstruction time series, we show how each outbreak had a unique trajectory across the state in terms of the archetypes.