Decoding influenza outbreaks in a rural region of the USA with archetypal analysis

Elham Bayat Mokhtari, Erin L. Landguth, Stacey Anderson, Emily Stone

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


We present the first application of archetypal analysis for influenza data from 2010 to 2018 in Montana, USA. Using archetypes, we decompose the data into spatial and temporal components, allowing for a more informed analysis of spatial-temporal dynamic trends during an influenza season. Initially, we reduce the dimension of the set of counties by using a mutual information measure on the influenza time series to create a smaller, maximal mutual information network. Archetypal analysis then describes the relationship between influenza cases across counties and regions in Montana. Finally, we discuss the potential implications this analysis can have for infectious disease modeling, particularly where data is sparse and limited.

Original languageEnglish
Article number100437
JournalSpatial and Spatio-temporal Epidemiology
StatePublished - Aug 2021


  • Archetypal Analysis
  • Seasonal Flu
  • Spatial-temporal infectious disease spread


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