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

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

doi:10.1016/j.sste.2021.100437

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 journal › Article › peer-review

1 Scopus citations

Abstract

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 language	English
Article number	100437
Journal	Spatial and Spatio-temporal Epidemiology
Volume	38
DOIs	https://doi.org/10.1016/j.sste.2021.100437
State	Published - Aug 2021

Keywords

Archetypal Analysis
Seasonal Flu
Spatial-temporal infectious disease spread

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.sste.2021.100437

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title = "Decoding influenza outbreaks in a rural region of the USA with archetypal analysis",

abstract = "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.",

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AU - Mokhtari, Elham Bayat

AU - Landguth, Erin L.

AU - Anderson, Stacey

AU - Stone, Emily

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AB - 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.

KW - Archetypal Analysis

KW - Seasonal Flu

KW - Spatial-temporal infectious disease spread

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JO - Spatial and Spatio-temporal Epidemiology

JF - Spatial and Spatio-temporal Epidemiology

M1 - 100437

ER -

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

Abstract

Keywords

UN SDGs

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