Evidence map›Paper›PMID 38170725›Full record

ArticlePloS one2024

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

Emily Stone, Sebastian Coombs, Erin Landguth

Abstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Emily StoneDept. of Mathematical Science, University of Montana, Missoula, MT, United States of America.ORCID 0000-0003-4979-895X
Sebastian CoombsDept. of Mathematical Science, University of Montana, Missoula, MT, United States of America.
Erin LandguthCenter for Population Health Research, School of Public and Community Health Sciences, University of Montana, Missoula, MT, United States of America.

Funding

Wildfires and Cardiovascular Health: Evaluating Associations and Intervention StrategiesP20GM130418 · NIGMS · UNIVERSITY OF MONTANA · PI Curtis William Noonan · 2020 to 2026
$17.9M
NIGMS NIH HHS P20 GM130418
6 · The paper itself

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.

Indexed as

COVID-19OrthopteraAnimalsCitiesDisease OutbreaksHumansMontanaPopulation Density

Identifiers

PMID38170725
PMCPMC10763954

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.