ArticlePloS one2021
Topological data analysis model for the spread of the coronavirus.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Topological analysis of COVID-19 wave patterns and policy responses in Europe.Frontiers in public health · 2025Article
- Nonlinear time series analysis of state-wise COVID-19 in Malaysia using wavelet and persistent homology.Scientific reports · 2024Article
- A topological data analysis-based method for gait signals with an application to the study of multiple sclerosis.PloS one · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We apply topological data analysis, specifically the Mapper algorithm, to the U.S. COVID-19 data. The resulting Mapper graphs provide visualizations of the pandemic that are more complete than those supplied by other, more standard methods. They allow for easy comparisons of the features of the pandemic across time and space and encode a variety of geometric features of the data cloud created from geographic information, time progression, and the number of COVID-19 cases. The Mapper graphs reflect the development of the pandemic across all of the U.S. and capture the growth rates as well as the regional prominence of hot-spots.
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Registered trials
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