Topological Data Analysis of Mortality Patterns During the COVID-19 Pandemic
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911216305176576 |
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| author | Fairchild, Megan Lemoine, Matthew |
| author_facet | Fairchild, Megan Lemoine, Matthew |
| contents | Topological Data Analysis is a relatively new field of study that uses topological invariants to study the shape of data. We analyze a dataset provided by the Centers for Disease Control and Prevention (CDC) using persistent homology and MAPPER. This dataset tracks mortality week-to-week from January 2020 to September 2023 in the United States during the COVID-19 pandemic. We examine the dataset as a whole and break the United States into geographic regions to analyze the overall shape of the data. Then, to explain this shape, we discuss events around the time of the pandemic and how they contribute to the observed patterns. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_15066 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Topological Data Analysis of Mortality Patterns During the COVID-19 Pandemic Fairchild, Megan Lemoine, Matthew Algebraic Topology Topological Data Analysis is a relatively new field of study that uses topological invariants to study the shape of data. We analyze a dataset provided by the Centers for Disease Control and Prevention (CDC) using persistent homology and MAPPER. This dataset tracks mortality week-to-week from January 2020 to September 2023 in the United States during the COVID-19 pandemic. We examine the dataset as a whole and break the United States into geographic regions to analyze the overall shape of the data. Then, to explain this shape, we discuss events around the time of the pandemic and how they contribute to the observed patterns. |
| title | Topological Data Analysis of Mortality Patterns During the COVID-19 Pandemic |
| topic | Algebraic Topology |
| url | https://arxiv.org/abs/2510.15066 |