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Bibliographic Details
Main Authors: Fairchild, Megan, Lemoine, Matthew
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2510.15066
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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