Complex Network and Topological Data Analysis Methods for County Level COVID-19 Vaccine Acceptance Analysis in the United States
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View abstract on PubMed
Summary
This summary is machine-generated.Community vaccine acceptance in the US is influenced by sociodemographic factors. Network analysis reveals that education, income, and region impact COVID-19 vaccination rates, with distinct patterns in rural versus urban areas.
Area Of Science
- Public Health
- Network Science
- Sociology
Background
- Vaccination against SARS-CoV-2 variants is crucial.
- Despite widespread availability, vaccine hesitancy persists in the US.
- Misinformation poses a significant threat to vaccination program success.
Purpose Of The Study
- To investigate community COVID-19 vaccine acceptance at the county level in the US.
- To identify sociodemographic factors influencing vaccination patterns using a network-based approach.
- To uncover macrolevel community trends in vaccine acceptance via topological data analysis.
Main Methods
- Utilized an exponential random graph model to analyze county-level vaccination data from the CDC.
- Employed topological data analysis (TDA) for network-based community clustering.
- Examined sociodemographic variables including education, income, and geographic region.
Main Results
- Higher education levels and household income were significantly associated with increased vaccine acceptance.
- US Census regions demonstrated distinct patterns in COVID-19 vaccine acceptance.
- Network clustering revealed significant differences in vaccine acceptance between rural and urban areas.
Conclusions
- Sociodemographic factors play a critical role in shaping community COVID-19 vaccine acceptance.
- Geographic and socioeconomic characteristics influence vaccination trends across the United States.
- Understanding these factors is essential for targeted public health interventions to improve vaccine uptake.
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