ArticleAmerican journal of infection control2021
Visualizing and assessing US county-level COVID19 vulnerability.
Article in American journal of infection control, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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
4 citing papers in PubMed, 9 citations in OpenAlex.
- Preparing for the next outbreak: A review of indices measuring outbreak preparedness, vulnerability, and resilience.Preventive medicine reports · 2023Review
- The Impact of US County-Level Factors on COVID-19 Morbidity and Mortality.Journal of urban health : bulletin of the New York Academy of Medicine · 2022Article
- Social vulnerability amplifies the disparate impact of mobility on COVID-19 transmissibility across the United States.Humanities & social sciences communications · 2022Article
- Using machine learning to develop a novel COVID-19 Vulnerability Index (C19VI).The Science of the total environment · 2021Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLike most of the world, the United States' public health and economy are impacted by the COVID19 pandemic. However, discrete pandemic effects may not be fully realized on the macro-scale. With this perspective, our goal is to visualize spread of the pandemic and measure county-level features which may portend vulnerability.
methodsWe accessed the New York Times GitHub repository COVID19 data and 2018 United States Census data for all United States Counties. The disparate datasets were merged and filtered to allow for visualization and assessments about case fatality rate (CFR%) and associated demographic, ethnic and economic features.
resultsOur results suggest that county-level COVID19 fatality rates are related to advanced population age (P < .001) and less diversity as evidenced by higher proportion of Caucasians in High CFR% counties (P < .001). Also, lower CFR% counties had a greater proportion of the population reporting has having 2 or more races (P < .001). We noted no significant differences between High and Low CFR% counties with respect to mean income or poverty rate.
conclusionsUnique COVID19 impacts are realized at the county level. Use of public datasets, data science skills and information visualization can yield helpful insights to drive understanding about community-level vulnerability.
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Registered trials
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.