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https://github.com/vjcitn/teachcoviddata

R package for getting familiar with COVID-19 data resources and their use
https://github.com/vjcitn/teachcoviddata

covid-19 data-science

Last synced: 2 days ago
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R package for getting familiar with COVID-19 data resources and their use

Host: GitHub
URL: https://github.com/vjcitn/teachcoviddata
Owner: vjcitn
Created: 2022-10-08T10:55:05.000Z (over 2 years ago)
Default Branch: main
Last Pushed: 2023-01-07T14:05:52.000Z (about 2 years ago)
Last Synced: 2024-11-11T12:00:53.500Z (2 months ago)
Topics: covid-19, data-science
Language: R
Homepage: https://vjcitn.github.io/teachCovidData
Size: 20.7 MB
Stars: 0
Watchers: 2
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

README

        # teachCovidData

This R package for getting familiar with COVID-19 data resources and their use was

inspired by work of Dr. Latrice Landry related to the 

[NIH Covid Community Engagement Alliance (CEAL)](https://covid19community.nih.gov/).

Learning objectives

- get comfortable with acquiring and exploring public datasets using R

- look specifically at temporal and geographic variations in impacts of COVID

- produce questions and projects related to COVID effects and responses

A concept map of a full course:

![COVID-19 data science concept map](man/figures/covidcoggle.jpg)

Important topics are not addressed, but comments and pull requests are welcome;

see the [issues tracker](https://github.com/vjcitn/teachCovidData/issues).

It would be very nice to have modules on

- environmental measures of population load, such as [wastewater monitoring results](https://data.cdc.gov/Public-Health-Surveillance/NWSS-Public-SARS-CoV-2-Wastewater-Metric-Data/2ew6-ywp6)

- studies of [host genetics](https://www.covid19hg.org/partners/) of susceptibility to disease and complications

    - example [case-control analysis](https://www.jci.org/articles/view/152386/figure/2)

    - [critical remarks and schema for predictive genomic medicine](https://www.jci.org/articles/view/155011/figure/1)

- mobility metrics related to incidence variation (google's [historical data](https://www.google.com/covid19/mobility/), [example analysis](https://www.science.org/doi/10.1126/science.abb4218))

- types and effects of non-pharmacologic interventions ([CDC link](https://www.cdc.gov/nonpharmaceutical-interventions/index.html))

- [vaccination mandates](https://data.cdc.gov/Policy-Surveillance/State-Level-Vaccine-Mandates-All/kw6u-z8u2)

- incidence of [vaccination-related adverse events](https://www.cdc.gov/coronavirus/2019-ncov/vaccines/safety/adverse-events.html)

- elaborations of elements on data publication and education in international contexts ([2021 overview](https://royalsocietypublishing.org/doi/pdf/10.1098/rsta.2021.0127), speculations on [data from China](https://www.science.org/content/article/china-flying-blind-pandemic-rages), [WHO overview](https://www.who.int/emergencies/diseases/novel-coronavirus-2019))

See the "get started" link on the vjcitn.github.io/teachCovidData site for current details.