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).
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
Important topics are not addressed, but comments and pull requests are welcome; see the issues tracker. It would be very nice to have modules on
- environmental measures of population load, such as wastewater monitoring results
- studies of host genetics of susceptibility to disease and complications
- mobility metrics related to incidence variation (google’s historical data, example analysis)
- types and effects of non-pharmacologic interventions (CDC link)
- vaccination mandates
- incidence of vaccination-related adverse events
- elaborations of elements on data publication and education in international contexts (2021 overview, speculations on data from China, WHO overview)
See the “get started” link on the vjcitn.github.io/teachCovidData site for current details.