In matthewhirschey/bespokelearnr: Bespoke data science learning

COVID-19 rapidly infected people across the US

url <- "https://github.com/BAREJAA/bespokelearnr/blob/merge_essentials/inst/tutorials/bespokelecture/data/covid.gif?raw=true"
knitr::include_graphics(url)

COVID-19 Data {.build}

• Infection rates (epidemiology)
• Basic science data (sequencing, molecular biology, drug lead)
• Translational data (pre-clinical, Phase 1, Phase 2)
• Clinical data (Chest CT)
• Literature (pre-print and peer reviewed)

Rapid emergence generated several healthcare decision-making challenges {.build}

Volume
- Rapid data collection & storage allowed access to huge amounts of medical information

Ubiquity
- Data became available anywhere across geography, social, and economic classes

Latency
- Technology facilitates no delay in access to data

How do you make sense of COVID-19 data to make better health care decisions? {.build}

Data-driven decision making!

url <- "https://upload.wikimedia.org/wikipedia/commons/0/06/DIKW_Pyramid.svg"
knitr::include_graphics(url)

https://en.wikipedia.org/wiki/DIKW_pyramid

matthewhirschey/bespokelearnr documentation built on Oct. 11, 2020, 12:57 a.m.