Create a datamation for covid efficacy

[jhofman]

dggoldst caught this tweet, which shows a possible case of Simpson's paradox among vaccinated people in Israel. the news media is starting to pick up on this as well.

it'd be really nice to datamate this if we can find a way. it's a bit different than the salary example because it's binary outcomes, so could challenge us in a nice way, too.

[sharlagelfand]

Oooh thanks Jake Hofman (@jhofman) dggoldst! I'll take a look and see how we might be able to visualize this - definitely good to think about binary outcomes and how we can show those.

[jhofman]

Btw, this was super hard for dggoldst and I to understand. Reading this post helped:

https://web.archive.org/web/20211109015334/https://www.covid-datascience.com/post/israeli-data-how-can-efficacy-vs-severe-disease-be-strong-when-60-of-hospitalized-are-vaccinated

[jhofman]

We looked at this and realized that it's extra challenging because the base rates are low (a handful out of 100,000 people have severe cases), so we created #98 as a simpler case---we'll look at Simpson's Paradox in batting averages to brainstorm binary outcomes, then we'll circle back to this.

[jhofman]

Adding some dangling code I had for creating dataframes to use for playing around with a COVID efficacy visualization:

library(tidyverse)

theme_set(theme_bw())

ppl_per_dot <- 10000

counts <- tribble(
  ~age, ~vax_status, ~num_ppl, ~num_severe_cases,
  "Below 50", "Not vaccinated", 116834, 43,
  "Below 50", "Fully vaccinated", 3501118, 11,
  "Above 50", "Not vaccinated", 186078, 171,
  "Above 50", "Fully vaccinated", 2133516, 290
) %>%
  mutate(num_ok = num_ppl - num_severe_cases)

individual_rows <- counts %>%
  pivot_longer(c(num_severe_cases, num_ok), "variable", "value") %>%
  mutate(num_points = as.integer(ceiling(value / ppl_per_dot))) %>%
  rowwise() %>%
  do(data.frame(age = .$age, vax_status = .$vax_status, outcome = rep(gsub('num_', '', .$variable), times = .$num_points))) %>%
  ungroup()

[jhofman]

Also, two related articles that could be interesting to datamate.

First viz:
https://messaging-custom-newsletters.nytimes.com/template/oakv2?campaign_id=9&emc=edit_nn_20211012&instance_id=42622&nl=the-morning&productCode%3DNN=&regi_id=151685017&segment_id=71397&te=1&uri=nyt%3A%2F%2Fnewsletter%2F3920aed4-2adc-5e7a-8901-0271ca47d214&user_id=3cfe556826e1420e5572984864db2469

Paragraphs starting with "Agency research has estimated":
https://www.nytimes.com/2021/10/06/health/covid-vaccine-children-dose.html?campaign_id=9&emc=edit_nn_20211012&instance_id=42622&nl=the-morning&regi_id=151685017&segment_id=71397&te=1&user_id=3cfe556826e1420e5572984864db2469

(Let me know if you hit paywall problems.)

[jhofman]

Relevant icon array!

https://twitter.com/xruiztru/status/1452180847088517131

[sharlagelfand]

Jake Hofman (@jhofman) can you link some of the other articles we were looking at in our meeting today? especially the ones looking by age. thank you!

[jhofman]

yes!

the first chart here could be interesting to visualize as an icon array / animate: https://messaging-custom-newsletters.nytimes.com/template/oakv2?campaign_id=9&emc=edit_nn_20211012&instance_id=42622&nl=the-morning&productCode%3DNN=&regi_id=151685017&segment_id=71397&te=1&uri=nyt%3A%2F%2Fnewsletter%2F3920aed4-2adc-5e7a-8901-0271ca47d214&user_id=3cfe556826e1420e5572984864db2469

here are the myocarditis numbers from the cdc's nov 2-3 meeting notes

see also slides 3 and 4 here: https://www.cdc.gov/vaccines/acip/meetings/downloads/slides-2021-11-2-3/04-COVID-Oster-508.pdf

[jhofman]

More interesting efficacy numbers here: https://www.cdc.gov/mmwr/volumes/70/wr/mm7032e3.htm?s_cid=mm7032e3_w