It was great to talk about reproducible workflows for @riotscienceclub @riotscience_wlv. You can watch the recording below, but if you don't want to listen to me talk for 40 minutes, I thought I would summarise my talk in a thread:
Thank you again @JamesEBartlett for a fantastic talk (with a really nice personal touch) on reproducible workflows!
— RIOT Science Club Wolverhampton (@riotscience_wlv) February 16, 2021
Thanks especially for the co-leads @IMLahart for co-hosting and @DrManiBhogal for nabbing James!
Slides: https://t.co/CNqxzOhch1
Video: https://t.co/YjHEHuRJlz
Why are lunch breaks important for #code?
— Dr Rebecca Hirst (@HirstRj) February 11, 2021
If you can't remember what your variable names refer to after lunch, you sure as hell won't remember in 3 months.
More from Science
1/
I've recently come across a disinformation around evidence relating to school closures and community transmission that's been platformed prominently. This arises from flawed understanding of the data that underlies this evidence, and the methodologies used in these studies. pic.twitter.com/VM7cVKghgj
— Deepti Gurdasani (@dgurdasani1) February 1, 2021
The paper does NOT evaluate the effect of school closures. Instead it conflates all ‘educational settings' into a single category, which includes universities.
2/
The paper primarily evaluates data from March and April 2020. The article is not particularly clear about this limitation, but the information can be found in the hefty supplementary material.
3/
The authors applied four different regression methods (some fancier than others) to the same data. The outcomes of the different regression models are correlated (enough to reach statistical significance), but they vary a lot. (heat map on the right below).
4/
The effect of individual interventions is extremely difficult to disentangle as the authors stress themselves. There is a very large number of interventions considered and the model was run on 49 countries and 26 US States (and not >200 countries).
5/
Simulation: Riding in car for 120 min w/ infected passenger who seems fine other than a cough every few mins. (1) a lot of SARS-CoV-2 virus (in fine aerosol particles) accumulation in car cabin w/ windows closed; (2) cracking window open slightly = dramatic reduction. #COVID19 pic.twitter.com/bCmrmnLUPG
— Dr. Richard Corsi (@CorsIAQ) April 4, 2020
2/ Related air exchange rates were based on experimental results in literature for mid-sized sedans. Particle deposition to indoor surfaces were accounted for, as the surface to volume ratio in a 3 m3 cab is large. An important outcome was the intake fraction (IF)
3/ Here, IF is the number of particles (or virions in collective particles) inhaled by a receptor DIVIDED BY the number or particles (or virions in collective particles) emitted by an infector.
4/ Integrated over the two hour drive (in this example) the IF for all windows closed & a receptor at rest is 0.08 (8% of what comes out of the infectors respiratory system ends up in the respiratory system of the receptor). 8%! That is a very high intake factor.
5/ With additional ventilation from cracking a window open drops the IF to 0.012 (1.2%) still relatively high. Can get lower by opening more windows.