Happy to share the publication of our work "Estimating social contact rates for the COVID-19 pandemic using Google mobility and pre-pandemic contact surveys", huge thanks to my co-authors @christopherjarvis.bsky.social @ngdavies.bsky.social @pietro-coletti.bsky.social Jantien Backer and John Edmunds

Stata users: if you want to make ‘kmunicate’-style Kaplan–Meier graphs, I’ve just released a Stata package on GitHub. To get it: . net from raw.githubusercontent.com/tpmorris/kmu... User feedback welcome! 1/

This is a nice option that moves beyond lots of copy paste in your script and gets you closer to thinking about repeated patterns that you might eventually put in a package. #rstats

My fav shortcut is alt+shift+k which shows you all the shortcuts.

I found #docker mysterious until I thought of it like buying a laptop setting it up exactly how I need and then handing it over to another person. Then dockerhub is like the post office that takes my laptop and delivers it to others. Tugboat makes it pretty easy to do in #rstats

I feel like I started my #stats journey wanting to do lots of fancy multi-level, spatial, and Bayesian models. Then I'm now happy if I can produce a useful count, that I understand and can clearly explain. In many ways knowing the complex stuff just gave me permission to do simple things. #rstats

Glad to see @mapaction.bsky.social is now on Bluesky #gis #humanitarian #emergencyresponse #informationmanagement

I was chatting with a colleague about their #PhD viva on #spatialstats and I showed them this diagram that I made as as part of my thesis to help me understand the building blocks of #spatial models, especially for #inla. #statssky. pg 254 tinyurl.com/3ymp26p3

Today I learned that the R in #RShiny is doing a lot of the heavy lifting in that hashtag. #shinyisnotjustR #Rstats

Knuth, one of the greats in CS history, famously said, "Premature optimization is the root of all evil...in programming." Only a small % of R apps need to be efficient. But they all need to be correct, and often loops etc. are clearer and thus more likely to be correct. Anti-loop mania is bad for R.

bluesky is inspiring! I think I've made my first #R package #rstats #eda github.com/jarvisc1/qex.... Inspired by [email protected] and a chat with @pete-mck.bsky.social bsky.app/profile/did:.... I thought why not try to bring what I like about #Stata and #data.table to the #tidyverse.

Nice map. Reminds me of the population density by James Cheshire. #maps

Something I find useful from base #R is the dput command. When I have an object but I want to recreate it and copy into my script I chuck it into dput and it gives me the code that could be used to create it. #Rstats #Datascience

Reminds me that there is a difference between being right and not having to be too wrong. It's great when the margin of error for success is wide. I also avoid absolutes! 'No one knows anything' or 'everyone is faking it' and I realise I'm very lucky to have met people with deep understanding.

I love this! Makes me think about stuff by Paul Graham and how clutter might relate to energy and perhaps (big perhaps!) kids have coarser perception so less drained. Maybe some of these high-energy rulers are Peter Pan like and never grew up? 😂 paulgraham.com/stuff.html

I am increasingly convinced that besides the usual suspects like beauty, intellect and charisma, it's the people with energy who run the world

Hello Bluesky! We’re Applied Epi! What do we want? To improve Applied Epidemiology worldwide! When do we want it? Well probably in an achievable timeframe that doesn’t completely destroy a much needed and already existing infrastructure that is overburdened!

ℹ️ Applying for postdoc roles in the UK? Check the helpful application advice thread by @rozeggo.bsky.social👇 #AcademicSky 🧪 Another great community contribution from Roz is IDDjobs! 🔗 Visit iddjobs.org for job opportunities in topics broadly aligned with infectious disease dynamics #IDsky #EpiSky

I love this post. So for me and maybe for me only, the key is abstraction and context shifting. The more I can zoom in and out of different levels of abstraction without giving myself the burden of fully understanding at the moment the easier I can start to see patterns.

This reminds me of a time I was sat in a meeting about efficiency savings where I was calculating the hourly cost of bringing everyone together being 10x any saving we could make. And we concluded that we would need several more meetings! 🤦‍♂️

I also quite like maximum likelihood theory, for me it’s just someone saying hey if you had a big bunch of numbers and you could only pick one to represent them all what would you go for?

My third favourite thing about stats. Once you learn how to do really complex modelling and methods you can give yourself permission to just calculate a percentage and make a decision. Because you’ve probably realised all the complexity didn’t affect anything meaningfully.

My next fav thing in stats simulation studies. You really can’t go wrong by following @timpmorris.bsky.social and reading this onlinelibrary.wiley.com/doi/10.1002/... It pretty much allows you to play God and understand why and when methods fail. Even when you’ve invented your own method!

Looks fun here. Okay my fav thing in stats. How do you get an upper bound for a confidence interval when you have no events? 3/n The rule of three. en.m.wikipedia.org/wiki/Rule_of...

This is how it works in the civil service except it’s binary. Take the bullet points, write your response in the same order as the advert, with the same key words. Maybe even keep the headings. Don’t try to be clever or original it won’t help you.