π Excited to share my first PhD publication! π
Our paper in Epidemics uses a mathematical model + Scottish surveillance data to understand what COVID-19 pandemic disruptions revealed about RSV disease in young children.
We have our first epinowcast seminar of 2026 today at 3 pm UK time/4 pm in Central Europe. Excited to hear from @dchodge.bsky.social who will discuss tools for serological inference! www.epinowcast.org/seminars/202...
New pre-print with @dchodge.bsky.social @thushan-desilva.bsky.social and others looks at SARS-CoV-2 antibody kinetics and correlates of protection in The Gambia. What can different biomarkers tell us about protection in population where >80% infections asymptomatic?
www.medrxiv.org/content/10.6...
Great to see the seroanalytics.org collection expanding with @dchodge.bsky.social 's work on seroCOP (R package for analysing correlates of protection using Bayesian methods)
It serves as a nice reminder to me that some of the most elegant solutions in machine learning and statistics aren't always the newest deep learning architectures. Sometimes, good old Bayesian inference with smart sampling strategies can create something really cool!
This visualises how modern statistical inference actually works. RJMCMC explores spaces where we don't even know how many parameters we need, perfect for art, where we don't know in advance how many strokes capture an image.
- Proposals are accepted/rejected based on how well they reconstruct your image
- The final painting emerges from thousands of probabilistic decisions
- It's not deterministic, run it twice -> get two different artworks
Upload an image, and the algorithms "paints", one probabilistic brushstroke at a time. Each brushstroke represents a "birth" or "death" jump in the model space:
- The algorithm proposes adding new strokes or removing existing ones
I built an MCMC painter!
I'm excited to share this project I've been working on for a long time, which sits at the intersection of computational statistics and generative art; mcmcPainter!
Link here: mcmcpainter.davidhodgson.me
Getting started on Substack, just some rambling on Yoga Philosophy and a academia. Give me a follow if interested and share yours too! Open to reading anything π open.substack.com/pub/themindf...
Want to understand serological data better? We've compiled a suite of tools which can help you out
π seroanalytics.org
These tools are free, open source, peer reviewed and have comprehensive documentation. Big thanks to @alexlizhill.com, @jameshay.bsky.social and others for their contributions!
New blog post on Correlates of Protection! I try and give an overview of this very confusing concept: davidhodgson.me/post/sm3_cop1/
I think it's good ID modellers try and get a solid understanding of this as it's going become increasingly important for vaccine development over the next few years.
Haha I actually switched to Claude this week so terse bluesky posts incoming.....
AHH cool! I'll have a play with this, doesn't seem active currently tho
Cheers Sam! I've not seen this have you got a link? They are fitting ODIN models with monty these days right?
Yeah it's actually great, converting the c++ to JavaScript is actually not too bad with a little help from AI !
Watch RJMC explore different model dimensions in real-time, use sample data or upload your own CSV.
No installation, just open and experiment. Great for teaching/learning Bayesian model selection!
Package/vignette: dchodge.github.io/rjmc/article...
#statistics #bayesian #MCMC #datascience
π New tool: Reversible Jump MCMC running in your browser!
Built an interactive widget for fitting mixture distributions when you don't know how many components you need.
Check it out: dchodge.github.io/rjmc-widget-...
π¬ New to serological data? Youβre not alone
When I first saw spreadsheets full of columns labelled ELISA_OD, PRNT50, HI_titre, and PVNT_ID50, I had no idea what they really meant.
That confusion inspired me to write a new blog post, βA Dummyβs Guide to Serological Assaysβ
π tinyurl.com/586dsy77
Sure, wanna drop me an email to sort out deets?
"2.1. Overview of inference framework" in the methods gives an overview. But basically if you infer an infection you also need to infer an infection time (an extra parameter), no infection then infection time isn't in the framework anymore. Hence need to jump between different dimensions
Thanks to everyone who worked on this: @jameshay.bsky.social, Sheikh Jarju, Dawda Jobe, Rhys Wenlock, @adamjkucharski.bsky.social, and @thushan-desilva.bsky.social!
It uses reversible jump MCMC to infer missed infections, to help understanding I made a little widget to show you how the fitting process works for simulated data:Β seroanalytics.org/serojump-widget
β¨ What we did:
- Made a Bayesian model to infer who was infected, when, and how their antibody levels changed
- Validated on both simulations and real-world SARS-CoV-2 data from The Gambia.
- Showed that serojump detects more infections (including sub-threshold ones) and provides richer insights
π¨ New paper out in PLOS Computational Biology! π¨
We're excited to share our new paper, serojump, a new probabilistic framework and R package for inferring infections and antibody kinetics from longitudinal serological data.
π Full paper: tinyurl.com/re7du3t2
R package: seroanalytics.org/serojump
Thanks to every who worked on this! @jameshay.bsky.social, Sheikh Jarju, Dawda Jobe, Rhys Wnelock, @adamjkucharski.bsky.social and @thushan-desilva.bsky.social
serojump was designed to be a flexible and pathogen-agnostic solution that can be applied to a wide range of pathogens.
Heres an interactive widget to help with understanding of them reversible jump mcmc methods: lnkd.in/eWGJ39PG
What we did:
- Made a Bayesian model to infer who was infected, when, and how antibody levels changed over time.
- Validated on both simulations and real-world SARS-CoV-2 data from The Gambia.
- Showed that serojump detects more infections (including sub-threshold ones) and provides richer insights
Key features:
- WebAssembly-powered performance (10-50x faster than JS)
- Adaptive MCMC for Bayesian inference
- Vaccine intervention analysis with waning immunity
- Real-time convergence diagnostics
- Export data and plots for further analysis
Just launched an interactive Bayesian epidemic modelling platform that runs entirely in your browser!
No downloads, no installations, no expensive software licenses. Just open the link and start modelling disease dynamics with real-time parameter estimation.
>> widget-bayesian-sir.davidhodgson.me
