just learnt that vs code recently added native git blame decorations with setting: "git.blame.editorDecoration.enabled": true good bye complex extensions, goodbye gitlens 👋

I've been sneaking on that course (by Sam Abbott, Thomas Robacker, Nick Reich) for the past few days on github (did not see the deployed version) and it is really good learning material for learning epi forecasting: nfidd.github.io/sismid/

James does really great science ! This is a great job

Wow. This is an amazing project with a huge amount of work behind it. Even if you aren't into drosophila, it's worth a look

But in fact, fly immunity is highly reproducible! 🎊 ~80% of claims could be verified. Moreover, some challenged claims just reflect the field advancing its knowledge/tools. The lesson: if the tools are good and the research largely exempt from direct translation pressures, science works. 3.5/n

When everything is considered together (w/ caveats), the most prestigious institutes are the most likely to produce irreproducible work. Journal tier was a more minor effect, and in fact, "high-impact" but not "trophy" journals publish the most reproducible science. But, many caveats (cont.) 4.3/n

more than that but right order of magnitude. I always say $10^1 for preprint; $10^3 for journal article; $10^5 for research. most costs are staff not tech because of responsible screening, customer service, etc.

This opinion (LLMs have no world model) is repeated at every AI track in IDD conferences, and while it's true for a narrow definition of world model, I am sure it does not square with audience experience. Contrary to what Gary says, LLMs play good (1'800 ELO) chess: dynomight.net/more-chess/.

Not sure how I missed this, but Dijkstra's algorithm has been beaten on sparse graphs 😮 arxiv.org/abs/2504.17033 (Unsurprisingly, this got a STOC'25 best paper award.)

Which academic communities have a good record of sharing domain-specific open source tools? Bioinformatics and geospatial analysis comes to mind. I am trying to think about if they are examples to follow or if there is some difference in the domain (to Infectious disease modelling).

Earlier this month I gave a workshop on Bayesian methods in epidemiology. With my hosts I developed a pretty elaborate, demonstrative analysis for the workshop which I am happy to share publicly here. HTML: betanalpha.github.io/assets/chapt... PDF: betanalpha.github.io/assets/chapt...

Great news today for note-taking: logseq database feature branch got merged into master after two year of limbo. Very excited to stop using obsidian for notes and switch back to it! github.com/logseq/logseq

Note that one of these projects is about delay estimation (described by one 5 year old as double extra boring) but critical to get right for most/all downstream modelling use cases.

For a few years, I have been paying out of pocket for the epinowcast forum as I thought it was important to have a venue for people to collab. There is some great stuff kicking off at the moment which is lovely to see. community.epinowcast.org/t/include-a-...

This is a very interesting talk by @scarpino.bsky.social continuing the exploration of the predictability of an influenza season using fundamental and practical methods www.youtube.com/live/8RslYIn...

The deadline for the Epidemics special issue on Artificial Intelligence for Infectious Disease Dynamics has been extended to 30 April 2025, so there is still time to submit your paper 📝 Feel free to contact me or another guest editor for more information 😊 www.sciencedirect.com/journal/epid...

The studies informing stratified parameters in IDD models often have partitions (e.g age group) different from the model partitions. Most papers takes the median/mean estimates, but a correct solution exists: see this great paper/R pkg (C. Pearson, @lucygoodfellow.bsky.social @mert0248.bsky.social)

Very interesting write up on OWD pandemic work. In general, it’s always surprising how small most teams doing important thing are.

Our framework for combining standard epi models with an economic dynamic decision model is up on medRxiv! super flexible and excited to see where this project goes next www.medrxiv.org/content/10.1...

Just discovered this excellent book on genomics from Tanja Stadler et al. Free pdf offered here: decodinggenomes.org. The chapters I've read hit a biology/statistics sweet spot for me and probably other IDD researchers.