# John Lees' blog

Pathogens, informatics and modelling at EMBL-EBI

# Model flexibility and number of parameters

This post is some thoughts I had after reading ‘Real numbers, data science and chaos: How to fit any dataset with a single parameter’ by Laurent Boué. arXiv:1904.12320 The paper above shows that any dataset can be approximated by the following single-parameter function: $\sin^2 (2^{x \tau} \mathrm{arcsin} \sqrt{\alpha})$ Where $x$ is an integer, $\tau$ is a constant which controls the level of accuracy, and $\alpha$ is a real-valued parameter which is fit to the dataset in question.

# Quantify everything, all of the time

I recently read the article by Wu et al in Nature Biotechnology (you can also find similar articles in pretty much all of the Nature journals) which analysed data on participants at some virtual meetings over the past couple of years, and came to the conclusion that ‘Virtual meetings promise to eliminate geographical and administrative barriers and increase accessibility, diversity and inclusivity’. Which sounds great! Of course there are certainly some good things to come out of virtual meetings, and many unresolved issues with in person conferences.

# Did 1.27M people die from AMR in 2019?

I would answer ‘I don’t know’. If I was being less trite, I would add that I’m more confident saying that it was between 100k and 10M – whichever way you look at it, vast numbers that are growing larger, and which require action on multiple fronts. The authors of the study ‘Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis’ (also called ’the GRAM study’) have actually attempted to estimate this.