Code and data for "Deep learning from videography as a tool for measuring infection in poultry".

Link to the paper: https://doi.org/10.1098/rsos.250151

Link to the data: https://doi.org/10.5281/zenodo.14712491

Version: 3.10.10

The dependencies for downstream analyses are listed in env.yml

You can install a virtual environment using conda by running:

conda env create -f env.yml

Available soon

Data is available at https://zenodo.org/records/14712492

python -m dlc4ecoli.dlc.extract /path/to/data

python -m dlc4ecoli.of.extract /path/to/data

You can reproduce most figures by running the plots.ipynb notebook.

The other brms figures are created from the R script in data/utils/analysis.R

Version: 4.4.0

install.packages(brms, envalysis, ggdist, ggplot2)

Simply run the analysis.R script after setting the work directory to this repository

setwd("path/to/dlc4ecoli")

@article{10.1098/rsos.250151,
  title={Deep learning from videography as a tool for measuring E. coli infection in poultry},
  author={Scheidwasser, Neil and Poulsen, Louise Ladefoged and Leow, Prince Ravi and Khurana, Mark Poulsen and Iglesias-Carrasco, Maider and Laydon, Daniel Joseph and Donnelly, Christl Ann and Bojesen, Anders Miki and Bhatt, Samir and Duch{\^e}ne, David Alejandro},
  journal={Royal Society Open Science},
  volume={12},
  number={10},
  pages={250151},
  year={2025},
  publisher={The Royal Society}
}