DERP uses neural networks to solve inference problems in genetic and mathematical epidemiology. In particular, it provides a way to estimate key aspects of an outbreak (e.g. the reproduction number and prevalence of infection) using a phylogeny reconstructed from genomic data.

  • Code to simulate data phylogenies from epidemics.
  • (Coming soon) Code to train a neural network to predict epidemic properties from a phylogeny.
  • (Coming soon) Code to assist with applying the trained neural network.

The slides below are from ANZIAM2024 where I presented DERP. If you are interested in deep learning with PyTorch, you might also like my blog.

Project maintained by Alexander E. Zarebski