jangevaare / pathogen.jl

Simulation, visualization, and inference of individual level infectious disease models with Julia

Pathogen.jl

Authors: Justin Angevaare, Zeny Feng, Rob Deardon

Pathogen.jl is a Julia software package for individual level models of infectious diseases (Deardon et al, 2010). It's capabilities include stochastic simulation and Bayesian inference of SEIR, SEI, SIR, and SI individual level models, with fully customizable functions describing individual specific transition rates between disease states (i.e. form of, and relevant risk factors to, susceptibility, transmissibility, latency, removal, and sparks functions). Pathogen.jl is written purely in Julia, which enables this generality without incurring performance costs.

Pathogen.jl infers transmission pathways (i.e. who-infected-who). This inference is completed using a Gibbs step in our specialized MCMC algorithm. This specialized MCMC algorithm also performs event time data augmentation. A detailed overview of this algorithm can be found here.

Installation

The current release can be installed from the Julia REPL with:

pkg> add Pathogen

The development version (master branch) can be installed with:

pkg> add Pathogen#master

Examples of Pathogen.jl workflow are included in the examples directory as a Jupyter notebooks.

1. SIR simulation, inference, and visualization
2. Analysis of a Tomato Spotted Wilt Virus experimental epidemic
3. Analysis of 1861 Hagelloch Measles outbreak

Citation and more information

This package is detailed in Pathogen.jl: Infectious Disease Transmission Network Modeling with Julia, in the Journal of Statistical Software.

@article{pathogenjl,
  title   = {Pathogen.jl: Infectious Disease Transmission Network Modeling with Julia},
  author  = {Angevaare, Justin and
             Feng, Zeny and
             Deardon, Rob},
  year    = {2022},
  journal = {Journal of Statistical Software},
  volume  = {104},
  number  = {4},
  pages   = {1–30},
  url     = {https://www.jstatsoft.org/index.php/jss/article/view/v104i04},
  doi     = {10.18637/jss.v104.i04}}

pathogen.jl's Issues

Create build/deps.jl so tests will pass

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Use with Turing.jl

Hi,

Thanks for the great package! I wonder how much work would be required to connect the simulator here to https://github.com/TuringLang/Turing.jl. Perhaps the Turing team can help with this effort too. Looking forward to hearing from you.

Regards,
Mohamed

Difficulty replicating SIR TN-ILM Simulation and Inference

Hi,
I am having some difficulty replicating the SIR-TN-ILM Simulation and Inference.ipynb

Specifically at this line: obs = observe(sim, Uniform(0.5, 2.5), Uniform(0.5, 2.5), force=true)
I get an argument error, ArgumentError: Uniform: the condition a < b is not satisfied.
The stacktrace brings me to EventObservations.jl, where I modified default_rng to be MersenneTwister based on an error that I got previously(i..e that default_rng is not defined).

Would you have any guidance on this?
Thank you

Comparison to EMOD

Your package looks very exiting. I'm a statistician working with simulator based statistical models and infectious disease modeling is one important application. Do you have experience with other disease simulators such as EMOD. How do they compare to Pathogen.jl?

Error tagging new release

The REQUIRE file could not be found.
cc: @jangevaare