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PyRenew: A Package for Bayesian Renewal Modeling with JAX and NumPyro.

⚠️ This is a work in progress ⚠️

The PyRenew package is a flexible tool for simulation and statistical inference of epidemiological models, emphasizing renewal models. Built on top of the numpyro Python library, pyrenew provides core components for model building, including pre-defined models for processing various types of observational processes.

The fundamental building blocks are the Model metaclass, from which we can draw samples, and the RandomVariable metaclass which has been abstracted to allow for sampling from distributions, computing a mechanistic equation, or simply returning a fixed value. (See the tutorial Fitting a basic renewal model to see how this works.)

The following diagram illustrates the composition of the HospitalAdmissionsModel class. (See the tutorial Fitting a hospital-only admissions model for details.)

flowchart LR

  %% Elements
  rt_proc["Random walk RT process <br> (latent)"];
  latent_inf["Latent infections"]
  latent_ihr["Infection to hospitalization rate <br> (latent)"]
  neg_binom["Observation process <br> (hospitalizations)"]
  latent_hosp["Latent hospitalizations"];
  i0["Initial infections <br> (latent)"];
  gen_int["Generation interval <br> (fixed)"];
  hosp_int["Hospitalization interval <br> (fixed)"];

  %% Models
  basic_model(("Infections model"));
  admin_model(("Hospital admissions model"));

  %% Latent infections
  rt_proc --> latent_inf;
  i0 --> latent_inf;
  gen_int --> latent_inf;
  latent_inf --> basic_model

  %% Hospitalizations
  hosp_int --> latent_hosp

  neg_binom --> admin_model;
  latent_ihr --> latent_hosp;
  basic_model --> admin_model;
  latent_hosp --> admin_model;

Installation

Install via pip with

pip install git+https://github.com/CDCgov/PyRenew@main

Models Implemented With PyRenew

Resources

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