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

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

⚠️ This is a work in progress ⚠️

pyrenew 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. To start, visit the tutorials section on the project’s website here.

The following diagram illustrates the composition of the HospitalAdmissionsModel class. Notably, all components are modular and can be replaced with custom implementations.

flowchart LR %% Elements rt_proc["Random Walk Rt\nProcess (latent)"]; latent_inf["Latent Infections"] latent_ihr["Infection to Hosp.\nrate (latent)"] neg_binom["Observation process\n(hospitalizations)"] latent_hosp["Latent Hospitalizations"]; i0["Initial infections\n(latent)"]; gen_int["Generation\ninterval (fixed)"]; hosp_int["Hospitalization\ninterval (fixed)"]; %% Models basic_model(("Infections\nModel")); admin_model(("Hospital Admissions\nModel")); %% 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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