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.

```mermaid flowchart LR

%% Elements rtproc["Random Walk Rt\nProcess (latent)"]; latentinf["Latent Infections"] latentihr["Infection to Hosp.\nrate (latent)"] negbinom["Observation process\n(hospitalizations)"] latenthosp["Latent Hospitalizations"]; i0["Initial infections\n(latent)"]; genint["Generation\ninterval (fixed)"]; hosp_int["Hospitalization\ninterval (fixed)"];

%% Models basicmodel(("Infections\nModel")); adminmodel(("Hospital Admissions\nModel"));

%% Latent infections rtproc --> latentinf; i0 --> latentinf; genint --> latentinf; latentinf --> basic_model

%% Hospitalizations hospint --> latenthosp

negbinom --> adminmodel; latentihr --> latenthosp; basicmodel --> adminmodel; latenthosp --> adminmodel; ```

Installation

Install via pip with

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

Models Implemented With PyRenew

• CDCgov/pyrenew-covid-wastewater: Models and infrastructure for forecasting COVID-19 hospitalizations using wastewater data with PyRenew.
• CDCgov/pyrenew-flu-light: An instantiation in PyRenew of an influenza forecasting model used in the 2023-24 respiratory season.

Resources

• The MSR Website provides general documentation and tutorials on using MSR.
• The Model Equations Sheet describe the mathematics of the renewal processes and models MSR supports.
• Additional reading on renewal processes in epidemiology
  • Semi-mechanistic Bayesian modelling of COVID-19 with renewal processes
  • Unifying incidence and prevalence under a time-varying general branching process

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