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;
Install via pip with
pip install git+https://github.com/CDCgov/PyRenew@main- 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
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