{hestia} fits Bayesian compartmental infection models — such as
susceptible-infected-recovered (SIR) and multi-compartment hidden Markov
variants — from individual-level outcome data. Models are composed from
infection-process and observation-process components and fit using Stan via
{rstan}.
remotes::install_github("ACCIDDA/hestia")Requires R >= 4.1.0 and a C++ toolchain (for Stan model compilation).
Compose an infection-process model from transmit() (transmission between
compartments) and progress() (within-host progression) steps:
library(hestia)
# A basic SIR model: S -> I transmission, I -> R recovery (rate fit from data)
inf_process <- make_infection_model(
transmit(from = "S", to = "I"),
progress(from = "I", to = "R", gamma = NA)
)Pair the infection process with an observation model
(make_observation_model()) and fit it with run_model(). See the
vignettes for full worked
examples, including a multi-compartment (symptomatic/asymptomatic) model.
{hestia} is developed by the Atlantic Coast Center for Infectious Disease
Dynamics and Analytics (ACCIDDA).