Meta-Population Compartmental Model for Respiratory Virus Diseases

Simulates respiratory virus epidemics using meta-population compartmental models following Fadikar et. al. (2025) . 'MetaRVM' implements a stochastic SEIRD (Susceptible-Exposed-Infected-Recovered-Dead) framework with demographic stratification by user provided attributes. It supports complex epidemiological scenarios including asymptomatic and presymptomatic transmission, hospitalization dynamics, vaccination schedules, and time-varying contact patterns via mixing matrices.

MetaRVM

Stochastic metapopulation compartmental modeling of respiratory infectious diseases.

Model

MetaRVM is an open-source R package for stochastic metapopulation modeling of respiratory infectious diseases. It supports multiple disease types and is designed for flexible stratification by geography, demographics, or any user-defined grouping. MetaRVM is built for real-time public health decision-making.

MetaRVM extends the classic SEIR framework with disease-specific compartments, time-varying contact patterns, and vaccination dynamics. Disease types currently supported: flu (influenza-like illness) and measles. Each disease has its own set of compartments, parameters, and initialization structure, all declared through a disease registry that keeps the core simulation engine disease-agnostic.

For more details, please refer to the paper: Developing and deploying a use-inspired metapopulation modeling framework for detailed tracking of stratified health outcomes

Documentation

Full documentation is available at: https://RESUME-Epi.github.io/MetaRVM/

Quickstart guide

Installation

Install the current CRAN release with (see the CRAN badge above for the exact released version):

install.packages("MetaRVM")

The development version (2.2.0) is available from GitHub:

# install.packages("remotes")
remotes::install_github("RESUME-Epi/MetaRVM")

Running a simulation

Configs can be supplied as a YAML file path or built programmatically with build_config().

library(MetaRVM)
options(odin.verbose = FALSE)

## prepare the configuration file
cfg <- system.file("extdata", "example_config.yaml", package = "MetaRVM")

The content of the yaml configuration file:

model:
  disease: flu
run_id: ExampleRun
population_data:
  initialization: population_init_n24.csv
  vaccination: vaccination_n24.csv
mixing_matrix:
  weekday_day: m_weekday_day.csv
  weekday_night: m_weekday_night.csv
  weekend_day: m_weekend_day.csv
  weekend_night: m_weekend_night.csv
disease_params:
  ts: 0.5
  ve: 0.4
  dv: 180
  dp: 1
  de: 3
  da: 5
  ds: 6
  dh: 8
  dr: 180
  pea: 0.3
  psr: 0.95
  phr: 0.97
simulation_config:
  start_date: 10/01/2023 # m/d/Y
  length: 150
  nsim: 1
  nrep: 1
  random_seed: 42

# run simulation
sim_out <- metaRVM(cfg)
#> Loading required namespace: pkgbuild

# basic plot: daily hospitalizations by date
library(ggplot2)
hosp <- sim_out$results[disease_state == "H"]
hosp_sum <- hosp[disease_state == "H", .(total = sum(value)), by = "date"]
ggplot(hosp_sum, aes(date, total)) +
  geom_line(, color = "red") +
  labs(y = "Hospitalizations", x = "Date") + theme_bw()

Model structure

MetaRVM implements stratified metapopulation models where each demographic subgroup has its own set of compartments and disease progression. All simulations are stochastic.

Transmission is stratified by user-defined demographic groups (e.g., age, zone, race). Time-varying mixing matrices define how these strata interact (daytime vs. nighttime, weekday vs. weekend), and MetaRVM computes stratum-specific forces of infection. Parameters can be specified as fixed scalars or drawn from distributions (lognormal, gamma, uniform, beta, gaussian), enabling uncertainty quantification across simulation runs.

For simulation control: - nsim sets the number of parameter sets; when distributions are used, each draw is a row in sampled parameter matrices - nrep sets the number of stochastic replicates per parameter set - total runs = nsim × nrep - random_seed ensures reproducibility for both parameter sampling and stochastic trajectories

Required input data and configuration

MetaRVM is configured through a YAML file or the build_config() programmatic builder — both accept the same arguments. A small set of CSV inputs are required alongside the config.

Model output structure

MetaRVM produces simulation results in a unified tidy long-format table that is easy to analyze with data.table, dplyr, or ggplot2. Every record corresponds to a single compartment count or flow quantity for a specific demographic stratum, simulation date, and scenario/instance.

The core output is available in:

• sim$results — compartment counts and flows for each day
• sim$config — the configuration object used to generate the run

Further examples and full documentation

MetaRVM includes several vignettes that demonstrate common workflows using real model configurations and data. These provide detailed, step-by-step examples of how to prepare inputs, configure the model, run simulations, and analyze outputs.

The vignettes can be accessed at:

https://resume-epi.github.io/MetaRVM/articles/

List of vignettes:

• Getting Started
  A gentle introduction showing how to load a configuration file, run a simulation, and inspect outputs.

• Model Configurations
  A detailed walkthrough of the YAML structure, required fields, parameter blocks, optional modules, and how to define population strata and mixing patterns.

• Running a Simulation with metaRVM Full example using a complete set of input files, showing how MetaRVM reads population initialization data, mixing matrices, and vaccination schedules. Includes flu and measles examples.

• Checkpointing and Restoring
  Demonstrates how to run many scenarios at scale, resume long runs, and store intermediate model states.

For the complete function reference, visit:

https://resume-epi.github.io/MetaRVM/reference/