Title:	Meta-Population Compartmental Model for Respiratory Virus Diseases
Version:	1.0.0
Description:	Simulates respiratory virus epidemics using meta-population compartmental models following Fadikar et. al. (2025) <doi:10.1101/2025.05.05.25327021>. 'MetaRVM' implements a stochastic SEIRD (Susceptible-Exposed-Infected-Recovered-Dead) framework with demographic stratification by age, race, and geographic zones. It supports complex epidemiological scenarios including asymptomatic and presymptomatic transmission, hospitalization dynamics, vaccination schedules, and time-varying contact patterns via mixing matrices.
URL:	https://RESUME-Epi.github.io/MetaRVM/, https://github.com/RESUME-Epi/MetaRVM
BugReports:	https://github.com/RESUME-Epi/MetaRVM/issues
License:	MIT + file LICENSE
Encoding:	UTF-8
RoxygenNote:	7.3.2
Depends:	R (≥ 3.5.0)
Imports:	data.table, dplyr, ggplot2, magrittr, methods, odin, purrr, R6, tidyr, yaml
Suggests:	dde, knitr, pkgdown, rmarkdown, testthat (≥ 3.0.0)
VignetteBuilder:	knitr, rmarkdown
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2025-12-15 20:20:43 UTC; fadikar
Author:	Arindam Fadikar [aut, cre, cph], Charles Macal [ctb], Martinez Moyano Ignacio Javier [ctb], Ozik Jonathan [ctb]
Maintainer:	Arindam Fadikar <afadikar@anl.gov>
Repository:	CRAN
Date/Publication:	2025-12-19 15:20:13 UTC

MetaRVM: Meta-Population Compartmental Model for Respiratory Virus Diseases

Description

Simulates respiratory virus epidemics using meta-population compartmental models following Fadikar et. al. (2025) doi:10.1101/2025.05.05.25327021. 'MetaRVM' implements a stochastic SEIRD (Susceptible-Exposed-Infected-Recovered-Dead) framework with demographic stratification by age, race, and geographic zones. It supports complex epidemiological scenarios including asymptomatic and presymptomatic transmission, hospitalization dynamics, vaccination schedules, and time-varying contact patterns via mixing matrices.

Author(s)

Maintainer: Arindam Fadikar afadikar@anl.gov (ORCID) [copyright holder]

Other contributors:

• Charles Macal [contributor]

• Martinez Moyano Ignacio Javier [contributor]

• Ozik Jonathan [contributor]

See Also

Useful links:

• https://RESUME-Epi.github.io/MetaRVM/

• https://github.com/RESUME-Epi/MetaRVM

• Report bugs at https://github.com/RESUME-Epi/MetaRVM/issues

MetaRVM Checkpoint Class

Description

R6 class to handle MetaRVM checkpoint data. This class is a simplified version of MetaRVMConfig tailored for storing and accessing simulation checkpoints.

Details

The MetaRVMCheck class is designed to hold the state of a simulation at a specific time point, allowing for continuation or analysis. It stores all necessary parameters and population states.

Super class

MetaRVM::MetaRVMConfig -> MetaRVMCheck

Public fields

Methods

Public methods

• MetaRVMCheck$new()

• MetaRVMCheck$clone()

Method new()

Initialize a new MetaRVMCheck object

Usage

MetaRVMCheck$new(input)

Arguments

Returns

A new MetaRVMCheck object.

Method clone()

The objects of this class are cloneable with this method.

Usage

MetaRVMCheck$clone(deep = FALSE)

Arguments

Author(s)

Arindam Fadikar

MetaRVM Configuration Class

Description

R6 class to handle MetaRVM configuration data with validation and methods. This class encapsulates all configuration parameters needed for MetaRVM simulations, providing methods for parameter access, validation, and introspection.

Details

The MetaRVMConfig class stores parsed configuration data from YAML files and provides structured access to simulation parameters. It automatically validates configuration completeness and provides convenient methods for accessing demographic categories, population mappings, and other simulation settings.

Public fields

Methods

Public methods

• MetaRVMConfig$new()

• MetaRVMConfig$get()

• MetaRVMConfig$get_all()

• MetaRVMConfig$list_parameters()

• MetaRVMConfig$parameter_summary()

• MetaRVMConfig$set()

• MetaRVMConfig$print()

• MetaRVMConfig$get_pop_map()

• MetaRVMConfig$get_age_categories()

• MetaRVMConfig$get_race_categories()

• MetaRVMConfig$get_zones()

• MetaRVMConfig$clone()

Method new()

Initialize a new MetaRVMConfig object

Usage

MetaRVMConfig$new(input)

Arguments

Returns

New MetaRVMConfig object (invisible)

Method get()

Get a configuration parameter

Usage

MetaRVMConfig$get(param)

Arguments

Returns

The requested parameter value

Method get_all()

Get all configuration parameters as a list

Usage

MetaRVMConfig$get_all()

Returns

Named list of all configuration parameters

Method list_parameters()

List all available parameter names

Usage

MetaRVMConfig$list_parameters()

Returns

Character vector of parameter names

Method parameter_summary()

Show summary of parameter types and sizes

Usage

MetaRVMConfig$parameter_summary()

Returns

Data frame with parameter information

Method set()

Set a configuration parameter

Usage

MetaRVMConfig$set(param, value)

Arguments

Returns

Self (invisible) for method chaining

Method print()

Print summary of configuration

Usage

MetaRVMConfig$print()

Returns

Self (invisible)

Method get_pop_map()

Get population mapping data

Usage

MetaRVMConfig$get_pop_map()

Returns

data.table containing population mapping with demographic categories

Method get_age_categories()

Get available age categories

Usage

MetaRVMConfig$get_age_categories()

Returns

Character vector of unique age categories, or NULL if no population mapping available

Method get_race_categories()

Get available race categories

Usage

MetaRVMConfig$get_race_categories()

Returns

Character vector of unique race categories, or NULL if no population mapping available

Method get_zones()

Get available zones

Usage

MetaRVMConfig$get_zones()

Returns

Character vector of unique zone identifiers, or NULL if no population mapping available

Method clone()

The objects of this class are cloneable with this method.

Usage

MetaRVMConfig$clone(deep = FALSE)

Arguments

Author(s)

Arindam Fadikar

Examples

# Initialize from YAML file
example_config <- system.file("extdata", "example_config.yaml", package = "MetaRVM")
config <- MetaRVMConfig$new(example_config)

# Access parameters
config$get("N_pop")
config$get("start_date")

# Get demographic categories
ages <- config$get_age_categories()
races <- config$get_race_categories()
zones <- config$get_zones()

MetaRVM Results Class

Description

R6 class to handle MetaRVM simulation results with comprehensive analysis and visualization methods. This class stores formatted simulation results and provides methods for data summarization, subsetting, and visualization with flexible demographic groupings.

Details

The MetaRVMResults class automatically formats raw simulation output upon initialization, converting time steps to calendar dates and adding demographic attributes. It provides methods for flexible data summarization across any combination of age, race, and geographic zone categories, plus method chaining for streamlined analysis workflows.

Public fields

Methods

Public methods

• MetaRVMResults$new()

• MetaRVMResults$print()

• MetaRVMResults$subset_data()

• MetaRVMResults$summarize()

• MetaRVMResults$clone()

Method new()

Initialize a new MetaRVMResults object

Usage

MetaRVMResults$new(
  raw_results,
  config,
  run_info = NULL,
  formatted_results = NULL
)

Arguments

Returns

New MetaRVMResults object (invisible)

Method print()

Print summary of results

Usage

MetaRVMResults$print()

Returns

Self (invisible)

Method subset_data()

Subset the data based on any combination of parameters

Usage

MetaRVMResults$subset_data(
  ages = NULL,
  races = NULL,
  zones = NULL,
  disease_states = NULL,
  date_range = NULL,
  instances = NULL,
  exclude_p_columns = TRUE
)

Arguments

Returns

MetaRVMResults object with subset of results

Method summarize()

Summarize results across specified demographic characteristics

Usage

MetaRVMResults$summarize(
  group_by,
  disease_states = NULL,
  date_range = NULL,
  stats = c("mean", "median", "sd"),
  quantiles = c(0.25, 0.75),
  exclude_p_columns = TRUE
)

Arguments

Returns

data.table with summarized time series data or all instances if stats = NULL

Method clone()

The objects of this class are cloneable with this method.

Usage

MetaRVMResults$clone(deep = FALSE)

Arguments

Author(s)

Arindam Fadikar

Examples

options(odin.verbose = FALSE)
example_config <- system.file("extdata", "example_config.yaml", package = "MetaRVM")
# Run simulation
results_obj <- metaRVM(example_config)
# Access formatted results
head(results_obj$results)

# Subset data with multiple filters
subset_data <- results_obj$subset_data(
  age = c("18-49", "50-64"), 
  disease_state = c("H", "D"),
  date_range = c(as.Date("2024-01-01"), as.Date("2024-02-01"))
)

# Method chaining for analysis and visualization
results_obj$subset_data(disease_state = "H")$summarize(
  group_by = c("age", "race"), 
  stats = c("median", "quantile"),
  quantiles = c(0.25, 0.75)
)$plot()

MetaRVM Summary Class

Description

R6 class for summarized MetaRVM results with plotting capabilities and method chaining support. This class stores summarized simulation data and provides visualization methods that automatically adapt based on the data structure and grouping variables.

Details

The MetaRVMSummary class is designed to work seamlessly with method chaining from MetaRVMResults. It stores either summary statistics (mean, median, quantiles, etc.) or individual instance data, and provides intelligent plotting methods that automatically determine appropriate visualizations based on the data structure and demographic groupings.

The class supports two data types:

• Summary data: Contains aggregated statistics across simulation instances

• Instance data: Contains individual trajectory data for each simulation instance

Plotting behavior adapts automatically:

• Single grouping variable: Facets by demographic category, colors by disease state

• Two grouping variables: Grid layout with both demographics as facet dimensions

• Three grouping variables: Grid layout with first two as facets, third as color

Public Fields

data

data.table containing summarized results

config

MetaRVMConfig object from original simulation

type

Character string indicating data type ("summary" or "instances")

Public fields

Methods

Public methods

• MetaRVMSummary$new()

• MetaRVMSummary$print()

• MetaRVMSummary$plot()

• MetaRVMSummary$clone()

Method new()

Initialize MetaRVMSummary object

Usage

MetaRVMSummary$new(data, config, type)

Arguments

Returns

New MetaRVMSummary object (invisible)

Method print()

Print summary of the data object

Usage

MetaRVMSummary$print()

Returns

Self (invisible)

Method plot()

Plot method that shows median with quantile bands

Usage

MetaRVMSummary$plot(ci_level = 0.95, theme = theme_minimal(), title = NULL)

Arguments

Details

This method creates time series plots with automatic layout adaptation based on grouping variables:

• For summary data: Shows median lines with quantile confidence bands

• Automatically determines faceting strategy based on number of grouping variables

• Uses disease states for color differentiation when appropriate

The method requires specific data structure:

• Summary data must contain 'median_value' and quantile columns (e.g., 'q25', 'q75')

• Instance data must contain 'instance' column for individual trajectory grouping

Returns

ggplot object

Method clone()

The objects of this class are cloneable with this method.

Usage

MetaRVMSummary$clone(deep = FALSE)

Arguments

Author(s)

Arindam Fadikar

Examples

options(odin.verbose = FALSE)
example_config <- system.file("extdata", "example_config_dist.yaml", package = "MetaRVM")
# Run simulation
results <- metaRVM(example_config)
# Typically created through method chaining
summary_obj <- results$subset_data(disease_state = "H")$summarize(
  group_by = c("age", "race"), 
  stats = c("median", "quantile"),
  quantiles = c(0.25, 0.75)
)

# Direct plotting
summary_obj$plot()

# Plot with custom ggplot theme and confidence level
summary_obj$plot(theme = ggplot2::theme_bw())

Function to draw samples for distributional parameters

Description

Function to draw samples for distributional parameters

Usage

draw_sample(config_list, N_pop, seed = NULL)

Arguments

Value

A random sample drawn from the distribution specified by the dist component

Format MetaRVM simulation output

Description

This function formats raw MetaRVM simulation output by:

1. Converting time steps to calendar dates

2. Adding demographic attributes from population mapping

3. Handling different disease states appropriately:

   • Regular states (S, E, I, etc.): Keep values at integer time points

   • New count states (n_ prefix): Sum pairs to get daily counts

Usage

format_metarvm_output(sim_output, config)

Arguments

Value

data.table with formatted output including calendar dates and demographics

Note

This function is used for formatting the meta_sim output when MetaRVM function is called.

NULL Coalescing Operator

Description

Returns the left-hand side if it's not NULL, otherwise returns the right-hand side. This is a utility function used internally by MetaRVM classes.

Usage

x %||% y

Arguments

Value

x if x is not NULL, otherwise y

Examples

## Not run: 
user_title <- "User Title"
# Internal usage in classes
title <- user_title %||% "Default Title"

## End(Not run)

Run a MetaRVM epidemic simulation

Description

metaRVM() is the high-level entry point for running a MetaRVM metapopulation respiratory virus simulation. It parses the configuration, runs one or more simulation instances (deterministic or stochastic), formats the ODIN/MetaRVM output into a tidy long table with calendar dates and demographic attributes, and returns a MetaRVMResults object for downstream analysis and plotting.

Usage

metaRVM(config_input)

Arguments

Details

The configuration input controls:

• Population structure (e.g., age, race, zone)

• Disease parameters (ts, tv, ve, de, dp, da, ds, dh, dr, pea, psr, phr, dv, etc.)

• Mixing matrices (weekday/weekend, day/night contact patterns)

• Vaccination schedule and immunity waning

• Simulation settings (start date, length, number of instances, stochastic vs. deterministic mode, checkpointing)

Internally, metaRVM():

1. Parses the YAML configuration via parse_config().

2. Calls the ODIN-based simulation engine meta_sim() for each instance.

3. Uses format_metarvm_output() to convert time steps to dates and attach demographic attributes.

4. Wraps the formatted output and metadata in a MetaRVMResults object that supports method chaining for subsetting, summarizing, and plotting.

Value

A MetaRVMResults R6 object with three key components:

$results

A tidy data.table with one row per date–subpopulation–disease state–instance combination. Typical columns include:

• date: calendar date (Date)

• age, race, zone: demographic categories (if present in the population mapping)

• disease_state: compartment or flow label (e.g., S, E, I_symp, H, R, D, n_SE, n_IsympH, etc.)

• value: population count or daily flow

• instance: simulation instance index (1, 2, …)

$config

The MetaRVMConfig object used for the run.

$run_info

A list with metadata such as n_instances, date_range, delta_t, and checkpoint information.

Author(s)

Arindam Fadikar, Charles Macal, Ignacio Martinez-Moyano, Jonathan Ozik

References

Fadikar, A., et al. "Developing and deploying a use-inspired metapopulation modeling framework for detailed tracking of stratified health outcomes"

See Also

parse_config() for reading YAML configurations, MetaRVMConfig for configuration management, MetaRVMResults for analysis and plotting, meta_sim() for the low-level simulation engine.

Examples

options(odin.verbose = FALSE)
example_config <- system.file("extdata", "example_config.yaml",
                              package = "MetaRVM")

# Run a single-instance simulation from a YAML file
results <- metaRVM(example_config)

# Print a high-level summary
results

# Access the tidy results table
head(results$results)

# Summarize and plot hospitalizations and deaths by age and race
results$summarize(
  group_by       = c("age", "race"),
  disease_states = c("H", "D"),
  stats          = c("median", "quantile"),
  quantiles      = c(0.25, 0.75)
)$plot()

# Using a pre-parsed configuration object
cfg <- parse_config(example_config, return_object = TRUE)
results2 <- metaRVM(cfg)

Metapopulation Respiratory Virus Model Simulator

Description

The core simulation engine that implements a stochastic compartmental SEIRD (Susceptible-Exposed-Infected-Recovered-Dead) model for respiratory virus epidemics across multiple demographic subpopulations. The function compiles and executes an ODIN-based differential equation model with time-varying contact patterns, vaccination dynamics, and complex disease progression pathways.

Usage

meta_sim(
  N_pop,
  ts,
  tv,
  S0,
  I0,
  P0,
  R0,
  H0 = rep(0, N_pop),
  D0 = rep(0, N_pop),
  Ia0 = rep(0, N_pop),
  Ip0 = rep(0, N_pop),
  E0 = rep(0, N_pop),
  V0 = rep(0, N_pop),
  m_weekday_day,
  m_weekday_night,
  m_weekend_day,
  m_weekend_night,
  start_day = 0,
  delta_t,
  vac_mat,
  dv,
  de,
  pea,
  dp,
  da,
  ds,
  psr,
  dh,
  phr,
  dr,
  ve,
  nsteps,
  is.stoch = FALSE,
  seed = NULL,
  do_chk = FALSE,
  chk_time_steps = NULL,
  chk_file_names = NULL
)

Arguments

Details

The model implements a metapopulation epidemiological framework with the following features:

Compartmental Structure:

• S: Susceptible individuals

• E: Exposed (incubating) individuals

• I_presymp: Presymptomatic infectious individuals

• I_asymp: Asymptomatic infectious individuals

• I_symp: Symptomatic infectious individuals

• H: Hospitalized individuals

• R: Recovered individuals

• D: Deceased individuals

• V: Vaccinated individuals

• P: Total living population (excludes deaths)

Disease Progression Pathways:

1. S → E: Exposure through contact with infectious individuals

2. E → I_asymp/I_presymp: Progression to infectious states (proportion pea)

3. I_presymp → I_symp: Development of symptoms

4. I_asymp → R: Direct recovery from asymptomatic state

5. I_symp → R/H: Recovery or hospitalization (proportion psr)

6. H → R/D: Hospital discharge or death (proportion phr)

7. R → S: Loss of immunity

8. S → V: Vaccination

9. V → S/E: Vaccine waning or breakthrough infection

Mixing Patterns: Contact patterns vary by:

• Day of week: Weekday vs. weekend patterns

• Time of day: Day (6 AM - 6 PM) vs. night (6 PM - 6 AM) patterns

• Each pattern specified by N_pop × N_pop contact matrix

Force of Infection: Transmission occurs through contact between susceptible/vaccinated individuals and all infectious compartments (I_presymp + I_asymp + I_symp), modified by:

• Population-specific transmission rates (ts, tv)

• Time-varying contact patterns

• Vaccine effectiveness for breakthrough infections

Stochastic vs. Deterministic Mode:

• Deterministic: Uses exact differential equations

• Stochastic: Adds demographic stochasticity via binomial draws

Vaccination Implementation: Vaccination is implemented as time-varying input with:

• Scheduled vaccination counts per time step and subpopulation

• Vaccine effectiveness reducing infection probability

• Waning immunity returning individuals to susceptible state

Value

Returns a data.table with the following structure:

step

Integer time step index (0 to nsteps)

time

Continuous simulation time (step × delta_t)

disease_state

Character vector of compartment names

population_id

Character vector of subpopulation identifiers

value

Numeric values representing population counts in each compartment

Available disease states in output:

• Core compartments: S, E, I_presymp, I_asymp, I_symp, H, R, D, V, P

• Derived outputs: I_all (total infectious), cum_V (cumulative vaccinations)

• Transition flows: n_SE, n_EI, n_HR, n_HD, etc. (new infections, hospitalizations, deaths)

• Debug outputs: p_SE, p_VE, I_eff (probabilities and effective populations)

Parameter Scaling

All duration parameters are automatically converted to rates (1/duration). Scalar parameters are automatically expanded to vectors of length N_pop. This allows flexible specification of homogeneous or heterogeneous parameters.

Checkpointing

When do_chk = TRUE, the function saves a checkpoint file containing:

• Final compartment states for simulation continuation

• All model parameters for reproducibility

• Vaccination schedule data

• Population structure information

Author(s)

Arindam Fadikar, Charles Macal, Ignacio Martinez-Moyano, Jonathan Ozik

References

• ODIN package: https://mrc-ide.github.io/odin/

• Fadikar, A., et al. "Developing and deploying a use-inspired metapopulation modeling framework for detailed tracking of stratified health outcomes"

See Also

metaRVM for high-level simulation interface with configuration files parse_config for configuration file processing format_metarvm_output for output formatting with demographics

Examples

options(odin.verbose = FALSE)
# Basic deterministic simulation
N_pop <- 2
nsteps <- 400

# Initialize populations
S0 <- rep(1000, N_pop)
I0 <- rep(10, N_pop)
P0 <- S0 + I0
R0 <- rep(0, N_pop)

# Contact matrices (simplified - identity matrices)
contact_matrix <- diag(N_pop)

# Basic vaccination schedule (10% vaccination)
vac_mat <- matrix(0, nrow = nsteps + 1, ncol = N_pop + 1)
vac_mat[, 1] <- 0:nsteps
vac_mat[1, 1 + (1:N_pop)] <- P0 * 0.1

# Run simulation
results <- meta_sim(
  N_pop = N_pop,
  ts = 0.5,
  tv = 0.1,
  S0 = S0,
  I0 = I0,
  P0 = P0,
  R0 = R0,
  m_weekday_day = contact_matrix,
  m_weekday_night = contact_matrix,
  m_weekend_day = contact_matrix,
  m_weekend_night = contact_matrix,
  delta_t = 0.5,
  vac_mat = vac_mat,
  dv = 365,
  de = 3,
  pea = 0.3,
  dp = 2,
  da = 7,
  ds = 7,
  psr = 0.95,
  dh = 10,
  phr = 0.9,
  dr = 180,
  ve = 0.8,
  nsteps = nsteps,
  is.stoch = FALSE
)

Parse MetaRVM Configuration File

Description

Reads and parses a YAML configuration file for MetaRVM simulations, extracting all necessary parameters for epidemic modeling including population data, disease parameters, mixing matrices, vaccination schedules, and simulation settings.

Usage

parse_config(config_file, return_object = FALSE)

Arguments

Details

The function processes a YAML configuration file with the following main sections:

Simulation Configuration:

• random_seed: Optional random seed for reproducibility in case of stochastic simulations or stochastic parameters

• nsim: Number of simulation instances (default: 1)

• start_date: Simulation start date in MM/DD/YYYY format

• length: Simulation length in days

• checkpoint_dir: Optional checkpoint directory for saving intermediate results

• checkpoint_dates: Optional list of dates to save checkpoints.

• restore_from: Optional path to restore simulation from checkpoint

Population Data:

• mapping: CSV file path containing population mapping with demographic categories age, race, zone. The file must contains columns population_id, age, race, zone, where the population_id is defined using natural numbers.

• initialization: CSV file with initial population states. The file must contains columns population_id, N, S0, I0, V0, R0.

• vaccination: CSV file with vaccination schedule over time. The first column must be dates in MM/DD/YYYY format. The rest of the columns must corresponds to respective subpopulations in the numeric order of population_id.

Mixing Matrices: Contact matrices for different time periods. Each CSV file must have a matrix of order (N_pop x N_pop), where, N_pop is the number of subpopulations. It is assumed that the i-th row and j-th column correspond to i-th and j-th subpopulations.

• weekday_day, weekday_night: Weekday contact patterns

• weekend_day, weekend_night: Weekend contact patterns

Disease Parameters: Epidemiological parameters (can be scalars or distributions):

• ts: Transmission rate for symptomatic individuals

• tv: Transmission rate for vaccinated individuals

• ve: Vaccine effectiveness

• de, dp, da, ds, dh, dr: Duration parameters for different disease states

• pea, psr, phr: Probability parameters for disease transitions

Sub-population Parameters: sub_disease_params allows specification of different parameter values for specific demographic categories (e.g., age groups, races).

The function supports stochastic parameters through distribution specifications with dist, mu, sd, shape, rate, etc.

Value

If return_object = FALSE (default), returns a named list containing:

N_pop

Number of population groups

pop_map

Data.table with population mapping and demographics

S_ini, E_ini, I_asymp_ini, I_presymp_ini, I_symp_ini, H_ini, D_ini, P_ini, V_ini, R_ini

Initial compartment populations

vac_time_id, vac_counts, vac_mat

Vaccination schedule data

m_wd_d, m_wd_n, m_we_d, m_we_n

Contact mixing matrices

ts, tv, ve, dv, de, dp, da, ds, dh, dr, pea, psr, phr

Disease parameter matrices (nsim × N_pop)

start_date

Simulation start date as Date object

sim_length

Simulation length in days

nsim

Number of simulation instances

random_seed

Random seed used (if any)

delta_t

Time step size (fixed at 0.5)

chk_file_names, chk_time_steps, do_chk

Checkpointing configuration

If return_object = TRUE, returns a MetaRVMConfig object with methods for parameter access and validation.

Parameter Distributions

Disease parameters can be specified as distributions for stochastic modeling:

• lognormal: dist: "lognormal", mu: value, sd: value

• gamma: dist: "gamma", shape: value, rate: value

• uniform: dist: "uniform", min: value, max: value

• beta: dist: "beta", shape1: value, shape2: value

• gaussian: dist: "gaussian", mean: value, sd: value

File Requirements

Population mapping file must contain columns:

• population_id: Unique identifier for each population group, natural numbers

• age: Age category (e.g., "0-4", "5-11", "12-17", "18-49", "50-64", "65+")

• race: Race/ethnicity category

• zone: Geographic zone identifier

Population initialization file must contain: N (total population), S0, I0, V0, R0 (initial compartment counts)

Vaccination file must contain: date (MM/DD/YYYY format) and vaccination counts for each population group

Author(s)

Arindam Fadikar

See Also

metaRVM for running simulations with parsed configuration MetaRVMConfig for the configuration object class process_vac_data for vaccination data processing

Examples

options(odin.verbose = FALSE)
example_config <- system.file("extdata", "example_config.yaml", package = "MetaRVM")
# Parse configuration file and return list (backward compatible)
config <- parse_config(example_config)

# Parse and return MetaRVMConfig object for method chaining
config_obj <- parse_config(example_config, return_object = TRUE)

# Access parameters from config object
config_obj$get("N_pop")
config_obj$list_parameters()

# Use with MetaRVM simulation
results <- metaRVM(config_obj)

Read and prepare vaccination data

Description

This function takes the vaccination schedule given by a data.table and prepares it according to the required structure needed in meta_sim() function

Usage

process_vac_data(vac_dt, sim_start_date, sim_length, delta_t)

Arguments

Value

A data.table