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Getting Started with MetaRVM

Source: vignettes/getting-started.Rmd
getting-started.Rmd

Introduction

MetaRVM is a comprehensive R package for simulating respiratory virus epidemics using meta-population compartmental models. This vignette will guide you through the basic usage of the package.

Installation

You can install the development version of MetaRVM from GitHub:

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

Loading the Package

Basic Example

This example shows how to run a basic meta-population simulation.

The metaRVM package includes a set of example files in its extdata directory. To run the example, we first need to locate these files. The system.file() function in R is the recommended way to do this, as it will find the files wherever the package is installed.

# Locate the example YAML configuration file
yaml_file <- system.file("extdata", "example_config.yaml", package = "MetaRVM")
print(yaml_file)
#> [1] "/tmp/RtmpEkEVxi/temp_libpath24fd7163cb6ff9/MetaRVM/extdata/example_config.yaml"

The yaml_file variable now holds the full path to the example configuration file. This file is set up to use the other example data files (also in the extdata directory) with relative paths. Below is the content of the yaml file.

run_id: ExampleRun
population_data:
  mapping: demographic_mapping_n24.csv
  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
  tv: 0.25
  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: 01/01/2023 # m/d/Y
  length: 150
  nsim: 1

For a detailed explanation of all the configuration options, please see the Model Configurations vignette.

Running the Simulation

Once we have the path to the configuration file, the simulation can be run using the metaRVM() function.

# Load the metaRVM library
library(MetaRVM)

# Run the simulation
sim_out <- metaRVM(yaml_file)
#> Loading required namespace: pkgbuild
#> Generating model in c
#> ℹ Re-compiling odin62a63f38 (debug build)
#> ℹ Loading odin62a63f38
print(sim_out)
#> MetaRVM Results Object
#> =====================
#> Instances: 1 
#> Populations: 24 
#> Date range: 2023-10-01 to 2024-02-27 
#> Total observations: 111600 
#> Disease states: D, E, H, I_all, I_asymp, I_eff, I_presymp, I_symp, P, R, S, V, cum_V, mob_pop, n_EI, n_EIpresymp, n_HD, n_HR, n_HRD, n_IasympR, n_IsympH, n_IsympR, n_IsympRH, n_SE, n_SV, n_VE, n_VS, n_preIsymp, p_HRD, p_SE, p_VE
head(sim_out$results)
#>          date    age   race   zone disease_state        value instance
#>        <Date> <char> <char> <char>        <char>        <num>    <int>
#> 1: 2023-10-01   0-17      A     11             D 2.252583e-04        1
#> 2: 2023-10-01   0-17      A     11             E 1.305178e+01        1
#> 3: 2023-10-01   0-17      A     11             H 2.304447e-01        1
#> 4: 2023-10-01   0-17      A     11         I_all 2.731688e+01        1
#> 5: 2023-10-01   0-17      A     11       I_asymp 3.227854e-01        1
#> 6: 2023-10-01   0-17      A     11         I_eff 2.647304e+01        1

For more details on running metaRVM, refer to the Running Simulation vignette.