---
title: "Getting Started with MetaRVM"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Getting Started with MetaRVM}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 5
)
```

## 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:

```{r eval=FALSE}
# install.packages("devtools")
devtools::install_github("RESUME-Epi/MetaRVM")
```

## Loading the Package

```{r setup, eval=FALSE}
library(MetaRVM)
library(ggplot2)
options(odin.verbose = FALSE)
```

## 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.

```{r}
# Locate the example YAML configuration file
yaml_file <- system.file("extdata", "example_config.yaml", package = "MetaRVM")
print(yaml_file)
```

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.

```yaml
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 `yaml-configuration.html` vignette.

## Running the Simulation

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

```{r, results='hide'}
# Load the metaRVM library
library(MetaRVM)

# Run the simulation
sim_out <- metaRVM(yaml_file)
```

```{r}
print(sim_out)
head(sim_out$results)
```

For more details on running `metaRVM`, refer to the `running-a-simulation.html` vignette.