---
title: "Run Multiple"
author:
  - Derek Meyer
  - George Vega Yon
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Run Multiple}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>", out.width = "80%", fig.width = 7, fig.height = 5,
  fig.align = "center"
)
```

# Introduction
The purpose of the "run_multiple" function is to run a specified number of
simulations using the same model object. That is, this function makes it
possible to compare model results across several separate and repeated
simulations.

# Example: Simulating a SEIRCONN Model 50 Times

## Setup and Running Model

To use the "run_multiple" function in epiworld, create your epimodel of choice;
in this case, the example uses a SEIRCONN model for COVID-19, 100000 people,
an initial prevalence of 0.0001 (0.01%), a contact rate of 2, probability of
transmission 0.5, a total of 7 incubation days, and probability of recovery
$\frac{1}{3}$.

```{r sirconn-setup}
library(epiworldR)
model_seirconn <- ModelSEIRCONN(
  name              = "COVID-19",
  n                 = 10000,
  prevalence        = 0.0001,
  contact_rate      = 2,
  transmission_rate = 0.5,
  incubation_days   = 7,
  recovery_rate     = 1 / 3
)
```

## Generating a Saver

Next, generate a saver for the purpose of extracting the "total_hist" and
"reproductive" information from the model object. Now, use the
"run_multiple" function with the model object, number of desired days to run
the simulation, number of simulations to run, and number of threads for
parallel computing.

```{r saver-generation}
# Generating a saver
saver <- make_saver("total_hist", "reproductive")
# Running and printing
run_multiple(model_seirconn, ndays = 50, nsims = 50, saver = saver, nthreads = 2)
```

Using the "`run_multiple_get_results`" function, extract the results from the
model object that was simulated 50 times for comparison across simulations.

```{r retrieving results}
# Retrieving the results
ans <- run_multiple_get_results(model_seirconn, nthreads = 2)
head(ans$total_hist)
head(ans$reproductive)
```

## Plotting

To plot the epicurves and reproductive numbers over time using boxplots, extract
the results from the model object using "`ans`". For this
example, the dates are filtered to be less than or equal to 20 to observe the
epicurves in the first 20 days. Notice each boxplot in the below table
represents the observed values from each of the 50 simulations for each date.

```{r plotting seirconn epicurves}
seirconn_50 <- ans$total_hist
seirconn_50 <- seirconn_50[seirconn_50$date <= 20, ]
plot(seirconn_50)
```

To view the a plot of the reproductive number over all 50 days for each of the
50 simulations, store the reproductive results to a new object using
"`ans`", then plot using the "boxplot" function. Notice
each source exposure date displays a boxplot representing the distribution
of reproductive numbers across all 50 simulations. As expected, the reproductive
number on average, decreases over time.

```{r reproductive number plot}
seirconn_50_r <- ans$reproductive
plot(seirconn_50_r)
# boxplot(rt ~ source_exposure_date, data = seirconn_50_r,
#         main = "Reproductive Number",
#         xlab = "Source Exposure Date",
#         ylab = "rt",
#         border = "black",
#         las = 2)
```
