---
title: Post-process data in a trajectory
author:
  Stefan Widgren
  <a href="https://orcid.org/0000-0001-5745-2284">
    <img src="https://info.orcid.org/wp-content/uploads/2019/11/orcid_16x16.png"
         alt="ORCID logo"
         width="16"
         height="16"
         style="border-style:none;" />
  </a>
output:
  html_vignette:
    toc: true
    toc_depth: 3
vignette: >
  %\VignetteIndexEntry{Post-process data in a trajectory}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

After a model is created, a simulation is started with a call to the
`run()` function with the model as the first argument. The function
returns a modified model object with a single stochastic solution
trajectory attached to it. Trajectory data contains the state of each
compartment, recorded at every time-point in `tspan`. This document
introduces you to functionality in `SimInf` to post-process and
explore that trajectory data.

## Extract trajectory data with `trajectory()`

Most modelling and simulation studies require custom data analysis
once the simulation data has been generated.  To support this, SimInf
provides the `trajectory()` method to obtain a `data.frame` with the
number of individuals in each compartment at the time points specified
in `tspan`.

Let's simulate 10 days of data from an SIR model with 6 nodes. For
reproducibility, we first call the `set.seed()` function and specify
the number of threads to use for the simulation.

```{r}
library(SimInf)

set.seed(123)
set_num_threads(1)

u0 <- data.frame(S = c(100, 101, 102, 103, 104, 105),
                 I = c(1, 2, 3, 4, 5, 6),
                 R = c(0, 0, 0, 0, 0, 0))

model  <- SIR(u0 = u0,
              tspan = 1:10,
              beta = 0.16,
              gamma = 0.077)

result <- run(model)
```

Extract the number of individuals in each compartment at the
time-points in `tspan`.

```{r}
trajectory(result)
```

Extract the number of recovered individuals in the first node.

```{r}
trajectory(result, compartments = "R", index = 1)
```

Extract the number of recovered individuals in the first and third
node.

```{r}
trajectory(result, compartments = "R", index = c(1, 3))
```

Consult the help page for other `trajectory()` parameter options.

## Calculate prevalence from a trajectory using `prevalence()`

Use the `prevalence` function to calculate the proportion of
individuals with disease in the population.  The `prevalence()`
function takes a model object and a formula specification, where the
left-hand-side of the formula specifies the compartments representing
cases i.e. have an attribute or a disease. The right-hand-side of the
formula specifies the compartments at risk.

Let's use the previously simulated data and determine the proportion
of infected individuals in the population at the time-points in
`tspan`.

```{r}
prevalence(result, I ~ S + I + R)
```

Identical result is obtained with the shorthand 'I ~ .'

```{r}
prevalence(result, I ~ .)
```

The prevalence function has an argument `level` which has a default
`level = 1`. This returns the prevalence at the whole population
level. Since we have several nodes (farms if you like) in the model
now, we can also ask for the proportion of nodes with infected
individuals by specifying `level = 2`.

```{r}
prevalence(result, I ~ S + I + R, level = 2)
```

Finally, we may wish to know the proportion of infected individuals
within each node with `level = 3`.

```{r}
prevalence(result, I ~ S + I + R, level = 3)
```

Consult the help page for other `prevalence()` parameter options.

## Visualize a trajectory with `plot()`

The `plot()` function is another useful way of inspecting the outcome
of a trajectory.  It can display either the median and the quantile
range of the counts in all nodes, plot the counts in specified nodes,
or the prevalence. Below are some examples of using the `plot()`
function.

Plot the median and interquartile range of the number of susceptible,
infected and recovered individuals.

```{r, fig.width=7, fig.height=4, fig.align="left"}
plot(result)
```

Plot the median and the middle 95\% quantile range of the number of
susceptible, infected and recovered individuals.

```{r, fig.width=7, fig.height=4, fig.align="left"}
plot(result, range = 0.95)
```

Plot the median and interquartile range of the number of infected
individuals.

```{r, fig.width=7, fig.height=4, fig.align="left"}
plot(result, "I")
```

Use the formula notation instead to plot the median and interquartile
range of the number of infected individuals.

```{r, fig.width=7, fig.height=4, fig.align="left"}
plot(result, ~I)
```

Plot the number of susceptible, infected and recovered individuals in
the first three nodes.

```{r, fig.width=7, fig.height=4, fig.align="left"}
plot(result, index = 1:3, range = FALSE)
```

Use plot type line instead.

```{r, fig.width=7, fig.height=4, fig.align="left"}
plot(result, index = 1:3, range = FALSE, type = "l")
```

Plot the number of infected individuals in the first node.

```{r, fig.width=7, fig.height=4, fig.align="left"}
plot(result, "I", index = 1, range = FALSE)
```

Plot the proportion of infected individuals (cases) in the population.

```{r, fig.width=7, fig.height=4, fig.align="left"}
plot(result, I ~ S + I + R)
```

Plot the proportion of nodes with infected individuals.

```{r, fig.width=7, fig.height=4, fig.align="left"}
plot(result, I ~ S + I + R, level = 2)
```

Plot the median and interquartile range of the proportion of infected
individuals in each node

```{r, fig.width=7, fig.height=4, fig.align="left"}
plot(result, I ~ S + I + R, level = 3)
```

Plot the proportion of infected individuals in the first three nodes.

```{r, fig.width=7, fig.height=4, fig.align="left"}
plot(result, I ~ S + I + R, level = 3, index = 1:3, range = FALSE)
```

Please run a couple of `plot(run(model))` to view the stochasticity
between trajectories. To find help on the SimInf plot function for the
model object run:

```{r, eval=FALSE}
help("plot,SimInf_model-method", package = "SimInf")
```
