---
title: "sirPLUS models"
date: "Last updated: 23 March 2020"
output:
    BiocStyle::html_document:
        toc: true
        toc_float: true
vignette: >
  %\VignetteIndexEntry{sirPLUS models}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

## Backgrouund on the SEIQHRF (SIR + extra compartments) model

Testing implementation of SEIQHRF (see [Tim Churches"'" blog post](https://timchurches.github.io/blog/posts/2020-03-18-modelling-the-effects-of-public-health-interventions-on-covid-19-transmission-part-2/) in an R package.

| Compartment | Functional definition                                                             |
|-------------|-----------------------------------------------------------------------------------|
| S           | Susceptible individuals                                                           |
| E           | Exposed **and** infected, not yet symptomatic but potentially infectious          |
| I           | Infected, symptomatic **and** infectious                                          |
| Q           | Infectious, but (self-)isolated                                                |
| H           | Requiring hospitalisation (would normally be hospitalised if capacity available) |
| R           | Recovered, immune from further infection                                          |
| F           | Case fatality (death due to COVID-19, not other causes)                           |


```{r Load package}
library(earlyR, lib.loc = '/mnt/mcfiles/rlyu/Software/R/3.6/Rlib')
library(epitrix, lib.loc = '/mnt/mcfiles/rlyu/Software/R/3.6/Rlib')
devtools::load_all(".")
```

## Simulate baseline model

```{r simulate baselines}  
control <- set.control()
param <- set.param()
init <- set.init(s.num = 10000, i.num = 10, q.num = 9, h.num = 1)

baseline_sim <- simulate(param, init, control)
```


## Inspect baseline simulations

```{r baseline sims}
times <- get_times(baseline_sim)

times %>% filter(duration <= 30) %>% ggplot(aes(x = duration)) + 
    geom_bar() + facet_grid(period_type ~ ., scales = "free_y") + 
    labs(title = "Duration frequency distributions", subtitle = "Baseline simulation")
```



```{r viz prevalence}
baseline_plot_df <- baseline_sim$df %>% # use only the prevalence columns
select(time, s.num, e.num, i.num, q.num, h.num, r.num, f.num) %>% 
    # examine only the first 100 days since it is all over by
# then using the default parameters
filter(time <= 100) %>% pivot_longer(-c(time), names_to = "compartment", 
    values_to = "count")

# define a standard set of colours to represent compartments
compcols <- c(s.num = "yellow", e.num = "orange", i.num = "red", 
    q.num = "cyan", h.num = "magenta", r.num = "lightgreen", 
    f.num = "black")
complabels <- c(s.num = "Susceptible", e.num = "Infected/asymptomatic", 
    i.num = "Infected/infectious", q.num = "Self-isolated", h.num = "Requires hospitalisation", 
    r.num = "Recovered", f.num = "Case fatality")

baseline_plot_df %>% ggplot(aes(x = time, y = count, colour = compartment)) + 
    geom_line(size = 2, alpha = 0.7) + scale_colour_manual(values = compcols, 
    labels = complabels) + theme_dark() + labs(title = "Baseline simulation", 
    x = "Days since beginning of epidemic", y = "Prevalence (persons)")
```
## Experiment 1


