added experiment back into vignette

.gitignore

@@ -16,6 +16,7 @@
 # produced vignettes
 vignettes/*.html
 vignettes/*.pdf
+vignettes/sirplus-logo.png
 # OAuth2 token, see https://github.com/hadley/httr/releases/tag/v0.3
 .httr-oauth
 # knitr and R markdown default cache directories

R/results-parse.R

@@ -158,7 +158,6 @@ plot_models <- function(sims = baseline_sim,
                                    linetype = sim)) + 
                 facet_grid(reo_exp(experiment) ~ .) + 
                 geom_line(size = 1.5, alpha = 0.8) + 
-                scale_linetype_manual(values = complines) + 
                 scale_colour_manual(values = compcols, labels = complabels) + 
                 labs(title = plot_title, x = x_axis, y = "Prevalence") +
                 theme_bw() 

vignettes/sirplus_intro.Rmd

@@ -40,19 +40,23 @@ that are associated with them see Tim Churches [blog post](https://timchurches.g
 
 
 ```{r Load package}
+#library(sirplus)
 devtools::load_all(".")
 ```
 
-## Simulate baseline model
+## Simulate and Inspect a Baseline sirplus Model
+
+### Simulate 
 
 Here we will simulate the epi data for a made up population with 1000 
 susceptible individuals, 50 that are infected but not in the hospital or in 
 self-quarentine (maybe people that are infected/symptomatic but not tested/
 aware), 10 confirmed cases that have self-isolated, and 1 confirmed case that
-has been hospitalized. 
+has been hospitalized. We call this the baseline model because it uses 
+default parameters for disease spread (i.e. no additional interventions).
 
 ```{r simulate baselines}  
-s.num <- 1000  # number susceptible
+s.num <- 2000  # number susceptible
 i.num <- 50  # number infected 
 q.num <- 10  # number in self-isolation
 h.num <- 1  # number in the hospital
@@ -64,7 +68,7 @@ head(baseline_sim$df)
 ```
 
 
-## Inspect Transition Distributions
+### Inspect Baseline Transition Distributions
 
 The sirplus model controls transitions between compartments using a variety of 
 transition parameters. You can use the `get_times()` and `plot_times()` 
@@ -73,17 +77,54 @@ on these parameters.
 
 ```{r baseline sims}
 times <- get_times(baseline_sim)
-plot(plot_times(times))
+plot_times(times)
 ```
 
 
-## Plot the SIR-plus results
+### Plot Baseline sirplus Results
+
+To visualise your sirplus model, plot the change in prevalence (i.e. people) 
+over time in each compartment. 
 
 ```{r viz prevalence}
-plot(plot_models(sims = baseline_sim,
-                 time_lim = 50,
-                 start_date = lubridate::ymd("2020-01-01"),
-                 comp_remove = c('s.num', 'r.num'),
-                 plot_title = 'Baseline Model'))
+plot_models(sims = baseline_sim,
+             time_lim = 50,
+             start_date = lubridate::ymd("2020-01-01"),
+             comp_remove = c('s.num', 'r.num'),
+             plot_title = 'Baseline Model')
+```
+
+
+## Run an experiment
+
+With the sirplus package you can also set up experiments. For example, lets say
+that one week after the beginning of the epidemic, schools and non-essential
+buisnesses are closed to encourage social distancing. We can model the impact 
+that policy will have and compare it to our baseline by ramping down the 
+act.rate.e (# of exposure events, or acts, between individuals in the E and S 
+compartment, per day) after 7 days and plot this model next to our baseline.
+
+
+```{r experiment 1}
+closures_RampOnday7 <- function(t) {
+    ifelse(t < 7, 10, ifelse(t <= 14, 10 - (t-7)*(10 - 5)/7, 5))
+}
+
+closures_sim <- simulate.seiqhrf(s.num = s.num, i.num = i.num, 
+                                 q.num = q.num, h.num = h.num,
+                                 act.rate.e = closures_RampOnday7(1:366),
+                                 act.rate.i = closures_RampOnday7(1:366))
+
+plot_models(sims = c(baseline_sim, closures_sim),
+            sim_id = c('Baseline', 'Closures (d14)'),
+            time_lim = 50,
+            start_date = lubridate::ymd("2020-01-01"),
+            comp_remove = c('s.num', 'r.num'),
+            plot_title = 'Closures Experiment')
+
 ```
 
+From these results we see that this policy would likey reduce the peak number
+of infections from 700 to 500 and would flatten the curve for infections and 
+thus hospitalizations. 
+