print summary table (#70) and use it for threshold

episodes/superspreading-simulate.Rmd

@@ -571,6 +571,8 @@ sim_chains_max <-
  cases_total = max(cases_cumsum)
  ) %>%
  ungroup()
+
+sim_chains_max
 ```
 
 Now, we are prepared for using the `{ggplot2}` package:
@@ -635,18 +637,13 @@ chains_null <- sim_chains_max %>%
 
 Although most introductions of `r initial_cases` index case do not generate secondary cases (N = `r chains_null`) or most outbreaks rapidly become extinct (median duration of `r chains_extinct$extinct_duration_median` and median size of `r chains_extinct$extinct_size_median`), only `r threshhold_summary$chains_theshold` epidemic trajectories among `r threshhold_summary$chains_number` simulations (`r threshhold_summary$chains_percentage`%) can reach to more than 100 infected cases. This finding is particularly remarkable because the reproduction number $R$ is less than 1 (offspring distribution mean of `r mers_offspring[["mean"]]`), but, given an offspring distribution dispersion parameter of `r mers_offspring[["dispersion"]]`, it shows the potential for explosive outbreaks of MERS disease.
 
-For a summary of the total time duration and size of each chain, we can use the `{dplyr}` "combo" of `group_by()` and `summarise()`:
+We can count how many chains reached the 100-case threshold using `{dplyr}` functions:
 
-```{r,eval=FALSE}
-simulated_chains_day %>%
- group_by(chain_id) %>%
- summarise(
- # duration
- day_max = max(day),
- # size
- cases_total = max(cases_cumsum)
- ) %>%
- ungroup()
+```{r}
+# number of chains that reached the 100-case threshold
+sim_chains_max %>%
+ arrange(desc(day_max)) %>%
+ filter(cases_total > 100)
 ```