## Further Information

- This app (and all others) are structured such that the Shiny part (the graphical interface you are currently using and the server-side function that goes with it) calls an underlying R script which runs the simulation for the model of interest and returns the results.
- For this app, the underlying function running the simulation is called `simulate_stochastic.R`. You can call this function directly, without going through the shiny app. Type `?simulate_stochastic` into the R console for more information (you need to exit the graphical interface first or start a new R session). If you go that route, you need to use the results returned from this function and produce useful output (such as a plot) yourself.
- You could also modify this R function for your own purposes - though that requires some more R coding knowledge.
- To get a quick start explaining how to interact with the simulator functions directly, read the vignette for the package (type `vignette('DSAIDE')` into the R console).
- For some further reading concerning stochastic dynamics of ID, see e.g. (Lloyd-Smith et al. 2005, Black (1966), Keeling (1997)).

### References

Black, F L. 1966. “Measles Endemicity in Insular Populations: Critical Community Size and Its Evolutionary Implication.” Journal of Theoretical Biology 11 (2): 207–11.

Keeling, M J. 1997. “Modelling the Persistence of Measles.” Trends in Microbiology 5 (12): 513–18. doi:10.1016/S0966-842X(97)01147-5.

Lloyd-Smith, James O, Paul C Cross, Cheryl J Briggs, Matt Daugherty, Wayne M Getz, John Latto, Maria S Sanchez, Adam B Smith, and Andrea Swei. 2005. “Should We Expect Population Thresholds for Wildlife Disease?” Trends in Ecology & Evolution 20 (9): 511–19. doi:10.1016/j.tree.2005.07.004.