## 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_reproductivenumber.R`. You can call this function directly, without going through the shiny app. Type `?simulate_reproductivenumber` 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 information on the reproductive number, see e.g. (Fine, Eames, and Heymann 2011, Heffernan, Smith, and Wahl (2005), Roberts (2007), Wallinga and Lipsitch (2007)).

### References

Fine, Paul, Ken Eames, and David L Heymann. 2011. “‘Herd Immunity’: A Rough Guide.” Clinical Infectious Diseases : An Official Publication of the Infectious Diseases Society of America 52 (7): 911–16. doi:10.1093/cid/cir007.

Heffernan, J M, R J Smith, and L M Wahl. 2005. “Perspectives on the Basic Reproductive Ratio.” Journal of the Royal Society, Interface 2 (4): 281–93. doi:10.1098/rsif.2005.0042.

Roberts, M G. 2007. “The Pluses and Minuses of R0.” Journal of the Royal Society, Interface 4 (16): 949–61. doi:10.1098/rsif.2007.1031.

Wallinga, J., and M. Lipsitch. 2007. “How generation intervals shape the relationship between growth rates and reproductive numbers.” Proceedings of the Royal Society B 274 (1609): 599–604.