add improvements

- [ ] when refering to [R0](https://epiverse-trace.github.io/tutorials-late/simulating-transmission.html#the-basic-reproduction-number-r_0) add link to https://www.nature.com/articles/s41592-020-0822-z for an explanation
- [ ] add summary table about intervention objects (pasted below)
- [ ] move [normalization](https://epiverse-trace.github.io/tutorials-late/simulating-transmission.html#headingSpoiler2) to end of episode
- [ ] add reference about knowledge dependencies and writing clarity in episode on https://epiverse-trace.github.io/tutorials-late/contact-normalization.html
- [ ] add https://www.nature.com/articles/s41592-020-0822-z and http://prism.edu.au/publications/prism-modeling-guideline/ according to the time before reading episode on simulating tranmission

```
## **Summary**: Intervention objects

In {epidemics} different interventions require different functions, arguments, and R class objects: **integers**, **matrices**, or **lists**.

| Intervention | Function | type | reduction / nu | Argument in model_default() |
|-------------------|--------------------|--------------|--------------------------------------------|--------------------------------------------------------------|
| Reduce rate | intervention() | *"rate"* | reduction = 0.163 | intervention = list(*transmission_rate* = \) |
| Reduce contacts | intervention() | *"contacts"* | reduction = **matrix**(c(0.5, 0.01, 0.01)) | intervention = list(*contacts* = \) |
| Vaccination | ***vaccination()*** | ------------ | nu = **matrix**(c(0.0006, 0.004, 0.02)) | ***vaccination*** = \ |

The **list()** class object helps accepting multiple **consecutive** or **overlapping** interventions in the same simulation.
```

Start with the linked simulating-transmission and contact-normalization episodes, locating the R0 references, normalization section, and reading-order material. Add the two references, the intervention-objects summary table, and the knowledge-dependencies reference, then verify every checklist item is reflected in the rendered tutorials.

Written by the indexing model from the issue text.