[Feat] Built-in parameter distributions and estimates from literature

[quantum9Innovation]

Is your feature request related to a problem? Please describe.
This feature is not related to any problem, but it is inspired by epiforcasts/EpiNow2

Describe the solution you'd like
Epispot should include a handful of parameter estimates from literature to aid with the creation of new models. Additionally, epispot should come with built in random modeling of various parameter distributions which can either be selected manually or automatically in the case that the user has not passed in a function for a specific parameter.

Describe alternatives you've considered
The alternative of not including this would complicate model creation greatly as there are often many, many parameters (exponentially many so as more compartments are added). Therefore, it would be helpful to have some kind of "filler" parameter distribution for unlabeled parameters. Additionally, by adding literature estimates, we can fit COVID-19 trends better using vetted sources rather than tweaking parameters to find an exact match for the data.

Additional context
No testing is expected to happen before this code is pushed to production

Current progress:

• Parameter distributions
  • Gamma (γ)
  • R Naught ($$ R_0 $$)
  • Total population (N)
• Estimates from literature #135
• Testing of params.py and use in model objects #134

[quantum9Innovation]

/claim

[github-actions]

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[quantum9Innovation]

Ready for the PR (all remaining items we aim to finish by the release of v3, though these can always be postponed to minor updates instead)