add in individual-level variation in infectiousness
Nobody has claimed this yet.
Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 28/100
- Issue type
- Feature
- Clarity
- Mostly clear
- Activity status
- Stale
- Tech stack
- r
- Domain
- machine-learning
Research direction
Start by tracing how the model assigns β to individuals and identify the relevant simulation code and tests; the issue does not name specific files. Explore drawing individual infectiousness values from a log-normal distribution centered around 1, then check that the model uses those values and that the distribution has the intended relative variation. The issue leaves the distribution open to alternatives, so the modeling choice needs maintainer input.
Written by the indexing model from the issue text.
Description
Currently all individuals implicitly have the same infectiousness in our model (i.e. the same β) - we should add some individual-level variation on top of this.
Anticipate this will look something like a draw from a lognormal distribution with a mean centred around 1.
rlnorm(n, meanlog = 0, sdlog = 1)
Log-normal selected as it approximately preserves the amount of mass either side of 1 on the relative scale (e.g. p(X<0.5) = p(X>2). Open to other suggestions though.
- Dominant language
- R
- Stars
- 8
- Forks
- 4
- PR merge metrics
- No merged PRs in 30d
Getting set up
This project ships no dev container, Dockerfile or contributing guide, so setting up is up to you: start from its README, and see our first-contribution guide for the general steps.
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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