safir: Individual-Based Model of COVID Transmission

Notes

If we need to include onward infectiousness as something dependent upon ab titre for breakthrough infections, it will need to interact with this:

# Group infection by age
ages <- variables$discrete_age$get_values(infectious)
inf_ages <- tab_bins(a = ages, nbins = parameters$N_age)

If it can be turned into a vector of values of relative infectiousness the same length as ages then we just need to make a modified tab_bins that handles that relative multiplicative contribution of each individual.

One thing to note is that when a large amount of doses are available from the start of the simulation there may be wasted doses during the lag between people getting their first dose, and when persons can get (become eligible for) their second dose. The prioritization step will only increment when both are fulfilled.
one way to include additional variation in inter-dose times is to include variance in dose_period used to calculate thresholds in threshold <- timestep - as.integer(parameters$dose_period[dose]/dt) from function distribution_vaccines.R/get_current_eligible_from_coverage.
including multiple vaccine types could be as simple as just finding out what proportion of doses belong to each type per time step and draw the ab titre from the appropriate parameter.