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Estimate constant seeding infections

Source: R/model_infections.R
seeding_estimate_constant.Rd

This option estimates a constant number of infections at the start of the modeled time period.

Usage

seeding_estimate_constant(
  intercept_prior_q5 = NULL,
  intercept_prior_q95 = NULL,
  modeldata = modeldata_init()
)

Arguments

intercept_prior_q5

Prior (5% quantile) on the initial number of infections. Can be interpreted as an approximate lower bound. If NULL (default), this is computed from a crude empirical estimate of the number of cases (see details).

intercept_prior_q95

Prior (95% quantile) on the initial number of infections. Can be interpreted as an approximate upper bound. If NULL (default), this is computed from a crude empirical estimate of the number of cases (see details).

modeldata

A modeldata object to which the above model specifications should be added. Default is an empty model given by modeldata_init(). Can also be an already partly specified model returned by other EpiSewer modeling functions.

Value

A modeldata object containing data and specifications of the model to be fitted. Can be passed on to other EpiSewer modeling functions to add further data and model specifications.

The modeldata object also includes information about parameter initialization (init), meta data (.metainfo), and checks to be performed before model fitting (.checks).

Details

The seeding phase has the length of the maximum generation time (during this time, the renewal model cannot be applied). It is here assumed that the expected number of new infections stays constant over this time period. This assumption can however be violated: While traditionally, seeding refers to the first few (potentially imported) infections of an epidemic, depending on what time period the model is fitted to, it may also cover a different phase with stronger growth dynamics. Thus, if your data starts in the middle of an epidemic wave, it is recommended to use seeding_estimate_rw() instead of seeding_estimate_constant().

If intercept_prior_q5 or intercept_prior_q95 are not specified by the user, EpiSewer will compute a rough median empirical estimate of the number of cases using the supplied wastewater measurements and shedding assumptions, and then infer the missing quantiles based on this. If none of the quantiles are provided, they are set to be roughly 1/10 and 10 times the empirical median estimate. We note that this is a violation of Bayesian principles (data must not be used to inform priors) - but a neglectable one, since it only ensures that the seeding is modeled on the right order of magnitude and does not have relevant impacts on later Rt estimates.

The priors of this component have the following functional form:

  • initial number of infections (log scale): Normal