probability_extinct: Calculate the probability a branching process will go extinct based on R, k and initial cases

Description

Calculates the probability a branching process will not causes an epidemic and will go extinct. This is the complement of the probability of a disease causing an epidemic (probability_epidemic()).

Usage

probability_extinct(
  R,
  k,
  num_init_infect,
  ind_control = 0,
  pop_control = 0,
  ...,
  offspring_dist
)

Value

A value with the probability of going extinct.

Arguments

R: A number specifying the R parameter (i.e. average secondary cases per infectious individual).
k: A number specifying the k parameter (i.e. overdispersion in offspring distribution from fitted negative binomial).
num_init_infect: An integer (or at least "integerish" if stored as double) specifying the number of initial infections.
ind_control: A numeric specifying the strength of individual-level control measures. These control measures assume that infected individuals do not produce any secondary infections with probability ind_control, thus increasing the proportion of cases that do not create any subsequent infections. The control measure is between 0 (default) and 1 (maximum).
pop_control: A numeric specifying the strength of population-level control measures that reduce the transmissibility of all cases by a constant factor. Between 0 (default) and 1 (maximum).
...: <dynamic-dots> Named elements to replace default optimisation settings. Currently only "fit_method" is accepted and can be either "optim" (default) or "grid" for numerical optimisation routine or grid search, respectively.
offspring_dist: An <epiparameter> object. An S3 class for working with epidemiological parameters/distributions, see epiparameter::epiparameter().

References

Lloyd-Smith, J. O., Schreiber, S. J., Kopp, P. E., & Getz, W. M. (2005). Superspreading and the effect of individual variation on disease emergence. Nature, 438(7066), 355-359. tools:::Rd_expr_doi("10.1038/nature04153")

Examples

Run this code

probability_extinct(R = 1.5, k = 0.1, num_init_infect = 10)