superspreading (version 0.3.0)

probability_epidemic: Calculate the probability a disease will cause an outbreak based on R, k and initial cases

Description

Calculates the probability a branching process will cause an epidemic (i.e. probability will fail to go extinct) based on R, k and initial cases.

Usage

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

Value

A value with the probability of a large epidemic.

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")

Kucharski, A. J., Russell, T. W., Diamond, C., Liu, Y., Edmunds, J., Funk, S. & Eggo, R. M. (2020). Early dynamics of transmission and control of COVID-19: a mathematical modelling study. The Lancet Infectious Diseases, 20(5), 553-558. tools:::Rd_expr_doi("10.1016/S1473-3099(20)30144-4")

See Also

probability_extinct()

Examples

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