Simulate contacts for an infectious disease outbreak

Usage

sim_contacts(
  contact_distribution = function(x) stats::dpois(x = x, lambda = 2),
  infectious_period = function(n) stats::rlnorm(n = n, meanlog = 2, sdlog = 0.5),
  prob_infection = 0.5,
  outbreak_start_date = as.Date("2023-01-01"),
  anonymise = FALSE,
  outbreak_size = c(10, 10000),
  population_age = c(1, 90),
  contact_tracing_status_probs = c(under_followup = 0.7, lost_to_followup = 0.2, unknown
    = 0.1),
  config = create_config()
)

Arguments

contact_distribution

A function or an <epiparameter> object to generate the number of contacts per infection.

The function can be defined or anonymous. The function must have a single argument in the form of an integer vector with elements representing the number of contacts, and return a numeric vector where each element corresponds to the probability of observing the number of contacts in the vector passed to the function. The index of the numeric vector returned is offset by one to the corresponding probability of observing the number of contacts, i.e. the first element of the output vector is the probability of observing zero contacts, the second element is the probability of observing one contact, etc.

An <epiparameter> can be provided. This will be converted into a probability mass function internally.

The default is an anonymous function with a Poisson probability mass function (dpois()) with a mean (\(\lambda\)) of 2 contacts per infection.

infectious_period

A function or an <epiparameter> object for the infectious period. This defines the duration from becoming infectious to no longer infectious. In the simulation, individuals are assumed to become infectious immediately after being infected (the latency period is assumed to be zero). The time intervals between an infected individual and their contacts are assumed to be uniformly distributed within the infectious period. Infectious periods must be strictly positive.

The function can be defined or anonymous. The function must return a vector of randomly generated real numbers representing sampled infectious periods. The function must have a single argument, the number of random infectious periods to generate.

An <epiparameter> can be provided. This will be converted into random number generator internally.

The default is an anonymous function with a lognormal distribution random number generator (rlnorm()) with meanlog = 2 and sdlog = 0.5.

prob_infection

A single numeric for the probability of a secondary contact being infected by an infected primary contact.

outbreak_start_date

A date for the start of the outbreak.

anonymise

A logical boolean for whether case names should be anonymised. Default is FALSE.

outbreak_size

A numeric vector of length 2 defining the minimum and the maximum number of infected individuals for the simulated outbreak. Default is c(10, 1e4), so the minimum outbreak size is 10 infected individuals, and the maximum outbreak size is 10,000 infected individuals. Either number can be changed to increase or decrease the maximum or minimum outbreak size to allow simulating larger or smaller outbreaks. If the minimum outbreak size cannot be reached after running the simulation for many iterations (internally) then the function errors, whereas if the maximum outbreak size is exceeded the function returns the data early and a warning stating how many cases and contacts are returned.

population_age

Either a numeric vector with two elements or a <data.frame> with age structure in the population. Use a numeric vector to specific the age range of the population, the first element is the lower bound for the age range, and and the second is the upper bound for the age range (both inclusive, i.e. [lower, upper]). The <data.frame> with age groups and the proportion of the population in that group. See details and examples for more information.

contact_tracing_status_probs

A named numeric vector with the probability of each contact tracing status. The names of the vector must be "under_followup", "lost_to_followup", "unknown". Values of each contact tracing status must sum to one.

config

A list of settings to adjust the randomly sampled delays and Ct values. See create_config() for more information.

Value

A contacts <data.frame>.

The structure of the output is:

from: character column with name of case.
to: character column with name of contacts of case.
age: integer with age of infectee.
sex: character column with either "m" or "f" for the sex of the contact.
date_first_contact: <Date> column for the first contact between case and contacts.
date_last_contact: <Date> column for the last contact between case and contacts.
was_case: logical boolean column with either TRUE or FALSE for if the contact becomes a case.
status: character column with the status of each contact. By default it is either "case", "under_followup" "lost_to_followup", or "unknown".

Author

Joshua W. Lambert, Carmen Tamayo

Examples

# quickly simulate contact tracing data using the function defaults
contacts <- sim_contacts()
#> Warning: Number of cases exceeds maximum outbreak size. 
#> Returning data early with 10152 cases and 20191 total contacts (including cases).
head(contacts)
#>               from                to age sex date_first_contact
#> 1 Arshad el-Hannan    Stephanie Phan  85   f         2022-12-31
#> 2 Arshad el-Hannan    Bryan Williams  10   m         2022-12-26
#> 3   Stephanie Phan Harrison Gonzales  25   f         2023-01-10
#> 4   Stephanie Phan    Jordan Pittman  17   m         2023-01-09
#> 5   Stephanie Phan  Shadhaa al-Jamal  47   f         2023-01-09
#> 6   Stephanie Phan    Sara Rodriguez  48   f         2023-01-10
#>   date_last_contact was_case         status
#> 1        2023-01-04     TRUE           case
#> 2        2023-01-03     TRUE           case
#> 3        2023-01-15     TRUE           case
#> 4        2023-01-15    FALSE under_followup
#> 5        2023-01-15    FALSE under_followup
#> 6        2023-01-17     TRUE           case

# to simulate more realistic contact tracing data load epiparameters from
# {epiparameter}
library(epiparameter)
contact_distribution <- epiparameter(
  disease = "COVID-19",
  epi_name = "contact distribution",
  prob_distribution = create_prob_distribution(
    prob_distribution = "pois",
    prob_distribution_params = c(mean = 2)
  )
)
#> Citation cannot be created as author, year, journal or title is missing

infectious_period <- epiparameter(
  disease = "COVID-19",
  epi_name = "infectious period",
  prob_distribution = create_prob_distribution(
    prob_distribution = "gamma",
    prob_distribution_params = c(shape = 1, scale = 1)
  )
)
#> Citation cannot be created as author, year, journal or title is missing

contacts <- sim_contacts(
  contact_distribution = contact_distribution,
  infectious_period = infectious_period,
  prob_infection = 0.5
)