Create a list of event probabilities to run the ringbp model

Create a list of event probabilities to run the ringbp model

Usage

event_prob_opts(
  asymptomatic,
  presymptomatic_transmission,
  symptomatic_traced,
  symptomatic_self_isolate = 0
)

Arguments

asymptomatic

a numeric scalar probability (between 0 and 1 inclusive): proportion of cases that are completely asymptomatic (subclinical)

presymptomatic_transmission

a numeric scalar probability (between 0 and 1 inclusive): proportion of transmission that occurs before symptom onset.

symptomatic_traced

a numeric scalar probability (between 0 and 1 inclusive), or a function of time returning probabilities in [0, 1]: proportion of infectious contacts ascertained by contact tracing.

A scalar is treated as a constant contact-tracing probability over the whole simulation. A function accepts a numeric vector of times (the contact's exposure time in days since the exposure of the initial cases on day 0) and returns a numeric vector of probabilities of the same length, allowing the contact-tracing probability to vary with time. For example, \(t) ifelse(t < 30, 0, 0.5) represents a contact-tracing programme that activates on day 30 and ascertains 50% of contacts.

Only contacts whose infector is symptomatic are eligible for tracing (see outbreak_step() for how isolation times are assigned).

symptomatic_self_isolate

a numeric scalar probability (between 0 and 1 inclusive): proportion of cases that self-isolate when they become symptomatic. These individuals do not get tested and do not require a positive test result to enter isolation. Default is 0 (i.e. no infectious individuals self-isolate).

If symptomatic_self_isolate is non-zero a random number generating function needs to be specified in the onset_to_self_isolation argument in the delay_opts() function, otherwise the scenario_sim() will error.

Value

A list with class <ringbp_event_prob_opts>.

Examples

event_prob_opts(
  asymptomatic = 0.1,
  presymptomatic_transmission = 0.5,
  symptomatic_traced = 0.2
)
#> $asymptomatic
#> [1] 0.1
#> 
#> $presymptomatic_transmission
#> [1] 0.5
#> 
#> $alpha
#> [1] 1.968077e-08
#> 
#> $symptomatic_traced
#> function (t) 
#> rep(x, length(t))
#> <bytecode: 0x558feff3d538>
#> <environment: 0x558feff3d148>
#> 
#> $symptomatic_self_isolate
#> [1] 0
#> 
#> attr(,"class")
#> [1] "ringbp_event_prob_opts"

# time-varying contact tracing ascertainment: programme activates
# on day 30 and ascertains 50%
event_prob_opts(
  asymptomatic = 0.1,
  presymptomatic_transmission = 0.5,
  symptomatic_traced = \(t) ifelse(t < 30, 0, 0.5)
)
#> $asymptomatic
#> [1] 0.1
#> 
#> $presymptomatic_transmission
#> [1] 0.5
#> 
#> $alpha
#> [1] 1.968077e-08
#> 
#> $symptomatic_traced
#> function (t) 
#> ifelse(t < 30, 0, 0.5)
#> <environment: 0x558feff2cb00>
#> 
#> $symptomatic_self_isolate
#> [1] 0
#> 
#> attr(,"class")
#> [1] "ringbp_event_prob_opts"