Run a specified number of simulations with identical parameters

scenario_sim(
  n.sim,
  prop.ascertain,
  cap_max_days,
  cap_cases,
  r0isolated,
  r0community,
  disp.iso,
  disp.com,
  k,
  delay_shape,
  delay_scale,
  num.initial.cases,
  prop.asym,
  quarantine,
  r0subclin = NULL,
  disp.subclin = NULL
)

Arguments

n.sim: number of simulations to run
prop.ascertain: numeric proportion of infectious contacts ascertained by contact tracing (must be 0<=x<=1)
cap_max_days: Stop the simulation when this many days is reached.
cap_cases: Stop the simulation when this many cases is reached.
r0isolated: numeric reproduction number for isolated cases (must be >0)
r0community: numeric reproduction number for non-isolated cases (must be >0)
disp.iso: numeric dispersion parameter for isolated cases (must be >0)
disp.com: numeric dispersion parameter for non-isolated cases (must be >0)
k: numeric skew parameter for sampling the serial interval from the incubation period
delay_shape: numeric shape parameter of delay distribution
delay_scale: numeric scale parameter of delay distribution
num.initial.cases: The number of initial or starting cases which are all assumed to be missed.
prop.asym: proportion of cases that are completely asymptomatic.
quarantine: logical whether quarantine is in effect, if TRUE then traced contacts are isolated before symptom onset
r0subclin: numeric reproduction number for sub-clinical non-isolated cases (must be >0)
disp.subclin: numeric dispersion parameter for sub-clincial non-isolated cases (must be >0)

Value

A data.table object returning the results for multiple simulations using the same set of parameters. The table has columns

week: The week in the simulation.
weekly_cases: The number of new cases that week.
cumulative: The cumulative cases.
effective_r0: The effective reproduction rate for the whole simulation
cases_per_gen: A list column with the cases per generation. This is repeated each row.
sim: Index column for which simulation.

Author

Joel Hellewell

Examples

if (FALSE) {
res <- scenario_sim(n.sim = 5,
num.initial.cases = 5,
cap_max_days = 365,
cap_cases = 2000,
r0isolated = 0,
r0community = 2.5,
disp.iso = 1,
disp.com = 0.16,
k = 0.7,
delay_shape = 2.5,
delay_scale = 5,
prop.asym = 0,
prop.ascertain = 0)
}