scenario_sim: Run a specified number of simulations with identical...
In emmalouisedavis/TTI-branching-process: TTI COVID-19 branching process model

Description Usage Arguments Value Author(s)

Run a specified number of simulations with identical parameters

scenario_sim(
  n.sim = NULL,
  prop.ascertain = NULL,
  cap_max_days = NULL,
  cap_cases = NULL,
  r0isolated = NULL,
  r0community = NULL,
  disp.iso = NULL,
  disp.com = NULL,
  delay_shape = NULL,
  delay_scale = NULL,
  inc_meanlog = NULL,
  inc_sdlog = NULL,
  inf_shape = NULL,
  inf_rate = NULL,
  inf_shift = NULL,
  num.initial.cases = NULL,
  min_quar_delay = 1,
  max_quar_delay = NULL,
  sensitivity = NULL,
  precaution = NULL,
  self_report = NULL,
  test_delay = NULL,
  prop.asym = NULL,
  quarantine = NULL
)

`n.sim`	number of simulations to run
`prop.ascertain`	Probability that cases are ascertained by contact tracing
`cap_max_days`	Max number of days to run the simulation.
`cap_cases`	After reaching this cap, assume the epidemic continues to grow.
`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)
`delay_shape`	Probability of adherence to isolation after symptom onset when not tracked.
`delay_scale`	Doesnt do anything and should be removed.
`inc_meanlog`	shape of distribution for incubation period.
`inc_sdlog`	scale of distribution for incubation period.
`inf_shape`	The shape for the gamma distribution of serial intervals around the symptom onset distribution.
`inf_rate`	Rate parameter for the gamma distribution of serial intervals around the symptom onset distribution.
`inf_shift`	Shift the gamma distribution of serial intervals around the symptom onset distribution back by this much (i.e. transmission can ocur this much before symptom onset).
`num.initial.cases`	Initial number of cases in each initial cluster
`min_quar_delay`	The minimum delay between a case being identified and their contacts being isolated (only applies when quarentine set to TRUE)
`max_quar_delay`	The maximum delay between a case being identified and their contacts being isolated (only applies when quarentine set to TRUE)
`sensitivity`	Test sensitivity.
`precaution`	After a negative test result, keep people in quarantine for this long as a precautionary measure.
`self_report`	Probability that someone that is not tracked will self report (111 for example) after symptoms.
`test_delay`	How long does it take for tests to be administered and results returned.
`prop.asym`	Proportion of asymptomatics.
`quarantine`	logical whether quarantine is in effect, if TRUE then traced contacts are isolated before symptom onset

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.