outbreak_model: Run a single instance of the branching process model
In emmalouisedavis/TTI-branching-process: TTI COVID-19 branching process model

Description Usage Arguments Value Author(s) Examples

Run a single instance of the branching process model

outbreak_model(
  num.initial.cases = 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,
  prop.asym = NULL,
  inf_shape = NULL,
  inf_rate = NULL,
  inf_shift = NULL,
  min_quar_delay = 1,
  max_quar_delay = NULL,
  test_delay = NULL,
  sensitivity = NULL,
  precaution = NULL,
  self_report = NULL,
  quarantine = NULL,
  testing = NULL
)

`num.initial.cases`	How many cases to start with.
`prop.ascertain`	numeric proportion of infectious contacts ascertained by contact tracing (must be 0<=x<=1)
`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.
`prop.asym`	Proportion of asymptomatics.
`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).
`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)
`test_delay`	How long does it take for tests to be administered and results returned.
`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.
`quarantine`	logical whether quarantine is in effect, if TRUE then traced contacts are isolated before symptom onset
`testing`	Logical to determine whether testing is used.

data.table of cases by week, cumulative cases, and the effective reproduction number of the outreak

Joel Hellewell

## Not run: 
incfn <- dist_setup(dist_param1 = 2.322, dist_param2 = 6.492, dist_type = 'weibull')
# delay distribution sampling function
delayfn <- dist_setup(2, 4, 'weibull')
# generate initial cases
case_data <- outbreak_setup(num.initial.cases = 5,
                            incfn=incfn,
                            delayfn = delayfn,
                            prop.asym=0)
# generate next generation of cases
case_data <- outbreak_step(case_data = case_data,
                           disp.iso = 1,
                           disp.com = 0.16,
                           r0isolated = 0,
                           r0community = 2.5,
                           prop.asym = 0,
                           incfn = incfn,
                           delayfn = delayfn,
                           inf_rate = 2,
                           inf_shape = 2,
                           inf_shift = 3,
                           prop.ascertain = 0,
                           quarantine = FALSE)

## End(Not run)