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,
r0subclin = NULL,
disp.iso = NULL,
disp.com = NULL,
disp.subclin = NULL,
k,
delay_shape = NULL,
delay_scale = NULL,
prop.asym = NULL,
quarantine = NULL
)a nonnegative integer scalar: number of initial
or starting cases which are all assumed to be missed.
a nonnegative numeric scalar: proportion of
infectious contacts ascertained by contact tracing (must be 0<=x<=1)
a positive integer scalar: stop the simulation when
this many days is reached.
a positive integer scalar: number of cumulative cases at
which the branching process (simulation) was terminated
a positive numeric scalar: reproduction number for
isolated cases (must be >0)
a positive numeric scalar: reproduction number for
non-isolated cases (must be >0)
a positive numeric scalar: reproduction number for
sub-clinical non-isolated cases (must be >0)
a positive numeric scalar: dispersion parameter for
isolated cases (must be >0)
a positive numeric scalar: dispersion parameter for
non-isolated cases (must be >0)
a positive numeric scalar: dispersion parameter for
sub-clincial non-isolated cases (must be >0)
a numeric scalar: skew parameter for sampling the serial
interval from the incubation period
a positive numeric scalar: shape parameter of delay
distribution
a positive numeric scalar: scale parameter of delay
distribution
a nonnegative numeric scalar: proportion of cases that
are completely asymptomatic (sublinical) (between 0 and 1)
a logical scalar: whether quarantine is in effect, if
TRUE then traced contacts are isolated before symptom onset
data.table of cases by week, cumulative cases, and the effective
reproduction number of the outbreak. data.table columns are:
$week: numeric
$weekly_cases: numeric
$cumulative: numeric
$effective_r0: numeric
$cases_per_gen: list
set.seed(1)
out <- outbreak_model(
num.initial.cases = 1,
prop.ascertain = 0.2,
cap_max_days = 350,
cap_cases = 4500,
r0isolated = 0.5,
r0community = 2.5,
r0subclin = 2.5,
disp.iso = 1,
disp.com = 0.16,
disp.subclin = 0.16,
k = 0,
delay_shape = 1.65,
delay_scale = 4.28,
prop.asym = 0,
quarantine = FALSE
)
out
#> week weekly_cases cumulative effective_r0 cases_per_gen
#> <num> <num> <num> <num> <list>
#> 1: 0 0 0 1 2,0
#> 2: 1 3 3 1 2,0
#> 3: 2 0 3 1 2,0
#> 4: 3 0 3 1 2,0
#> 5: 4 0 3 1 2,0
#> 6: 5 0 3 1 2,0
#> 7: 6 0 3 1 2,0
#> 8: 7 0 3 1 2,0
#> 9: 8 0 3 1 2,0
#> 10: 9 0 3 1 2,0
#> 11: 10 0 3 1 2,0
#> 12: 11 0 3 1 2,0
#> 13: 12 0 3 1 2,0
#> 14: 13 0 3 1 2,0
#> 15: 14 0 3 1 2,0
#> 16: 15 0 3 1 2,0
#> 17: 16 0 3 1 2,0
#> 18: 17 0 3 1 2,0
#> 19: 18 0 3 1 2,0
#> 20: 19 0 3 1 2,0
#> 21: 20 0 3 1 2,0
#> 22: 21 0 3 1 2,0
#> 23: 22 0 3 1 2,0
#> 24: 23 0 3 1 2,0
#> 25: 24 0 3 1 2,0
#> 26: 25 0 3 1 2,0
#> 27: 26 0 3 1 2,0
#> 28: 27 0 3 1 2,0
#> 29: 28 0 3 1 2,0
#> 30: 29 0 3 1 2,0
#> 31: 30 0 3 1 2,0
#> 32: 31 0 3 1 2,0
#> 33: 32 0 3 1 2,0
#> 34: 33 0 3 1 2,0
#> 35: 34 0 3 1 2,0
#> 36: 35 0 3 1 2,0
#> 37: 36 0 3 1 2,0
#> 38: 37 0 3 1 2,0
#> 39: 38 0 3 1 2,0
#> 40: 39 0 3 1 2,0
#> 41: 40 0 3 1 2,0
#> 42: 41 0 3 1 2,0
#> 43: 42 0 3 1 2,0
#> 44: 43 0 3 1 2,0
#> 45: 44 0 3 1 2,0
#> 46: 45 0 3 1 2,0
#> 47: 46 0 3 1 2,0
#> 48: 47 0 3 1 2,0
#> 49: 48 0 3 1 2,0
#> 50: 49 0 3 1 2,0
#> 51: 50 0 3 1 2,0
#> week weekly_cases cumulative effective_r0 cases_per_gen