Run a single instance of the branching process model
outbreak_model(
initial_cases,
r0_community,
r0_isolated,
r0_asymptomatic,
disp_community,
disp_isolated,
disp_asymptomatic,
incubation_period,
prop_presymptomatic,
onset_to_isolation,
prop_ascertain,
prop_asymptomatic,
cap_max_days,
cap_cases,
quarantine = FALSE
)a nonnegative integer scalar: number of initial
or starting cases which are all assumed to be missed.
a positive numeric scalar: reproduction number for
non-isolated cases (must be >0)
a positive numeric scalar: reproduction number for
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
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 sub-clincial non-isolated cases (must be >0)
a function: a random number generating
function that samples from incubation period distribution, the
function accepts a single integer argument specifying the number of
times to sample the incubation period (i.e. length of the function
output).
a numeric scalar probability (between 0 and 1
inclusive): proportion of transmission that occurs before symptom onset.
a function: a random number generating
function that accepts a single integer argument specifying the
length of the function output.
a numeric scalar probability (between 0 and 1
inclusive): proportion of infectious contacts ascertained by contact
tracing
a numeric scalar probability (between 0 and 1
inclusive): proportion of cases that are completely asymptomatic
(subclinical)
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 logical scalar: whether quarantine is in effect, if
TRUE then traced contacts are isolated before symptom onset; defaults to
FALSE
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(
initial_cases = 1,
prop_ascertain = 0.2,
cap_max_days = 350,
cap_cases = 4500,
r0_isolated = 0.5,
r0_community = 2.5,
r0_asymptomatic = 2.5,
disp_isolated = 1,
disp_community = 0.16,
disp_asymptomatic = 0.16,
prop_presymptomatic = 0.5,
onset_to_isolation = \(x) rweibull(n = x, shape = 1.65, scale = 4.28),
incubation_period = \(x) rweibull(n = x, shape = 2.32, scale = 6.49),
prop_asymptomatic = 0,
quarantine = FALSE
)
out
#> week weekly_cases cumulative effective_r0
#> <num> <num> <num> <num>
#> 1: 0 2 2 4.295861
#> 2: 1 47 49 4.295861
#> 3: 2 253 302 4.295861
#> 4: 3 865 1167 4.295861
#> 5: 4 1550 2717 4.295861
#> 6: 5 1857 4574 4.295861
#> 7: 6 1336 5910 4.295861
#> 8: 7 412 6322 4.295861
#> 9: 8 49 6371 4.295861
#> 10: 9 1 6372 4.295861
#> 11: 10 0 6372 4.295861
#> 12: 11 0 6372 4.295861
#> 13: 12 0 6372 4.295861
#> 14: 13 0 6372 4.295861
#> 15: 14 0 6372 4.295861
#> 16: 15 0 6372 4.295861
#> 17: 16 0 6372 4.295861
#> 18: 17 0 6372 4.295861
#> 19: 18 0 6372 4.295861
#> 20: 19 0 6372 4.295861
#> 21: 20 0 6372 4.295861
#> 22: 21 0 6372 4.295861
#> 23: 22 0 6372 4.295861
#> 24: 23 0 6372 4.295861
#> 25: 24 0 6372 4.295861
#> 26: 25 0 6372 4.295861
#> 27: 26 0 6372 4.295861
#> 28: 27 0 6372 4.295861
#> 29: 28 0 6372 4.295861
#> 30: 29 0 6372 4.295861
#> 31: 30 0 6372 4.295861
#> 32: 31 0 6372 4.295861
#> 33: 32 0 6372 4.295861
#> 34: 33 0 6372 4.295861
#> 35: 34 0 6372 4.295861
#> 36: 35 0 6372 4.295861
#> 37: 36 0 6372 4.295861
#> 38: 37 0 6372 4.295861
#> 39: 38 0 6372 4.295861
#> 40: 39 0 6372 4.295861
#> 41: 40 0 6372 4.295861
#> 42: 41 0 6372 4.295861
#> 43: 42 0 6372 4.295861
#> 44: 43 0 6372 4.295861
#> 45: 44 0 6372 4.295861
#> 46: 45 0 6372 4.295861
#> 47: 46 0 6372 4.295861
#> 48: 47 0 6372 4.295861
#> 49: 48 0 6372 4.295861
#> 50: 49 0 6372 4.295861
#> 51: 50 0 6372 4.295861
#> week weekly_cases cumulative effective_r0
#> cases_per_gen
#> <list>
#> 1: 15, 33, 89, 252, 724,1643,...
#> 2: 15, 33, 89, 252, 724,1643,...
#> 3: 15, 33, 89, 252, 724,1643,...
#> 4: 15, 33, 89, 252, 724,1643,...
#> 5: 15, 33, 89, 252, 724,1643,...
#> 6: 15, 33, 89, 252, 724,1643,...
#> 7: 15, 33, 89, 252, 724,1643,...
#> 8: 15, 33, 89, 252, 724,1643,...
#> 9: 15, 33, 89, 252, 724,1643,...
#> 10: 15, 33, 89, 252, 724,1643,...
#> 11: 15, 33, 89, 252, 724,1643,...
#> 12: 15, 33, 89, 252, 724,1643,...
#> 13: 15, 33, 89, 252, 724,1643,...
#> 14: 15, 33, 89, 252, 724,1643,...
#> 15: 15, 33, 89, 252, 724,1643,...
#> 16: 15, 33, 89, 252, 724,1643,...
#> 17: 15, 33, 89, 252, 724,1643,...
#> 18: 15, 33, 89, 252, 724,1643,...
#> 19: 15, 33, 89, 252, 724,1643,...
#> 20: 15, 33, 89, 252, 724,1643,...
#> 21: 15, 33, 89, 252, 724,1643,...
#> 22: 15, 33, 89, 252, 724,1643,...
#> 23: 15, 33, 89, 252, 724,1643,...
#> 24: 15, 33, 89, 252, 724,1643,...
#> 25: 15, 33, 89, 252, 724,1643,...
#> 26: 15, 33, 89, 252, 724,1643,...
#> 27: 15, 33, 89, 252, 724,1643,...
#> 28: 15, 33, 89, 252, 724,1643,...
#> 29: 15, 33, 89, 252, 724,1643,...
#> 30: 15, 33, 89, 252, 724,1643,...
#> 31: 15, 33, 89, 252, 724,1643,...
#> 32: 15, 33, 89, 252, 724,1643,...
#> 33: 15, 33, 89, 252, 724,1643,...
#> 34: 15, 33, 89, 252, 724,1643,...
#> 35: 15, 33, 89, 252, 724,1643,...
#> 36: 15, 33, 89, 252, 724,1643,...
#> 37: 15, 33, 89, 252, 724,1643,...
#> 38: 15, 33, 89, 252, 724,1643,...
#> 39: 15, 33, 89, 252, 724,1643,...
#> 40: 15, 33, 89, 252, 724,1643,...
#> 41: 15, 33, 89, 252, 724,1643,...
#> 42: 15, 33, 89, 252, 724,1643,...
#> 43: 15, 33, 89, 252, 724,1643,...
#> 44: 15, 33, 89, 252, 724,1643,...
#> 45: 15, 33, 89, 252, 724,1643,...
#> 46: 15, 33, 89, 252, 724,1643,...
#> 47: 15, 33, 89, 252, 724,1643,...
#> 48: 15, 33, 89, 252, 724,1643,...
#> 49: 15, 33, 89, 252, 724,1643,...
#> 50: 15, 33, 89, 252, 724,1643,...
#> 51: 15, 33, 89, 252, 724,1643,...
#> cases_per_gen