tests/testthat/test-outbreak_step.R
In emmalouisedavis/TTI-branching-process: TTI COVID-19 branching process model
context("Test basic usage")
test_that("A basic sim returns the correct object", {
set.seed(20200410)
incfn <- dist_setup(1.434065,0.6612,dist_type='lognormal')
# delay distribution sampling function
delayfn <- dist_setup(2, 4,'weibull')
# generate initial cases
case_data <- outbreak_setup(num.initial.cases = 1,
incfn=incfn,
delayfn = delayfn,
testing = FALSE,
test_delay = 1,
sensitivity = 0.9,
precaution = 3,
self_report = 0,
prop.asym = 0,
iso_adhere = 0.9)
# generate next generation of cases
case_data2 <- outbreak_step(case_data = case_data,
disp.iso = 1,
disp.com = 0.16,
r0isolated = 0,
r0community = 500, # almost guarentees to get new cases
prop.asym = 0,
incfn = incfn,
delayfn = delayfn,
inf_shape = 2.115779,
inf_rate = 0.6898583,
inf_shift = 3,
max_quar_delay = 4,
min_quar_delay = 1,
prop.ascertain = 0,
quarantine = FALSE,
testing = FALSE,
sensitivity = 0.9,
precaution = 5,
self_report = 0,
test_delay = 0,
test_asym = F,
iso_adhere = 0.9)
expect_true(nrow(case_data2$cases) > 1)
expect_equal(as.vector(table(case_data2$cases$infector)), c(1, nrow(case_data2$cases) - 1))
# With R0 = 0 we should get no additional cases.
case_data3 <- outbreak_step(case_data = case_data,
disp.iso = 1,
disp.com = 0.16,
r0isolated = 0,
r0community = 0, # almost guarentees to get new cases
prop.asym = 0,
incfn = incfn,
delayfn = delayfn,
inf_shape = 2.115779,
inf_rate = 0.6898583,
inf_shift = 3,
prop.ascertain = 0,
quarantine = FALSE,
testing = FALSE,
sensitivity = 0.9,
precaution = 5,
self_report = 0,
test_delay = 0,
test_asym = F,
iso_adhere = 0.9)
expect_true(nrow(case_data3$cases) == 1)
})
test_that("Sim with multiple infectors makes senes", {
set.seed(20212318)
incfn <- dist_setup(1.434065,0.6612,dist_type='lognormal')
# delay distribution sampling function
delayfn <- dist_setup(2, 4,'weibull')
# generate initial cases
case_data <- outbreak_setup(num.initial.cases = 2,
incfn=incfn,
delayfn = delayfn,
testing = FALSE,
test_delay = 1,
sensitivity = 0.1,
precaution = 3,
self_report = 0,
prop.asym = 0,
iso_adhere = 0.9)
# generate next generation of cases
case_data2 <- outbreak_step(case_data = case_data,
disp.iso = 1,
disp.com = 1,
r0isolated = 0,
r0community = 1000, # almost guarentees to get new cases
prop.asym = 0,
incfn = incfn,
delayfn = delayfn,
inf_shape = 2.115779,
inf_rate = 0.6898583,
inf_shift = 3,
max_quar_delay = 4,
min_quar_delay = 1,
prop.ascertain = 0,
quarantine = FALSE,
testing = FALSE,
sensitivity = 0.9,
precaution = 5,
self_report = 0,
test_delay = 0,
test_asym = F,
iso_adhere = 0.9)
expect_true(nrow(case_data2$cases) > 1)
expect_true(as.vector(table(case_data2$cases$infector))[1] == 2)
expect_true(all(as.vector(table(case_data2$cases$infector))[2:3] > 1))
})
test_that("R0isolated is working properly", {
set.seed(20200410)
incfn <- dist_setup(1.434065,0.6612,dist_type='lognormal')
# delay distribution sampling function
delayfn <- dist_setup(2, 4,'weibull')
# generate initial cases
case_data <- outbreak_setup(num.initial.cases = 1,
incfn=incfn,
delayfn = delayfn,
testing = FALSE,
test_delay = 1,
sensitivity = 0.9,
precaution = 3,
self_report = 0,
prop.asym = 0,
iso_adhere = 0.9)
case_data$isolated <- TRUE
# generate next generation of cases
case_data2 <- outbreak_step(case_data = case_data,
disp.iso = 1,
disp.com = 0.16,
r0isolated = 0,
r0community = 1000, # almost guarentees to get new cases
prop.asym = 0,
incfn = incfn,
delayfn = delayfn,
inf_shape = 2.115779,
inf_rate = 0.6898583,
inf_shift = 3,
max_quar_delay = 4,
min_quar_delay = 1,
prop.ascertain = 0,
quarantine = FALSE,
testing = FALSE,
sensitivity = 0.9,
precaution = 5,
self_report = 0,
test_delay = 0,
test_asym = F,
iso_adhere = 0.9)
expect_true(nrow(case_data2$cases) == 1)
# generate next generation of cases
case_data3 <- outbreak_step(case_data = case_data,
disp.iso = 1,
disp.com = 0.16,
r0isolated = 500, # Shoiuld get lots of cases
r0community = 0, # Case is isolated so irrelevent
prop.asym = 0,
incfn = incfn,
delayfn = delayfn,
inf_shape = 2.115779,
inf_rate = 0.6898583,
inf_shift = 3,
prop.ascertain = 0,
max_quar_delay = 4,
min_quar_delay = 1,
quarantine = FALSE,
testing = FALSE,
sensitivity = 0.9,
precaution = 5,
self_report = 0,
test_delay = 0,
test_asym = F,
iso_adhere = 0.9)
expect_true(nrow(case_data3$cases) > 1)
})
test_that('Test self_report arg',{
incfn <- dist_setup(1.434065,0.6612,dist_type='lognormal')
# delay distribution sampling function
delayfn <- dist_setup(2, 4,'weibull')
# generate initial cases
case_data <- outbreak_setup(num.initial.cases = 1,
incfn=incfn,
delayfn = delayfn,
testing = FALSE,
test_delay = 1,
prop.asym=0,
self_report = 0.2,
iso_adhere = 0.9)
# generate next generation of cases
# Ascertain = 0
case_data2 <- outbreak_step(case_data = case_data,
disp.iso = 1,
disp.com = 1,
r0isolated = 0,
r0community = 500, # Should get lots of cases
prop.asym = 0,
incfn = incfn,
delayfn = delayfn,
inf_shape = 2.115779,
inf_rate = 0.6898583,
inf_shift = 3,
prop.ascertain = 0.5,
max_quar_delay = 4,
min_quar_delay = 1,
quarantine = FALSE,
testing = FALSE,
sensitivity = 0.9,
precaution = 5,
self_report = 0,
test_delay = 0,
test_asym = F,
iso_adhere = 0.9)
expect_true(all(case_data2$cases$missed))
# Now ascertain = 1
case_data3 <- outbreak_step(case_data = case_data,
disp.iso = 1,
disp.com = 1,
r0isolated = 0,
r0community = 500, # Shoiuld get lots of cases
prop.asym = 0,
incfn = incfn,
delayfn = delayfn,
inf_shape = 2.115779,
inf_rate = 0.6898583,
inf_shift = 3,
prop.ascertain = 0.5,
max_quar_delay = 4,
min_quar_delay = 1,
quarantine = FALSE,
testing = FALSE,
sensitivity = 0.9,
precaution = 5,
self_report = 1,
test_delay = 0,
test_asym = F,
iso_adhere = 1)
# The index case should be missed but no others.
# This test relies on the index being symptomatic which I haven't forced.
expect_true(sum(case_data3$cases$missed) == 1)
case_data4 <- outbreak_step(case_data = case_data,
disp.iso = 1,
disp.com = 1,
r0isolated = 0,
r0community = 500, # Shoiuld get lots of cases
prop.asym = 0,
incfn = incfn,
delayfn = delayfn,
inf_shape = 2.115779,
inf_rate = 0.6898583,
inf_shift = 3,
prop.ascertain = 0.5,
max_quar_delay = 4,
min_quar_delay = 1,
quarantine = FALSE,
testing = FALSE,
sensitivity = 0.9,
precaution = 5,
self_report = 0.5,
test_delay = 0,
test_asym = F,
iso_adhere = 0.9)
# After ignoring the index case we should still get both true and false.
# This is more complicated with prop.asym and self_report.
# I'll add more tests.
expect_length(unique(case_data4$cases$missed[-1]), 2)
})
test_that('Test ascertain arg',{
inc_meanlog = 1.434065
inc_sdlog = 0.6612
incfn <- dist_setup(dist_param1 = inc_meanlog,
dist_param2 = inc_sdlog,
dist_type = 'lognormal')
delay_shape = 0.9
delayfn <- dist_setup(delay_shape,
1, "adherence")
# generate initial cases
case_data <- outbreak_setup(num.initial.cases = 1,
incfn=incfn,
delayfn = delayfn,
testing = FALSE,
test_delay = 1,
precaution = 2,
prop.asym=0,
self_report = 0.2,
iso_adhere = 0.9)
# Start with a tracked individual, not in isolation. So I'm not sure how that will work.
case_data$missed <- FALSE
case_data$isolated_time <- 100
# generate next generation of cases
# Ascertain = 1 so all cases should be tracked.
case_data2 <- outbreak_step(case_data = case_data,
disp.iso = 1,
disp.com = 1,
r0isolated = 0,
r0community = 500, # Shoiuld get lots of cases
prop.asym = 0,
incfn = incfn,
delayfn = delayfn,
inf_shape = 2.115779,
inf_rate = 0.6898583,
inf_shift = 3,
prop.ascertain = 1,
max_quar_delay = 4,
min_quar_delay = 1,
quarantine = FALSE,
testing = FALSE,
sensitivity = 0.9,
precaution = 5,
self_report = 0,
test_delay = 0,
test_asym = F,
iso_adhere = 1)
expect_true(all(!case_data2$cases$missed))
expect_true(nrow(case_data2$cases) > 1)
# generate next generation of cases
# Ascertain = 0 so all cases except the index should not be tracked.
case_data2 <- outbreak_step(case_data = case_data,
disp.iso = 1,
disp.com = 1,
r0isolated = 0,
r0community = 500, # Shoiuld get lots of cases
prop.asym = 0,
incfn = incfn,
delayfn = delayfn,
inf_shape = 2.115779,
inf_rate = 0.6898583,
inf_shift = 3,
prop.ascertain = 0,
max_quar_delay = 4,
min_quar_delay = 1,
quarantine = FALSE,
testing = FALSE,
sensitivity = 0,
precaution = 5,
self_report = 0,
test_delay = 0,
test_asym = F,
iso_adhere = 0)
expect_true(all(case_data2$cases$missed[-1]))
expect_true(nrow(case_data2$cases) > 1)
})
test_that('Test testing arg',{
inc_meanlog = 1.434065
inc_sdlog = 0.6612
incfn <- dist_setup(dist_param1 = inc_meanlog,
dist_param2 = inc_sdlog,
dist_type = 'lognormal')
delay_shape = 0.9
delayfn <- dist_setup(delay_shape,
1, "adherence")
# generate initial cases
case_data <- outbreak_setup(num.initial.cases = 1,
incfn=incfn,
delayfn = delayfn,
testing = FALSE,
test_delay = 1,
prop.asym=0,
self_report = 0.2,
iso_adhere = 0.9)
# generate next generation of cases
# Testing = FALSE so all tests should be NA
case_data2 <- outbreak_step(case_data = case_data,
disp.iso = 1,
disp.com = 1,
r0isolated = 0,
r0community = 500, # Shoiuld get lots of cases
prop.asym = 0,
incfn = incfn,
delayfn = delayfn,
inf_shape = 2.115779,
inf_rate = 0.6898583,
inf_shift = 3,
prop.ascertain = 1,
max_quar_delay = 4,
min_quar_delay = 1,
quarantine = FALSE,
testing = FALSE,
sensitivity = 0.9,
precaution = 5,
self_report = 0,
test_delay = 0,
test_asym = F,
iso_adhere = 0.9)
expect_true(all(is.na(case_data2$cases$test_result)))
# generate next generation of cases
# Testing = TRUE so same tests should be TRUE or FALSE
case_data <- outbreak_setup(num.initial.cases = 1,
incfn=incfn,
delayfn = delayfn,
testing = TRUE,
test_delay = 1,
prop.asym=0,
self_report = 0.2,
iso_adhere = 0.9)
case_data2 <- outbreak_step(case_data = case_data,
disp.iso = 1,
disp.com = 1,
r0isolated = 0,
r0community = 500, # Shoiuld get lots of cases
prop.asym = 0,
incfn = incfn,
delayfn = delayfn,
inf_shape = 2.115779,
inf_rate = 0.6898583,
inf_shift = 3,
prop.ascertain = 1,
max_quar_delay = 4,
min_quar_delay = 1,
quarantine = FALSE,
testing = TRUE,
sensitivity = 0.9,
test_delay = 1,
precaution = 5,
self_report = 0.5,
test_asym = F,
iso_adhere = 0.9)
expect_true(all(c(TRUE, FALSE) %in% case_data2$cases$test_result))
})
test_that('Test sensitivity arg',{
inc_meanlog = 1.434065
inc_sdlog = 0.6612
incfn <- dist_setup(dist_param1 = inc_meanlog,
dist_param2 = inc_sdlog,
dist_type = 'lognormal')
delay_shape = 0.9
delayfn <- dist_setup(delay_shape,
1, "adherence")
case_data <- outbreak_setup(num.initial.cases = 1,
incfn=incfn,
delayfn = delayfn,
testing = TRUE,
test_delay = 1,
prop.asym=0,
sensitivity = 0.9,
self_report = 0,
iso_adhere = 0.9)
# generate next generation of cases
# Sensitivity = 1 so should be NAs (missed) and TRUEs but not falses.
case_data2 <- outbreak_step(case_data = case_data,
disp.iso = 1,
disp.com = 1,
r0isolated = 0,
r0community = 500, # Shoiuld get lots of cases
prop.asym = 0,
incfn = incfn,
delayfn = delayfn,
inf_shape = 2.115779,
inf_rate = 0.6898583,
inf_shift = 3,
prop.ascertain = 1,
max_quar_delay = 4,
min_quar_delay = 1,
quarantine = FALSE,
testing = TRUE,
sensitivity = 1,
precaution = 5,
test_delay = 0.3,
self_report = 0.5,
test_asym = F,
iso_adhere = 0.9)
expect_true(all(case_data2$cases$test_result %in% c(TRUE, NA)))
# Missed cases should be na, not missed should be TRUE
expect_true(all(case_data2$cases$test_result[case_data2$cases$missed == FALSE] == TRUE))
expect_true(all(is.na(case_data2$cases$test_result[case_data2$cases$missed == TRUE])))
# generate next generation of cases
# Sensitivity = 0 so should be NAs (missed) and FALSE but no TRUES.
case_data2 <- outbreak_step(case_data = case_data,
disp.iso = 1,
disp.com = 1,
r0isolated = 0,
r0community = 500, # Shoiuld get lots of cases
prop.asym = 0,
incfn = incfn,
delayfn = delayfn,
inf_shape = 2.115779,
inf_rate = 0.6898583,
inf_shift = 3,
prop.ascertain = 1,
max_quar_delay = 4,
min_quar_delay = 1,
quarantine = FALSE,
testing = TRUE,
sensitivity = 0,
precaution = 5,
test_delay = 0.3,
self_report = 0.5,
test_asym = F,
iso_adhere = 0.9)
expect_true(all(case_data2$cases$test_result %in% c(FALSE, NA)))
# Missed cases should be na, not missed should be TRUE
expect_true(all(case_data2$cases$test_result[case_data2$cases$missed == FALSE] == FALSE))
expect_true(all(is.na(case_data2$cases$test_result[case_data2$cases$missed == TRUE])))
# generate next generation of cases
# Sensitivity = 0.5 so should be NAs (missed) and FALSE and TRUES.
case_data2 <- outbreak_step(case_data = case_data,
disp.iso = 1,
disp.com = 1,
r0isolated = 0,
r0community = 500, # Shoiuld get lots of cases
prop.asym = 0,
incfn = incfn,
delayfn = delayfn,
inf_shape = 2.115779,
inf_rate = 0.6898583,
inf_shift = 3,
prop.ascertain = 1,
max_quar_delay = 4,
min_quar_delay = 1,
quarantine = FALSE,
testing = TRUE,
sensitivity = 0.5,
precaution = 5,
test_delay = 0.3,
self_report = 0.5,
test_asym = F,
iso_adhere = 0.9)
expect_true(all(case_data2$cases$test_result %in% c(FALSE, TRUE, NA)))
expect_true(all(c(FALSE, TRUE, NA) %in% case_data2$cases$test_result))
# Missed cases should be na, not missed should be TRUE
expect_true(all(c(TRUE, FALSE) %in% case_data2$cases$test_result[case_data2$cases$missed == FALSE]))
expect_true(all(case_data2$cases$test_result[case_data2$cases$missed == FALSE] %in% c(TRUE, FALSE)))
expect_true(all(is.na(case_data2$cases$test_result[case_data2$cases$missed == TRUE])))
})
emmalouisedavis/TTI-branching-process documentation built on Dec. 20, 2021, 5:17 a.m.
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context("Test basic usage")
test_that("A basic sim returns the correct object", {
set.seed(20200410)
incfn <- dist_setup(1.434065,0.6612,dist_type='lognormal')
# delay distribution sampling function
delayfn <- dist_setup(2, 4,'weibull')
# generate initial cases
case_data <- outbreak_setup(num.initial.cases = 1,
incfn=incfn,
delayfn = delayfn,
testing = FALSE,
test_delay = 1,
sensitivity = 0.9,
precaution = 3,
self_report = 0,
prop.asym = 0,
iso_adhere = 0.9)
# generate next generation of cases
case_data2 <- outbreak_step(case_data = case_data,
disp.iso = 1,
disp.com = 0.16,
r0isolated = 0,
r0community = 500, # almost guarentees to get new cases
prop.asym = 0,
incfn = incfn,
delayfn = delayfn,
inf_shape = 2.115779,
inf_rate = 0.6898583,
inf_shift = 3,
max_quar_delay = 4,
min_quar_delay = 1,
prop.ascertain = 0,
quarantine = FALSE,
testing = FALSE,
sensitivity = 0.9,
precaution = 5,
self_report = 0,
test_delay = 0,
test_asym = F,
iso_adhere = 0.9)
expect_true(nrow(case_data2$cases) > 1)
expect_equal(as.vector(table(case_data2$cases$infector)), c(1, nrow(case_data2$cases) - 1))
# With R0 = 0 we should get no additional cases.
case_data3 <- outbreak_step(case_data = case_data,
disp.iso = 1,
disp.com = 0.16,
r0isolated = 0,
r0community = 0, # almost guarentees to get new cases
prop.asym = 0,
incfn = incfn,
delayfn = delayfn,
inf_shape = 2.115779,
inf_rate = 0.6898583,
inf_shift = 3,
prop.ascertain = 0,
quarantine = FALSE,
testing = FALSE,
sensitivity = 0.9,
precaution = 5,
self_report = 0,
test_delay = 0,
test_asym = F,
iso_adhere = 0.9)
expect_true(nrow(case_data3$cases) == 1)
})
test_that("Sim with multiple infectors makes senes", {
set.seed(20212318)
incfn <- dist_setup(1.434065,0.6612,dist_type='lognormal')
# delay distribution sampling function
delayfn <- dist_setup(2, 4,'weibull')
# generate initial cases
case_data <- outbreak_setup(num.initial.cases = 2,
incfn=incfn,
delayfn = delayfn,
testing = FALSE,
test_delay = 1,
sensitivity = 0.1,
precaution = 3,
self_report = 0,
prop.asym = 0,
iso_adhere = 0.9)
# generate next generation of cases
case_data2 <- outbreak_step(case_data = case_data,
disp.iso = 1,
disp.com = 1,
r0isolated = 0,
r0community = 1000, # almost guarentees to get new cases
prop.asym = 0,
incfn = incfn,
delayfn = delayfn,
inf_shape = 2.115779,
inf_rate = 0.6898583,
inf_shift = 3,
max_quar_delay = 4,
min_quar_delay = 1,
prop.ascertain = 0,
quarantine = FALSE,
testing = FALSE,
sensitivity = 0.9,
precaution = 5,
self_report = 0,
test_delay = 0,
test_asym = F,
iso_adhere = 0.9)
expect_true(nrow(case_data2$cases) > 1)
expect_true(as.vector(table(case_data2$cases$infector))[1] == 2)
expect_true(all(as.vector(table(case_data2$cases$infector))[2:3] > 1))
})
test_that("R0isolated is working properly", {
set.seed(20200410)
incfn <- dist_setup(1.434065,0.6612,dist_type='lognormal')
# delay distribution sampling function
delayfn <- dist_setup(2, 4,'weibull')
# generate initial cases
case_data <- outbreak_setup(num.initial.cases = 1,
incfn=incfn,
delayfn = delayfn,
testing = FALSE,
test_delay = 1,
sensitivity = 0.9,
precaution = 3,
self_report = 0,
prop.asym = 0,
iso_adhere = 0.9)
case_data$isolated <- TRUE
# generate next generation of cases
case_data2 <- outbreak_step(case_data = case_data,
disp.iso = 1,
disp.com = 0.16,
r0isolated = 0,
r0community = 1000, # almost guarentees to get new cases
prop.asym = 0,
incfn = incfn,
delayfn = delayfn,
inf_shape = 2.115779,
inf_rate = 0.6898583,
inf_shift = 3,
max_quar_delay = 4,
min_quar_delay = 1,
prop.ascertain = 0,
quarantine = FALSE,
testing = FALSE,
sensitivity = 0.9,
precaution = 5,
self_report = 0,
test_delay = 0,
test_asym = F,
iso_adhere = 0.9)
expect_true(nrow(case_data2$cases) == 1)
# generate next generation of cases
case_data3 <- outbreak_step(case_data = case_data,
disp.iso = 1,
disp.com = 0.16,
r0isolated = 500, # Shoiuld get lots of cases
r0community = 0, # Case is isolated so irrelevent
prop.asym = 0,
incfn = incfn,
delayfn = delayfn,
inf_shape = 2.115779,
inf_rate = 0.6898583,
inf_shift = 3,
prop.ascertain = 0,
max_quar_delay = 4,
min_quar_delay = 1,
quarantine = FALSE,
testing = FALSE,
sensitivity = 0.9,
precaution = 5,
self_report = 0,
test_delay = 0,
test_asym = F,
iso_adhere = 0.9)
expect_true(nrow(case_data3$cases) > 1)
})
test_that('Test self_report arg',{
incfn <- dist_setup(1.434065,0.6612,dist_type='lognormal')
# delay distribution sampling function
delayfn <- dist_setup(2, 4,'weibull')
# generate initial cases
case_data <- outbreak_setup(num.initial.cases = 1,
incfn=incfn,
delayfn = delayfn,
testing = FALSE,
test_delay = 1,
prop.asym=0,
self_report = 0.2,
iso_adhere = 0.9)
# generate next generation of cases
# Ascertain = 0
case_data2 <- outbreak_step(case_data = case_data,
disp.iso = 1,
disp.com = 1,
r0isolated = 0,
r0community = 500, # Should get lots of cases
prop.asym = 0,
incfn = incfn,
delayfn = delayfn,
inf_shape = 2.115779,
inf_rate = 0.6898583,
inf_shift = 3,
prop.ascertain = 0.5,
max_quar_delay = 4,
min_quar_delay = 1,
quarantine = FALSE,
testing = FALSE,
sensitivity = 0.9,
precaution = 5,
self_report = 0,
test_delay = 0,
test_asym = F,
iso_adhere = 0.9)
expect_true(all(case_data2$cases$missed))
# Now ascertain = 1
case_data3 <- outbreak_step(case_data = case_data,
disp.iso = 1,
disp.com = 1,
r0isolated = 0,
r0community = 500, # Shoiuld get lots of cases
prop.asym = 0,
incfn = incfn,
delayfn = delayfn,
inf_shape = 2.115779,
inf_rate = 0.6898583,
inf_shift = 3,
prop.ascertain = 0.5,
max_quar_delay = 4,
min_quar_delay = 1,
quarantine = FALSE,
testing = FALSE,
sensitivity = 0.9,
precaution = 5,
self_report = 1,
test_delay = 0,
test_asym = F,
iso_adhere = 1)
# The index case should be missed but no others.
# This test relies on the index being symptomatic which I haven't forced.
expect_true(sum(case_data3$cases$missed) == 1)
case_data4 <- outbreak_step(case_data = case_data,
disp.iso = 1,
disp.com = 1,
r0isolated = 0,
r0community = 500, # Shoiuld get lots of cases
prop.asym = 0,
incfn = incfn,
delayfn = delayfn,
inf_shape = 2.115779,
inf_rate = 0.6898583,
inf_shift = 3,
prop.ascertain = 0.5,
max_quar_delay = 4,
min_quar_delay = 1,
quarantine = FALSE,
testing = FALSE,
sensitivity = 0.9,
precaution = 5,
self_report = 0.5,
test_delay = 0,
test_asym = F,
iso_adhere = 0.9)
# After ignoring the index case we should still get both true and false.
# This is more complicated with prop.asym and self_report.
# I'll add more tests.
expect_length(unique(case_data4$cases$missed[-1]), 2)
})
test_that('Test ascertain arg',{
inc_meanlog = 1.434065
inc_sdlog = 0.6612
incfn <- dist_setup(dist_param1 = inc_meanlog,
dist_param2 = inc_sdlog,
dist_type = 'lognormal')
delay_shape = 0.9
delayfn <- dist_setup(delay_shape,
1, "adherence")
# generate initial cases
case_data <- outbreak_setup(num.initial.cases = 1,
incfn=incfn,
delayfn = delayfn,
testing = FALSE,
test_delay = 1,
precaution = 2,
prop.asym=0,
self_report = 0.2,
iso_adhere = 0.9)
# Start with a tracked individual, not in isolation. So I'm not sure how that will work.
case_data$missed <- FALSE
case_data$isolated_time <- 100
# generate next generation of cases
# Ascertain = 1 so all cases should be tracked.
case_data2 <- outbreak_step(case_data = case_data,
disp.iso = 1,
disp.com = 1,
r0isolated = 0,
r0community = 500, # Shoiuld get lots of cases
prop.asym = 0,
incfn = incfn,
delayfn = delayfn,
inf_shape = 2.115779,
inf_rate = 0.6898583,
inf_shift = 3,
prop.ascertain = 1,
max_quar_delay = 4,
min_quar_delay = 1,
quarantine = FALSE,
testing = FALSE,
sensitivity = 0.9,
precaution = 5,
self_report = 0,
test_delay = 0,
test_asym = F,
iso_adhere = 1)
expect_true(all(!case_data2$cases$missed))
expect_true(nrow(case_data2$cases) > 1)
# generate next generation of cases
# Ascertain = 0 so all cases except the index should not be tracked.
case_data2 <- outbreak_step(case_data = case_data,
disp.iso = 1,
disp.com = 1,
r0isolated = 0,
r0community = 500, # Shoiuld get lots of cases
prop.asym = 0,
incfn = incfn,
delayfn = delayfn,
inf_shape = 2.115779,
inf_rate = 0.6898583,
inf_shift = 3,
prop.ascertain = 0,
max_quar_delay = 4,
min_quar_delay = 1,
quarantine = FALSE,
testing = FALSE,
sensitivity = 0,
precaution = 5,
self_report = 0,
test_delay = 0,
test_asym = F,
iso_adhere = 0)
expect_true(all(case_data2$cases$missed[-1]))
expect_true(nrow(case_data2$cases) > 1)
})
test_that('Test testing arg',{
inc_meanlog = 1.434065
inc_sdlog = 0.6612
incfn <- dist_setup(dist_param1 = inc_meanlog,
dist_param2 = inc_sdlog,
dist_type = 'lognormal')
delay_shape = 0.9
delayfn <- dist_setup(delay_shape,
1, "adherence")
# generate initial cases
case_data <- outbreak_setup(num.initial.cases = 1,
incfn=incfn,
delayfn = delayfn,
testing = FALSE,
test_delay = 1,
prop.asym=0,
self_report = 0.2,
iso_adhere = 0.9)
# generate next generation of cases
# Testing = FALSE so all tests should be NA
case_data2 <- outbreak_step(case_data = case_data,
disp.iso = 1,
disp.com = 1,
r0isolated = 0,
r0community = 500, # Shoiuld get lots of cases
prop.asym = 0,
incfn = incfn,
delayfn = delayfn,
inf_shape = 2.115779,
inf_rate = 0.6898583,
inf_shift = 3,
prop.ascertain = 1,
max_quar_delay = 4,
min_quar_delay = 1,
quarantine = FALSE,
testing = FALSE,
sensitivity = 0.9,
precaution = 5,
self_report = 0,
test_delay = 0,
test_asym = F,
iso_adhere = 0.9)
expect_true(all(is.na(case_data2$cases$test_result)))
# generate next generation of cases
# Testing = TRUE so same tests should be TRUE or FALSE
case_data <- outbreak_setup(num.initial.cases = 1,
incfn=incfn,
delayfn = delayfn,
testing = TRUE,
test_delay = 1,
prop.asym=0,
self_report = 0.2,
iso_adhere = 0.9)
case_data2 <- outbreak_step(case_data = case_data,
disp.iso = 1,
disp.com = 1,
r0isolated = 0,
r0community = 500, # Shoiuld get lots of cases
prop.asym = 0,
incfn = incfn,
delayfn = delayfn,
inf_shape = 2.115779,
inf_rate = 0.6898583,
inf_shift = 3,
prop.ascertain = 1,
max_quar_delay = 4,
min_quar_delay = 1,
quarantine = FALSE,
testing = TRUE,
sensitivity = 0.9,
test_delay = 1,
precaution = 5,
self_report = 0.5,
test_asym = F,
iso_adhere = 0.9)
expect_true(all(c(TRUE, FALSE) %in% case_data2$cases$test_result))
})
test_that('Test sensitivity arg',{
inc_meanlog = 1.434065
inc_sdlog = 0.6612
incfn <- dist_setup(dist_param1 = inc_meanlog,
dist_param2 = inc_sdlog,
dist_type = 'lognormal')
delay_shape = 0.9
delayfn <- dist_setup(delay_shape,
1, "adherence")
case_data <- outbreak_setup(num.initial.cases = 1,
incfn=incfn,
delayfn = delayfn,
testing = TRUE,
test_delay = 1,
prop.asym=0,
sensitivity = 0.9,
self_report = 0,
iso_adhere = 0.9)
# generate next generation of cases
# Sensitivity = 1 so should be NAs (missed) and TRUEs but not falses.
case_data2 <- outbreak_step(case_data = case_data,
disp.iso = 1,
disp.com = 1,
r0isolated = 0,
r0community = 500, # Shoiuld get lots of cases
prop.asym = 0,
incfn = incfn,
delayfn = delayfn,
inf_shape = 2.115779,
inf_rate = 0.6898583,
inf_shift = 3,
prop.ascertain = 1,
max_quar_delay = 4,
min_quar_delay = 1,
quarantine = FALSE,
testing = TRUE,
sensitivity = 1,
precaution = 5,
test_delay = 0.3,
self_report = 0.5,
test_asym = F,
iso_adhere = 0.9)
expect_true(all(case_data2$cases$test_result %in% c(TRUE, NA)))
# Missed cases should be na, not missed should be TRUE
expect_true(all(case_data2$cases$test_result[case_data2$cases$missed == FALSE] == TRUE))
expect_true(all(is.na(case_data2$cases$test_result[case_data2$cases$missed == TRUE])))
# generate next generation of cases
# Sensitivity = 0 so should be NAs (missed) and FALSE but no TRUES.
case_data2 <- outbreak_step(case_data = case_data,
disp.iso = 1,
disp.com = 1,
r0isolated = 0,
r0community = 500, # Shoiuld get lots of cases
prop.asym = 0,
incfn = incfn,
delayfn = delayfn,
inf_shape = 2.115779,
inf_rate = 0.6898583,
inf_shift = 3,
prop.ascertain = 1,
max_quar_delay = 4,
min_quar_delay = 1,
quarantine = FALSE,
testing = TRUE,
sensitivity = 0,
precaution = 5,
test_delay = 0.3,
self_report = 0.5,
test_asym = F,
iso_adhere = 0.9)
expect_true(all(case_data2$cases$test_result %in% c(FALSE, NA)))
# Missed cases should be na, not missed should be TRUE
expect_true(all(case_data2$cases$test_result[case_data2$cases$missed == FALSE] == FALSE))
expect_true(all(is.na(case_data2$cases$test_result[case_data2$cases$missed == TRUE])))
# generate next generation of cases
# Sensitivity = 0.5 so should be NAs (missed) and FALSE and TRUES.
case_data2 <- outbreak_step(case_data = case_data,
disp.iso = 1,
disp.com = 1,
r0isolated = 0,
r0community = 500, # Shoiuld get lots of cases
prop.asym = 0,
incfn = incfn,
delayfn = delayfn,
inf_shape = 2.115779,
inf_rate = 0.6898583,
inf_shift = 3,
prop.ascertain = 1,
max_quar_delay = 4,
min_quar_delay = 1,
quarantine = FALSE,
testing = TRUE,
sensitivity = 0.5,
precaution = 5,
test_delay = 0.3,
self_report = 0.5,
test_asym = F,
iso_adhere = 0.9)
expect_true(all(case_data2$cases$test_result %in% c(FALSE, TRUE, NA)))
expect_true(all(c(FALSE, TRUE, NA) %in% case_data2$cases$test_result))
# Missed cases should be na, not missed should be TRUE
expect_true(all(c(TRUE, FALSE) %in% case_data2$cases$test_result[case_data2$cases$missed == FALSE]))
expect_true(all(case_data2$cases$test_result[case_data2$cases$missed == FALSE] %in% c(TRUE, FALSE)))
expect_true(all(is.na(case_data2$cases$test_result[case_data2$cases$missed == TRUE])))
})
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