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Merged
albrja merged 16 commits into from
Feb 5, 2026
Merged

Albrja/mic 6782/update testing #66

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1ecfab4
Change on_time_step metho in treatment to on_time_step_cleanup
albrja Jan 30, 2026
200a272
Update ramp start and artifact paths
albrja Jan 30, 2026
dc967ad
Revert config changes
albrja Jan 30, 2026
3ca13e5
Update treatment and testing ramp up dates
albrja Jan 31, 2026
d76eef7
Add constant for time step interval
albrja Jan 31, 2026
359c464
Update bbbm testing history method to record next test date
albrja Jan 31, 2026
04df9e1
Update bbbm testing update method
albrja Jan 31, 2026
fd66e22
Update initial options for steps
albrja Jan 31, 2026
5d0f6b5
Update implementation
albrja Feb 2, 2026
da2c51f
Update implementation to use second column for future test date
albrja Feb 2, 2026
ef38c5e
Revert observer changes
albrja Feb 2, 2026
3ba2e1c
Config for runs
albrja Feb 3, 2026
03a1f05
Revert config
albrja Feb 3, 2026
422114f
Merge branch 'main' into albrja/mic-6782/update-testing
albrja Feb 4, 2026
acbd47e
Address pr comments
albrja Feb 4, 2026
d81bc08
Update bbbm positive result probability
albrja Feb 4, 2026
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10 changes: 7 additions & 3 deletions src/vivarium_csu_alzheimers/components/observers.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -10,9 +10,9 @@
BBBM_AGE_MAX,
BBBM_AGE_MIN,
BBBM_TEST_RESULTS,
BBBM_TIMESTEPS_UNTIL_RETEST,
COLUMNS,
TESTING_STATES,
TIME_STEPS_UNTIL_NEXT_BBBM_TEST,
)
from vivarium_csu_alzheimers.constants.models import ALZHEIMERS_DISEASE_MODEL
from vivarium_csu_alzheimers.utilities import get_timedelta_from_step_size
Expand Down Expand Up @@ -257,10 +257,12 @@ def count_newly_eligible_simulants(self, pop: pd.DataFrame) -> float:
aged_in = (pop[COLUMNS.AGE] >= BBBM_AGE_MIN) & (
pop[COLUMNS.AGE] < BBBM_AGE_MIN + step_size_in_years
)
# Retest population will be simulants with previous test date >= 3 years ago,
# so we find newly eligible simulants whose last test is exactly 6 time steps ago
retest = pop[
COLUMNS.BBBM_TEST_DATE
] == self.clock() + self.step_size() - get_timedelta_from_step_size(
self.step_size().days, BBBM_TIMESTEPS_UNTIL_RETEST
self.step_size().days, min(TIME_STEPS_UNTIL_NEXT_BBBM_TEST)
)

return sum(eligible_baseline & (new_entrants | aged_in | retest))
Expand All @@ -273,7 +275,9 @@ def aggregate_eligible_person_time(self, pop: pd.DataFrame) -> float:
eligible_history = (pop[COLUMNS.BBBM_TEST_DATE].isna()) | (
pop[COLUMNS.BBBM_TEST_DATE]
<= event_time
- get_timedelta_from_step_size(self.step_size().days, BBBM_TIMESTEPS_UNTIL_RETEST)
- get_timedelta_from_step_size(
self.step_size().days, min(TIME_STEPS_UNTIL_NEXT_BBBM_TEST)
)
)
eligible_results = pop[COLUMNS.BBBM_TEST_RESULT] != BBBM_TEST_RESULTS.POSITIVE

Expand Down
68 changes: 45 additions & 23 deletions src/vivarium_csu_alzheimers/components/testing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,9 +17,9 @@
BBBM_TEST_RESULTS,
BBBM_TESTING_RATES,
BBBM_TESTING_START_DATE,
BBBM_TIMESTEPS_UNTIL_RETEST,
COLUMNS,
TESTING_STATES,
TIME_STEPS_UNTIL_NEXT_BBBM_TEST,
)
from vivarium_csu_alzheimers.constants.models import ALZHEIMERS_DISEASE_MODEL
from vivarium_csu_alzheimers.utilities import get_timedelta_from_step_size
Expand All @@ -39,6 +39,7 @@ def columns_created(self) -> list[str]:
COLUMNS.TESTING_PROPENSITY,
COLUMNS.TESTING_STATE,
COLUMNS.BBBM_TEST_DATE,
COLUMNS.NEXT_BBBM_TEST_DATE,
COLUMNS.BBBM_TEST_RESULT,
COLUMNS.BBBM_TEST_EVER_ELIGIBLE,
]
Expand Down Expand Up @@ -77,6 +78,7 @@ def on_initialize_simulants(self, pop_data: SimulantData) -> None:
pop[COLUMNS.BBBM_TEST_RESULT] = BBBM_TEST_RESULTS.NOT_TESTED
pop[COLUMNS.BBBM_TEST_EVER_ELIGIBLE] = False
pop[COLUMNS.BBBM_TEST_DATE] = pd.NaT
pop[COLUMNS.NEXT_BBBM_TEST_DATE] = pd.NaT

# Update to reflect history
event_time = pop_data.creation_time + get_timedelta_from_step_size(self.step_size)
Expand All @@ -86,13 +88,13 @@ def on_initialize_simulants(self, pop_data: SimulantData) -> None:
test_history_mask = bbbm_eligible_mask & (
pop[COLUMNS.TESTING_PROPENSITY] < testing_rate
)
pop[COLUMNS.BBBM_TEST_DATE] = self._generate_bbbm_testing_history(
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@stevebachmeier stevebachmeier Feb 4, 2026

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don't we still need to provide the historical date? Or is it just not useful since it's in the past at that point?

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@albrja albrja Feb 4, 2026

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We do not need history necessarily, we just need a way to sample 3-5 years instead of every 3 years so we are generating future tests since that is easier.

pop[COLUMNS.NEXT_BBBM_TEST_DATE] = self._generate_bbbm_testing_data(
pop, test_history_mask, event_time
)

self._update_baseline_testing(pop)

bbbm_tested_now_mask = pop[COLUMNS.BBBM_TEST_DATE] == event_time
bbbm_tested_now_mask = pop[COLUMNS.NEXT_BBBM_TEST_DATE] == event_time
self._update_bbbm_testing(
pop,
bbbm_tested_now_mask,
Expand Down Expand Up @@ -168,19 +170,29 @@ def _update_bbbm_testing(
)

# Update BBBM-specific columns for those who had BBBM tests
pop.loc[tested_mask, COLUMNS.BBBM_TEST_DATE] = event_time
pop.loc[tested_mask, COLUMNS.TESTING_STATE] = TESTING_STATES.BBBM
pop.loc[tested_mask, COLUMNS.BBBM_TEST_RESULT] = test_results
pop.loc[tested_mask, COLUMNS.BBBM_TEST_DATE] = event_time
# Choose next test date for negative tests
negative_test = (test_results == BBBM_TEST_RESULTS.NEGATIVE) & tested_mask
time_steps_until_next_test = self.randomness.choice(
index=pop[negative_test].index,
choices=TIME_STEPS_UNTIL_NEXT_BBBM_TEST,
additional_key="bbbm_time_until_next_test",
)
pop.loc[
negative_test, COLUMNS.NEXT_BBBM_TEST_DATE
] = event_time + get_timedelta_from_step_size(
self.step_size, time_steps_until_next_test
)

def _get_bbbm_eligible_simulants(
self, pop: pd.DataFrame, event_time: Time
) -> pd.Series[bool]:
eligible_state = pop[COLUMNS.DISEASE_STATE] == ALZHEIMERS_DISEASE_MODEL.BBBM_STATE
eligible_age = (pop[COLUMNS.AGE] >= BBBM_AGE_MIN) & (pop[COLUMNS.AGE] < BBBM_AGE_MAX)
eligible_history = (pop[COLUMNS.BBBM_TEST_DATE].isna()) | (
pop[COLUMNS.BBBM_TEST_DATE]
<= event_time
- get_timedelta_from_step_size(self.step_size, BBBM_TIMESTEPS_UNTIL_RETEST)
eligible_history = (pop[COLUMNS.NEXT_BBBM_TEST_DATE].isna()) | (
pop[COLUMNS.NEXT_BBBM_TEST_DATE] <= event_time
)
eligible_results = pop[COLUMNS.BBBM_TEST_RESULT] != BBBM_TEST_RESULTS.POSITIVE
return eligible_state & eligible_age & eligible_history & eligible_results
Expand All @@ -202,27 +214,37 @@ def _get_bbbm_testing_rate(self, event_time: pd.Timestamp) -> float:

return np.interp(event_time.value, timestamps, rates)

def _generate_bbbm_testing_history(
def _generate_bbbm_testing_data(
self, simulants: pd.DataFrame, eligible_sims: pd.Series, time_of_event: Time
) -> pd.Series[Time]:
"""Generates previous BBBM test data for new simulants between 0 and 2.5 years prior
to entering the simulation."""

test_dates = simulants[COLUMNS.BBBM_TEST_DATE].copy()
"""Generates BBBM test data for new simulants next BBBM test 0 to 4.5 into the future.
This will sa mple the 3-5 year range for time until the next test. It will then sample
how far along that simulant is in that range to determine the test date. For example,
if a simulant is intering the simulation on 2030-01-01, and CRN samples that their test
will be 4 years after their previous test, then then will take a sample between 0 and 4
years. If that sample is 1 year, their COLUMNS.BBBM_TEST_DATE will be 2033-01-01.
"""

test_dates = simulants[COLUMNS.NEXT_BBBM_TEST_DATE].copy()
if not self.scenario.bbbm_testing or time_of_event < BBBM_TESTING_START_DATE:
return test_dates

test_date_options = [
time_of_event + get_timedelta_from_step_size(self.step_size, time_step)
for time_step in range(0, -6, -1)
if time_of_event + get_timedelta_from_step_size(self.step_size, time_step)
>= BBBM_TESTING_START_DATE
]
# Calculate test results
test_dates.loc[eligible_sims] = self.randomness.choice(
time_steps_until_next_test = self.randomness.choice(
index=simulants[eligible_sims].index,
choices=test_date_options,
additional_key="bbbm_test_date_history",
choices=TIME_STEPS_UNTIL_NEXT_BBBM_TEST[:-1],
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@stevebachmeier stevebachmeier Feb 4, 2026

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Remind me why we exclude the last one?

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@albrja albrja Feb 4, 2026

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We know that simulants will have a test at most 5 years (10 time steps) from their last test. We are going to have simulants test now, or in one of the next 9 time steps.

additional_key="bbbm_time_until_next_test_history",
)
# Sample uniformly from [0, time_steps_until_next_test] for each simulant
time_steps_into_test_interval = np.round(
self.randomness.get_draw(
index=simulants[eligible_sims].index,
additional_key="bbbm_time_into_test_interval_history",
)
* time_steps_until_next_test
)
steps_until_next_test = time_steps_until_next_test - time_steps_into_test_interval
test_dates.loc[eligible_sims] = time_of_event + get_timedelta_from_step_size(
self.step_size, steps_until_next_test
)

return test_dates
32 changes: 10 additions & 22 deletions src/vivarium_csu_alzheimers/constants/data_values.py
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Original file line number Diff line number Diff line change
Expand Up @@ -88,6 +88,7 @@ class __Columns(NamedTuple):
f"{TREATMENT_DISEASE_MODEL.NO_EFFECT_NEVER_TREATED_STATE}_event_count"
)
TREATMENT_DURATION: str = "treatment_duration"
NEXT_BBBM_TEST_DATE: str = "next_bbbm_test_date"


COLUMNS = __Columns()
Expand Down Expand Up @@ -153,10 +154,9 @@ class TestingRates(NamedTuple):
}


BBBM_AGE_MIN = 60
BBBM_AGE_MIN = 65
BBBM_AGE_MAX = 80
BBBM_TIMESTEPS_UNTIL_RETEST = 6 # three years b/c time step is ~6 months
BBBM_POSITIVE_DIAGNOSIS_PROBABILITY = 0.9
BBBM_POSITIVE_DIAGNOSIS_PROBABILITY = 0.5


class __BBBMTestResults(NamedTuple):
Expand All @@ -167,34 +167,22 @@ class __BBBMTestResults(NamedTuple):

BBBM_TEST_RESULTS = __BBBMTestResults()

# bbbm testing rates are piecewise-linear starting at 2030 and maxing out in 2045
# bbbm testing rates are piecewise-linear starting at 2027 and maxing out in 2045
BBBM_TESTING_RATES = [
(pd.Timestamp("2030-01-01"), 0.1), # step increase from 0 in 2030
(pd.Timestamp("2035-01-01"), 0.2),
(pd.Timestamp("2040-01-01"), 0.4),
(pd.Timestamp("2045-01-01"), 0.6), # plateaus from here on out
(pd.Timestamp("2027-01-01"), 0.0), # step increase from 0 in 2027
(pd.Timestamp("2035-01-01") + pd.Timedelta(days=182), 0.1),
(pd.Timestamp("2045-01-01"), 0.5),
(pd.Timestamp("2055-01-01"), 0.6), # plateaus from here on out
]
BBBM_TESTING_START_DATE = BBBM_TESTING_RATES[0][0]
TIME_STEPS_UNTIL_NEXT_BBBM_TEST = [6.0, 7.0, 8.0, 9.0, 10.0]

# TODO: update time start of ramp with test model updates
TREATMENT_PROBS_RAMP = [
(pd.Timestamp("2030-01-01"), 0.0),
(pd.Timestamp("2027-01-01"), 0.0),
(pd.Timestamp("2035-01-01") + pd.Timedelta(days=182), 0.3),
(pd.Timestamp("2100-01-01"), 0.8),
]

# LOCATION_TREATMENT_PROBS = {
# "united_states_of_america": 0.3,
# "germany": TREATMENT_PROBS_RAMP,
# "spain": TREATMENT_PROBS_RAMP,
# "sweden": TREATMENT_PROBS_RAMP,
# "united_kingdom": TREATMENT_PROBS_RAMP,
# "japan": 0.8,
# "israel": TREATMENT_PROBS_RAMP,
# "taiwan_(province_of_china)": TREATMENT_PROBS_RAMP,
# "brazil": TREATMENT_PROBS_RAMP,
# "china": TREATMENT_PROBS_RAMP,
# }
DWELL_TIME_AWAITING_EFFECT_TIMESTEPS = 1 # 6 months
DWELL_TIME_TREATMENT_EFFECT_TIMESTEPS = 12 # 6 years
DWELL_TIME_WANING_EFFECT_TIMESTEPS = 22 # 11 years
Expand Down
1 change: 0 additions & 1 deletion src/vivarium_csu_alzheimers/utilities.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,6 @@
from vivarium_public_health.risks.data_transformations import pivot_categorical

from vivarium_csu_alzheimers.constants import metadata
from vivarium_csu_alzheimers.constants.data_values import BBBM_TIMESTEPS_UNTIL_RETEST


def len_longest_location() -> int:
Expand Down