def test_get_crude_birth_rate():
country_iso_code = "AUS"
birth_rates, years = get_crude_birth_rate(country_iso_code)
assert years == [
1952.5,
1957.5,
1962.5,
1967.5,
1972.5,
1977.5,
1982.5,
1987.5,
1992.5,
1997.5,
2002.5,
2007.5,
2012.5,
2017.5,
]
assert birth_rates == [
22.981,
22.742,
21.427,
19.932,
19.124,
15.847,
15.556,
15.100,
14.768,
13.524,
12.801,
13.800,
13.379,
12.879,
]
def build_model(params: dict) -> StratifiedModel:
"""
Build the master function to run the TB model for the Republic of the Marshall Islands
:param update_params: dict
Any parameters that need to be updated for the current run
:return: StratifiedModel
The final model with all parameters and stratifications
"""
# Define compartments and initial conditions.
compartments = [
Compartment.SUSCEPTIBLE,
Compartment.EARLY_LATENT,
Compartment.LATE_LATENT,
Compartment.EARLY_INFECTIOUS,
Compartment.ON_TREATMENT,
Compartment.RECOVERED,
# Compartment.LTBI_TREATED,
]
init_pop = {Compartment.EARLY_INFECTIOUS: 10, Compartment.LATE_LATENT: 100}
model_parameters = params
# Update partial immunity/susceptibility parameters
model_parameters = update_transmission_parameters(
model_parameters,
[
Compartment.RECOVERED, Compartment.LATE_LATENT,
Compartment.LTBI_TREATED
],
)
# Set integration times
integration_times = get_model_times_from_inputs(
model_parameters["start_time"],
model_parameters["end_time"],
model_parameters["time_step"],
)
# Sequentially add groups of flows to flows list
flows = add_standard_infection_flows([])
flows = add_standard_latency_flows(flows)
flows = add_standard_natural_history_flows(flows)
# flows = add_latency_progression(flows)
flows = add_case_detection(flows, compartments)
flows = add_treatment_flows(flows)
# flows = add_acf(flows, compartments)
# flows = add_acf_ltbi(flows)
# Make sure incidence and notifications are tracked during integration
out_connections = {}
out_connections.update(
create_request_stratified_incidence(
model_parameters["incidence_stratification"],
model_parameters["all_stratifications"],
))
out_connections.update(
create_request_stratified_notifications(
model_parameters["notification_stratifications"],
model_parameters["all_stratifications"],
))
# Define model
tb_model = StratifiedModel(
integration_times,
compartments,
init_pop,
model_parameters,
flows,
birth_approach="add_crude_birth_rate",
starting_population=model_parameters["start_population"],
output_connections=out_connections,
death_output_categories=list_all_strata_for_mortality(compartments),
)
# Add crude birth rate from UN estimates (using Federated States of Micronesia as a proxy as no data for RMI)
birth_rates, years = inputs.get_crude_birth_rate("FSM")
tb_model.time_variants["crude_birth_rate"] = scale_up_function(
years, birth_rates, smoothness=0.2, method=5)
# Find raw case detection rate with multiplier, which is 1 by default, and adjust for differences by organ status
cdr_scaleup_raw = build_scale_up_function(
model_parameters["cdr"], model_parameters["cdr_multiplier"])
detect_rate_by_organ = find_organ_specific_cdr(
cdr_scaleup_raw,
model_parameters,
model_parameters["all_stratifications"]["organ"],
target_organ_props=model_parameters["target_organ_props"],
)
# Find base case detection rate and time-variant treatment completion function
base_detection_rate = detect_rate_by_organ[
"smearpos" if "organ" in
model_parameters["stratify_by"] else "overall"]
treatment_success_rate = (
lambda time: build_scale_up_function(model_parameters["tsr"])
(time) / model_parameters["treatment_duration"])
treatment_nonsuccess_rate = (
lambda time: (1.0 - build_scale_up_function(model_parameters["tsr"])
(time)) / model_parameters["treatment_duration"])
# Set acf screening rate using proportion of population reached and duration of intervention
# acf_screening_rate = -numpy.log(1 - 0.9) / 0.5
# acf_rate_over_time = progressive_step_function_maker(
# 2018.2, 2018.7, acf_screening_rate, scaling_time_fraction=0.3
# )
# Initialise acf_rate function
# acf_rate_function = (
# lambda t: model_parameters["acf_coverage"]
# * (acf_rate_over_time(t))
# * model_parameters["acf_sensitivity"]
# )
# acf_ltbi_rate_function = (
# lambda t: model_parameters["acf_coverage"]
# * (acf_rate_over_time(t))
# * model_parameters["acf_ltbi_sensitivity"]
# * model_parameters["acf_ltbi_efficacy"]
# )
# Assign newly created functions to model parameters
add_time_variant_parameter_to_model(
tb_model,
"case_detection",
base_detection_rate,
len(model_parameters["stratify_by"]),
)
add_time_variant_parameter_to_model(
tb_model,
"treatment_success",
treatment_success_rate,
len(model_parameters["stratify_by"]),
)
add_time_variant_parameter_to_model(
tb_model,
"treatment_nonsuccess",
treatment_nonsuccess_rate,
len(model_parameters["stratify_by"]),
)
# add_time_variant_parameter_to_model(
# tb_model, 'acf_rate', acf_rate_function, len(model_parameters['stratify_by']))
# add_time_variant_parameter_to_model(
# tb_model, 'acf_ltbi_rate', acf_ltbi_rate_function, len(model_parameters['stratify_by']))
# Stratification processes
if "age" in model_parameters["stratify_by"]:
age_specific_latency_parameters = manually_create_age_specific_latency_parameters(
model_parameters)
tb_model = stratify_by_age(
tb_model,
age_specific_latency_parameters,
model_parameters["all_stratifications"]["age"],
)
if "diabetes" in model_parameters["stratify_by"]:
tb_model = stratify_by_diabetes(
tb_model,
model_parameters,
model_parameters["all_stratifications"]["diabetes"],
model_parameters["diabetes_target_props"],
age_specific_prevalence=False,
)
if "organ" in model_parameters["stratify_by"]:
tb_model = stratify_by_organ(
tb_model,
model_parameters,
detect_rate_by_organ,
model_parameters["all_stratifications"]["organ"],
)
if "location" in model_parameters["stratify_by"]:
tb_model = stratify_by_location(
tb_model,
model_parameters,
model_parameters["all_stratifications"]["location"],
)
# Capture reported prevalence in Majuro assuming over-reporting (needed for calibration)
def calculate_reported_majuro_prevalence(model, time):
true_prev = 0.0
pop_majuro = 0.0
for i, compartment in enumerate(model.compartment_names):
if "majuro" in compartment:
pop_majuro += model.compartment_values[i]
if "infectious" in compartment:
true_prev += model.compartment_values[i]
return (
1.0e5 * true_prev / pop_majuro *
(1.0 + model_parameters["over_reporting_prevalence_proportion"]))
tb_model.derived_output_functions.update(
{"reported_majuro_prevalence": calculate_reported_majuro_prevalence})
return tb_model
def build_model(params: dict) -> StratifiedModel:
external_params = deepcopy(params)
model_parameters = {
"contact_rate":
external_params["contact_rate"],
"contact_rate_recovered":
external_params["contact_rate"] *
external_params["rr_transmission_recovered"],
"contact_rate_late_latent":
external_params["contact_rate"] *
external_params["rr_transmission_late_latent"],
"recovery":
external_params["self_recovery_rate"],
"infect_death":
external_params["tb_mortality_rate"],
**external_params,
}
stratify_by = external_params["stratify_by"]
derived_output_types = external_params["derived_outputs"]
n_iter = (int(
round((external_params["end_time"] - external_params["start_time"]) /
external_params["time_step"])) + 1)
integration_times = numpy.linspace(external_params["start_time"],
external_params["end_time"],
n_iter).tolist()
model_parameters.update(
change_parameter_unit(provide_aggregated_latency_parameters(),
365.251))
# sequentially add groups of flows
flows = add_standard_infection_flows([])
flows = add_standard_latency_flows(flows)
flows = add_standard_natural_history_flows(flows)
# compartments
compartments = [
"susceptible",
"early_latent",
"late_latent",
"infectious",
"recovered",
]
# define model #replace_deaths add_crude_birth_rate
init_pop = {"infectious": 1000, "late_latent": 1000000}
tb_model = StratifiedModel(
integration_times,
compartments,
init_pop,
model_parameters,
flows,
birth_approach="replace_deaths",
starting_population=external_params["start_population"],
output_connections={},
derived_output_functions={},
death_output_categories=((), ("age_0", )),
)
# add crude birth rate from un estimates
birth_rates, years = get_crude_birth_rate("MNG")
# Provisional patch to birth rates
for i in range(len(birth_rates)):
if years[i] > 1990.0:
birth_rates[i] = 0.04
tb_model.time_variants["crude_birth_rate"] = scale_up_function(
years, birth_rates, smoothness=0.2, method=5)
# add case detection process to basic model
tb_model.add_transition_flow({
"type": "standard_flows",
"parameter": "case_detection",
"origin": "infectious",
"to": "recovered",
})
# Add IPT as a customised flow
def ipt_flow_func(model, n_flow, _time, _compartment_values):
"""
Work out the number of detected individuals from the relevant active TB compartments (with regard to the origin
latent compartment of n_flow) multiplied with the proportion of the relevant infected contacts that is from this
latent compartment.
"""
dict_flows = model.transition_flows_dict
origin_comp_name = dict_flows["origin"][n_flow]
components_latent_comp = find_name_components(origin_comp_name)
# find compulsory tags to be found in relevant infectious compartments
tags = []
for component in components_latent_comp:
if "location_" in component or "strain_" in component:
tags.append(component)
# loop through all relevant infectious compartments
total_tb_detected = 0.0
for comp_ind in model.infectious_indices["all_strains"]:
active_components = find_name_components(
model.compartment_names[comp_ind])
if all(elem in active_components for elem in tags):
infectious_pop = _compartment_values[comp_ind]
detection_indices = [
index for index, val in dict_flows["parameter"].items()
if "case_detection" in val
]
flow_index = [
index for index in detection_indices
if dict_flows["origin"][index] ==
model.compartment_names[comp_ind]
][0]
param_name = dict_flows["parameter"][flow_index]
detection_tx_rate = model.get_parameter_value(
param_name, _time)
tsr = mongolia_tsr(_time) + external_params[
"reduction_negative_tx_outcome"] * (1.0 -
mongolia_tsr(_time))
if "strain_mdr" in model.compartment_names[comp_ind]:
tsr = external_params["mdr_tsr"] * external_params[
"prop_mdr_detected_as_mdr"]
if tsr > 0.0:
total_tb_detected += infectious_pop * detection_tx_rate / tsr
# list all latent compartments relevant to the relevant infectious population
relevant_latent_compartments_indices = [
i for i, comp_name in enumerate(model.compartment_names)
if find_stem(comp_name) == "early_latent" and all(elem in comp_name
for elem in tags)
]
total_relevant_latent_size = sum(
_compartment_values[i]
for i in relevant_latent_compartments_indices)
current_latent_size = _compartment_values[
model.compartment_names.index(origin_comp_name)]
prop_of_relevant_latent = (current_latent_size /
total_relevant_latent_size if
total_relevant_latent_size > 0.0 else 0.0)
return total_tb_detected * prop_of_relevant_latent
tb_model.add_transition_flow({
"type": "customised_flows",
"parameter": "ipt_rate",
"origin": "early_latent",
"to": "recovered",
"function": ipt_flow_func,
})
# add ACF flow
tb_model.add_transition_flow({
"type": "standard_flows",
"parameter": "acf_rate",
"origin": "infectious",
"to": "recovered",
})
# load time-variant case detection rate
cdr_scaleup_overall = build_mongolia_timevariant_cdr(
external_params["cdr_multiplier"])
# targeted TB prevalence proportions by organ
prop_smearpos = 0.25
prop_smearneg = 0.40
prop_extrapul = 0.35
# disease duration by organ
overall_duration = prop_smearpos * 1.6 + 5.3 * (1 - prop_smearpos)
disease_duration = {
"smearpos": 1.6,
"smearneg": 5.3,
"extrapul": 5.3,
"overall": overall_duration,
}
# work out the CDR for smear-positive TB
def cdr_smearpos(time):
# Had to replace external_params['diagnostic_sensitivity_smearneg'] with its hard-coded value .7 to avoid
# cdr_smearpos to be affected when increasing diagnostic_sensitivity_smearneg in interventions (e.g. Xpert)
# return (cdr_scaleup_overall(time) /
# (prop_smearpos + prop_smearneg * external_params['diagnostic_sensitivity_smearneg'] +
# prop_extrapul * external_params['diagnostic_sensitivity_extrapul']))
return cdr_scaleup_overall(time) / (
prop_smearpos + prop_smearneg * 0.7 +
prop_extrapul * external_params["diagnostic_sensitivity_extrapul"])
def cdr_smearneg(time):
return cdr_smearpos(
time) * external_params["diagnostic_sensitivity_smearneg"]
def cdr_extrapul(time):
return cdr_smearpos(
time) * external_params["diagnostic_sensitivity_extrapul"]
cdr_by_organ = {
"smearpos": cdr_smearpos,
"smearneg": cdr_smearneg,
"extrapul": cdr_extrapul,
"overall": cdr_scaleup_overall,
}
detect_rate_by_organ = {}
for organ in ["smearpos", "smearneg", "extrapul", "overall"]:
prop_to_rate = convert_competing_proportion_to_rate(
1.0 / disease_duration[organ])
detect_rate_by_organ[organ] = return_function_of_function(
cdr_by_organ[organ], prop_to_rate)
# load time-variant treatment success rate
mongolia_tsr = build_mongolia_timevariant_tsr()
# create a treatment succes rate function adjusted for treatment support intervention
tsr_function = lambda t: mongolia_tsr(t) + external_params[
"reduction_negative_tx_outcome"] * (1.0 - mongolia_tsr(t))
# tb control recovery rate (detection and treatment) function set for overall if not organ-specific, smearpos otherwise
if "organ" not in stratify_by:
tb_control_recovery_rate = lambda t: tsr_function(
t) * detect_rate_by_organ["overall"](t)
else:
tb_control_recovery_rate = lambda t: tsr_function(
t) * detect_rate_by_organ["smearpos"](t)
# initialise ipt_rate function assuming coverage of 1.0 before age stratification
ipt_rate_function = (lambda t: 1.0 * external_params[
"yield_contact_ct_tstpos_per_detected_tb"] * external_params[
"ipt_efficacy"])
# initialise acf_rate function
acf_rate_function = (
lambda t: external_params["acf_coverage"] * external_params[
"acf_sensitivity"] *
(mongolia_tsr(t) + external_params["reduction_negative_tx_outcome"] *
(1.0 - mongolia_tsr(t))))
# assign newly created functions to model parameters
tb_model.adaptation_functions["case_detection"] = tb_control_recovery_rate
tb_model.parameters["case_detection"] = "case_detection"
tb_model.adaptation_functions["ipt_rate"] = ipt_rate_function
tb_model.parameters["ipt_rate"] = "ipt_rate"
tb_model.adaptation_functions["acf_rate"] = acf_rate_function
tb_model.parameters["acf_rate"] = "acf_rate"
if "strain" in stratify_by:
mdr_adjustment = (external_params["prop_mdr_detected_as_mdr"] *
external_params["mdr_tsr"] / 0.9
) # /.9 for last DS TSR
tb_model.stratify(
"strain",
["ds", "mdr"],
["early_latent", "late_latent", "infectious"],
verbose=False,
requested_proportions={"mdr": 0.0},
adjustment_requests={
"contact_rate": {
"ds": 1.0,
"mdr": 1.0
},
"case_detection": {
"mdr": mdr_adjustment
},
"ipt_rate": {
"ds": 1.0, # external_params['ds_ipt_switch'],
"mdr": external_params["mdr_ipt_switch"],
},
},
infectiousness_adjustments={
"ds": 1.0,
"mdr": external_params["mdr_infectiousness_multiplier"],
},
)
tb_model.add_transition_flow({
"type":
"standard_flows",
"parameter":
"dr_amplification",
"origin":
"infectiousXstrain_ds",
"to":
"infectiousXstrain_mdr",
"implement":
len(tb_model.all_stratifications),
})
dr_amplification_rate = (
lambda t: detect_rate_by_organ["overall"](t) *
(1.0 - mongolia_tsr(t)) *
(1.0 - external_params["reduction_negative_tx_outcome"]
) * external_params["dr_amplification_prop_among_nonsuccess"])
tb_model.adaptation_functions[
"dr_amplification"] = dr_amplification_rate
tb_model.parameters["dr_amplification"] = "dr_amplification"
if "age" in stratify_by:
age_breakpoints = [0, 5, 15, 60]
age_infectiousness = get_parameter_dict_from_function(
logistic_scaling_function(10.0), age_breakpoints)
age_params = get_adapted_age_parameters(age_breakpoints)
age_params.update(split_age_parameter(age_breakpoints, "contact_rate"))
# adjustment of latency parameters
for param in ["early_progression", "late_progression"]:
for age_break in age_breakpoints:
if age_break > 5:
age_params[param][
str(age_break) +
"W"] *= external_params["adult_latency_adjustment"]
death_rates_by_age, death_rate_years = get_death_rates_by_agegroup(
age_breakpoints, "MNG")
pop_morts = {}
for age_group in age_breakpoints:
pop_morts[age_group] = scale_up_function(
death_rate_years,
death_rates_by_age[age_group],
smoothness=0.2,
method=5)
age_params["universal_death_rate"] = {}
for age_break in age_breakpoints:
tb_model.time_variants["universal_death_rateXage_" +
str(age_break)] = pop_morts[age_break]
tb_model.parameters[
"universal_death_rateXage_" +
str(age_break)] = "universal_death_rateXage_" + str(age_break)
age_params["universal_death_rate"][
str(age_break) +
"W"] = "universal_death_rateXage_" + str(age_break)
tb_model.parameters["universal_death_rateX"] = 0.0
# age-specific IPT
ipt_by_age = {"ipt_rate": {}}
for age_break in age_breakpoints:
ipt_by_age["ipt_rate"][str(age_break)] = external_params[
"ipt_age_" + str(age_break) + "_ct_coverage"]
age_params.update(ipt_by_age)
# add BCG effect without stratification assuming constant 100% coverage
bcg_wane = create_sloping_step_function(15.0, 0.3, 30.0, 1.0)
age_bcg_efficacy_dict = get_parameter_dict_from_function(
lambda value: bcg_wane(value), age_breakpoints)
age_params.update({"contact_rate": age_bcg_efficacy_dict})
tb_model.stratify(
"age",
deepcopy(age_breakpoints),
[],
{},
adjustment_requests=age_params,
infectiousness_adjustments=age_infectiousness,
verbose=False,
)
# patch for IPT to overwrite parameters when ds_ipt has been turned off while we still need some coverage at baseline
if external_params["ds_ipt_switch"] == 0.0 and external_params[
"mdr_ipt_switch"] == 1.0:
tb_model.parameters["ipt_rateXstrain_dsXage_0"] = 0.17
for age_break in [5, 15, 60]:
tb_model.parameters["ipt_rateXstrain_dsXage_" +
str(age_break)] = 0.0
if "organ" in stratify_by:
props_smear = {
"smearpos": external_params["prop_smearpos"],
"smearneg": 1.0 - (external_params["prop_smearpos"] + 0.20),
"extrapul": 0.20,
}
mortality_adjustments = {
"smearpos": 1.0,
"smearneg": 0.064,
"extrapul": 0.064
}
recovery_adjustments = {
"smearpos": 1.0,
"smearneg": 0.56,
"extrapul": 0.56
}
# workout the detection rate adjustment by organ status
adjustment_smearneg = (detect_rate_by_organ["smearneg"](2015.0) /
detect_rate_by_organ["smearpos"](2015.0) if
detect_rate_by_organ["smearpos"](2015.0) > 0.0
else 1.0)
adjustment_extrapul = (detect_rate_by_organ["extrapul"](2015.0) /
detect_rate_by_organ["smearpos"](2015.0) if
detect_rate_by_organ["smearpos"](2015.0) > 0.0
else 1.0)
tb_model.stratify(
"organ",
["smearpos", "smearneg", "extrapul"],
["infectious"],
infectiousness_adjustments={
"smearpos": 1.0,
"smearneg": 0.25,
"extrapul": 0.0,
},
verbose=False,
requested_proportions=props_smear,
adjustment_requests={
"recovery": recovery_adjustments,
"infect_death": mortality_adjustments,
"case_detection": {
"smearpos": 1.0,
"smearneg": adjustment_smearneg,
"extrapul": adjustment_extrapul,
},
"early_progression": props_smear,
"late_progression": props_smear,
},
)
if "location" in stratify_by:
props_location = {
"rural_province": 0.48,
"urban_nonger": 0.368,
"urban_ger": 0.15,
"prison": 0.002,
}
raw_relative_risks_loc = {"rural_province": 1.0}
for stratum in ["urban_nonger", "urban_ger", "prison"]:
raw_relative_risks_loc[stratum] = external_params[
"rr_transmission_" + stratum]
scaled_relative_risks_loc = scale_relative_risks_for_equivalence(
props_location, raw_relative_risks_loc)
# dummy matrix for mixing by location
location_mixing = numpy.array([
0.899,
0.05,
0.05,
0.001,
0.049,
0.7,
0.25,
0.001,
0.049,
0.25,
0.7,
0.001,
0.1,
0.1,
0.1,
0.7,
]).reshape((4, 4))
location_mixing *= 3.0 # adjusted such that heterogeneous mixing yields similar overall burden as homogeneous
location_adjustments = {}
for beta_type in ["", "_late_latent", "_recovered"]:
location_adjustments["contact_rate" +
beta_type] = scaled_relative_risks_loc
location_adjustments["acf_rate"] = {}
for stratum in [
"rural_province", "urban_nonger", "urban_ger", "prison"
]:
location_adjustments["acf_rate"][stratum] = external_params[
"acf_" + stratum + "_switch"]
tb_model.stratify(
"location",
["rural_province", "urban_nonger", "urban_ger", "prison"],
[],
requested_proportions=props_location,
verbose=False,
entry_proportions=props_location,
adjustment_requests=location_adjustments,
mixing_matrix=location_mixing,
)
# tb_model.transition_flows.to_csv("transitions.csv")
# tb_model.death_flows.to_csv("deaths.csv")
# create some customised derived_outputs
if "notifications" in derived_output_types:
def notification_function_builder(stratum):
"""
example of stratum: "Xage_0Xstrain_mdr"
"""
def calculate_notifications(model, time):
total_notifications = 0.0
dict_flows = model.transition_flows_dict
comp_ind = model.compartment_names.index("infectious" +
stratum)
infectious_pop = model.compartment_values[comp_ind]
detection_indices = [
index for index, val in dict_flows["parameter"].items()
if "case_detection" in val
]
flow_index = [
index for index in detection_indices
if dict_flows["origin"][index] ==
model.compartment_names[comp_ind]
][0]
param_name = dict_flows["parameter"][flow_index]
detection_tx_rate = model.get_parameter_value(param_name, time)
tsr = mongolia_tsr(time) + external_params[
"reduction_negative_tx_outcome"] * (1.0 -
mongolia_tsr(time))
if "strain_mdr" in model.compartment_names[comp_ind]:
tsr = external_params["mdr_tsr"] * external_params[
"prop_mdr_detected_as_mdr"]
if tsr > 0.0:
total_notifications += infectious_pop * detection_tx_rate / tsr
return total_notifications
return calculate_notifications
for compartment in tb_model.compartment_names:
if "infectious" in compartment:
stratum = compartment.split("infectious")[1]
tb_model.derived_output_functions[
"notifications" +
stratum] = notification_function_builder(stratum)
# tb_model.derived_output_functions['popsize_treatment_support' + stratum] = notification_function_builder(stratum)
if "incidence" in derived_output_types:
# add output_connections for all stratum-specific incidence outputs
incidence_output_conns = create_output_connections_for_incidence_by_stratum(
tb_model.compartment_names)
tb_model.output_connections.update(incidence_output_conns)
# Create a 'combined incidence' derived output
early_names = [
k for k in incidence_output_conns.keys()
if k.startswith("incidence_early")
]
for early_name in early_names:
rootname = early_name[15:]
late_name = f"incidence_late{rootname}"
combined_name = f"incidence{rootname}"
def add_combined_incidence(model, time, e=early_name, l=late_name):
time_idx = model.times.index(time)
early_incidence = model.derived_outputs[e][time_idx]
late_incidence = model.derived_outputs[l][time_idx]
return early_incidence + late_incidence
tb_model.derived_output_functions[
combined_name] = add_combined_incidence
if "mortality" in derived_output_types:
# prepare death outputs for all strata
tb_model.death_output_categories = list_all_strata_for_mortality(
tb_model.compartment_names)
############################################
# population sizes for costing
############################################
if "popsizes" in derived_output_types:
# nb of detected individuals by strain:
def detected_popsize_function_builder(tag):
"""
example of tag: "starin_mdr" or "organ_smearpos"
"""
def calculate_nb_detected(model, time):
nb_treated = 0.0
for key, value in model.derived_outputs.items():
if "notifications" in key and tag in key:
this_time_index = model.times.index(time)
nb_treated += value[this_time_index]
return nb_treated
return calculate_nb_detected
for tag in [
"strain_mdr",
"strain_ds",
"organ_smearpos",
"organ_smearneg",
"organ_extrapul",
]:
tb_model.derived_output_functions[
"popsizeXnb_detectedX" +
tag] = detected_popsize_function_builder(tag)
# ACF popsize: number of people screened
def popsize_acf(model, time):
if external_params["acf_coverage"] == 0.0:
return 0.0
pop_urban_ger = sum([
model.compartment_values[i]
for i, c_name in enumerate(model.compartment_names)
if "location_urban_ger" in c_name
])
return external_params["acf_coverage"] * pop_urban_ger
tb_model.derived_output_functions[
"popsizeXnb_screened_acf"] = popsize_acf
return tb_model
def build_model(params: dict) -> StratifiedModel:
"""
Build the master function to run a tuberculosis model
"""
validate_params(params) # perform validation of parameter format
check_param_values(params) # perform validation of some parameter values
# Define model times.
time = params["time"]
integration_times = get_model_times_from_inputs(round(time["start"]),
time["end"], time["step"],
time["critical_ranges"])
# Define model compartments.
compartments = [
Compartment.SUSCEPTIBLE,
Compartment.EARLY_LATENT,
Compartment.LATE_LATENT,
Compartment.INFECTIOUS,
Compartment.ON_TREATMENT,
Compartment.RECOVERED,
]
infectious_comps = [
Compartment.INFECTIOUS,
Compartment.ON_TREATMENT,
]
# Define initial conditions - 1 infectious person.
init_conditions = {
Compartment.INFECTIOUS: 1,
}
# prepare infectiousness adjustment for individuals on treatment
treatment_infectiousness_adjustment = [{
"comp_name":
Compartment.ON_TREATMENT,
"comp_strata": {},
"value":
params["on_treatment_infect_multiplier"],
}]
# Define inter-compartmental flows.
flows = deepcopy(preprocess.flows.DEFAULT_FLOWS)
# is ACF implemented?
implement_acf = len(params["time_variant_acf"]) > 0
if implement_acf:
flows.append(preprocess.flows.ACF_FLOW)
# is ltbi screening implemented?
implement_ltbi_screening = len(params["time_variant_ltbi_screening"]) > 0
if implement_ltbi_screening:
flows += preprocess.flows.get_preventive_treatment_flows(
params["pt_destination_compartment"])
# Set some parameter values or parameters that require pre-processing
(
params,
treatment_recovery_func,
treatment_death_func,
relapse_func,
detection_rate_func,
acf_detection_rate_func,
preventive_treatment_func,
contact_rate_functions,
) = preprocess.flows.process_unstratified_parameter_values(
params, implement_acf, implement_ltbi_screening)
# Create the model.
tb_model = StratifiedModel(
times=integration_times,
compartment_names=compartments,
initial_conditions=init_conditions,
parameters=params,
requested_flows=flows,
infectious_compartments=infectious_comps,
birth_approach=BirthApproach.ADD_CRUDE,
entry_compartment=Compartment.SUSCEPTIBLE,
starting_population=int(params["start_population_size"]),
)
# register acf_detection_func
if acf_detection_rate_func is not None:
tb_model.time_variants["acf_detection_rate"] = acf_detection_rate_func
# register preventive_treatment_func
if preventive_treatment_func is not None:
tb_model.time_variants[
"preventive_treatment_rate"] = preventive_treatment_func
# register time-variant contact-rate functions:
for param_name, func in contact_rate_functions.items():
tb_model.time_variants[param_name] = func
# Apply infectiousness adjustment for individuals on treatment
tb_model.individual_infectiousness_adjustments = treatment_infectiousness_adjustment
# apply age stratification
if "age" in params["stratify_by"]:
stratify_by_age(tb_model, params, compartments)
else:
# set time-variant functions for treatment death and relapse rates
tb_model.time_variants[
"treatment_recovery_rate"] = treatment_recovery_func
tb_model.time_variants["treatment_death_rate"] = treatment_death_func
tb_model.time_variants["relapse_rate"] = relapse_func
# Load time-variant birth rates
birth_rates, years = inputs.get_crude_birth_rate(params["iso3"])
birth_rates = [b / 1000.0 for b in birth_rates
] # birth rates are provided / 1000 population
tb_model.time_variants["crude_birth_rate"] = scale_up_function(
years, birth_rates, smoothness=0.2, method=5)
tb_model.parameters["crude_birth_rate"] = "crude_birth_rate"
# apply user-defined stratifications
user_defined_stratifications = [
s for s in list(params["user_defined_stratifications"].keys())
if s in params["stratify_by"]
]
for stratification in user_defined_stratifications:
assert "_" not in stratification, "Stratification name should not include '_'"
stratification_details = params["user_defined_stratifications"][
stratification]
apply_user_defined_stratification(
tb_model,
compartments,
stratification,
stratification_details,
implement_acf,
implement_ltbi_screening,
)
if "organ" in params["stratify_by"]:
stratify_by_organ(tb_model, params)
else:
tb_model.time_variants["detection_rate"] = detection_rate_func
# Register derived output functions, which are calculations based on the model's compartment values or flows.
# These are calculated after the model is run.
outputs.get_all_derived_output_functions(params["calculated_outputs"],
params["outputs_stratification"],
tb_model)
return tb_model