def add_scenario_specific_smc(cb, age_range="default"):
# Both years get 2 years of 90% coverage SMC
for year in [0, 1]:
dtk_start_days = year * 365 + smc_days_in_year
if age_range in ["default", "u10", "u15"]:
add_smc(cb,
u5_coverage=0.9,
start_days=dtk_start_days,
age_range=age_range)
elif age_range == "u10_with_DP":
add_drug_campaign(cb,
campaign_type="MDA",
drug_code="DP",
start_days=list(dtk_start_days),
coverage=0.9,
target_group={
'agemin': 0.25,
'agemax': 10
},
receiving_drugs_event_name="Received_SMC")
elif age_range == "u15_with_DP":
add_drug_campaign(cb,
campaign_type="MDA",
drug_code="DP",
start_days=list(dtk_start_days),
coverage=0.9,
target_group={
'agemin': 0.25,
'agemax': 15
},
receiving_drugs_event_name="Received_SMC")
def add_rcd(self):
rcd_events = pd.read_csv(self.rcd_filename)
rcd_events['simday'] = [convert_to_day(x, self.sim_start_date, "%Y-%m-%d") for x in rcd_events.fulldate]
rcd_events = rcd_events[np.in1d(rcd_events['grid_cell'], self.demo_cells)]
rcd_events.reset_index(inplace=True, drop=True)
rcd_events = rcd_events[['grid_cell','event','fulldate','simday','coverage','trigger_coverage','interval']]
[binned_and_grouped, grouped_by_fields] = self.try_campaign_compression(rcd_events)
for tup, group in binned_and_grouped:
table = self.convert_group_tuple_to_dict(tup, grouped_by_fields)
node_list = list(group['grid_cell'])
if sorted(node_list) == self.demo_cells:
nodeIDs = []
else:
nodeIDs = node_list
coverage = float(table['coverage'])
if coverage > 0:
add_drug_campaign(self.cb,
campaign_type='rfMSAT',
drug_code='AL',
start_days=[float(table['simday'])],
coverage=float(table['coverage']),
trigger_coverage=float(table['trigger_coverage']),
interval=float(table['interval']),
nodes=nodeIDs)
def add_smc_group(cb,
coverage=1.0,
start_days=[60],
agemax=10,
drug='DP',
target_property_name='ReceivedIntervention',
target_property_value='NoDrugs'):
add_drug_campaign(cb,
'SMC',
drug,
start_days=start_days,
repetitions=4,
tsteps_btwn_repetitions=30,
coverage=coverage,
target_group={
'agemin': 0,
'agemax': agemax
})
# ind_property_restrictions=[{target_property_name: target_property_value}])
return {
'Coverage': coverage,
'Start': start_days[0],
'Intervention_type': 'SMC%i' % agemax
}
def add_ipt_for_timing_sweep(cb, scenario_number, archetype, num_years=1):
if archetype == "Southern":
timings_df = pd.read_csv("southern_term_sweep_scenarios.csv")
elif archetype == "Sahel":
timings_df = pd.read_csv("sahel_term_sweep_scenarios.csv")
elif archetype == "Central":
timings_df = pd.read_csv("central_term_sweep_scenarios.csv")
else:
raise NotImplementedError
scenario_dict = dict(
timings_df[timings_df["scenario_number"] ==
scenario_number].reset_index(drop=True).iloc[0])
for y in range(num_years):
campaign_days = np.array([
scenario_dict["term1_day"] + y * 365,
scenario_dict["term2_day"] + y * 365,
scenario_dict["term3_day"] + y * 365
])
add_drug_campaign(cb,
campaign_type="MDA",
drug_code="DP",
start_days=list(campaign_days),
coverage=1,
ind_property_restrictions=[{
"SchoolStatus":
"AttendsSchool"
}])
return {"scenario_number": scenario_number}
def add_mda(self):
mda_events = pd.read_csv(self.mda_filename)
mda_events['simday'] = [convert_to_day(x, self.sim_start_date, "%Y-%m-%d") for x in mda_events.fulldate]
mda_events = mda_events[np.in1d(mda_events['grid_cell'], self.demo_cells)]
mda_events.reset_index(inplace=True, drop=True)
mda_events = mda_events[['grid_cell','event','fulldate','simday','cov_all']]
[binned_and_grouped, grouped_by_fields] = self.try_campaign_compression(mda_events)
for tup, group in binned_and_grouped:
table = self.convert_group_tuple_to_dict(tup, grouped_by_fields)
node_list = list(group['grid_cell'])
if sorted(node_list) == self.demo_cells:
nodeIDs = []
else:
nodeIDs = node_list
cov_all = float(table['cov_all'])
if cov_all > 0:
add_drug_campaign(self.cb,
campaign_type='MDA',
drug_code='DP',
start_days=[float(table['simday'])],
coverage=float(table['cov_all']),
repetitions=1,
interval=60,
nodes=nodeIDs)
def add_smc_group(cb,
coverage=1.0,
start_days=[60, 60 + 365],
agemax=10,
drug='DP'):
'''
This function sets up an SMC campaign for children under a certain age. Drug parameters can be changed to include or exclude gametocyte killing.
:param cb: config builder
:param coverage: coverage for SMC
:param start_days: campaign start day
:param agemax: children under this age will receive SMC drugs
:param drug: type of drug to be used in SMC campaign
:return: tags for SMC campaign
'''
add_drug_campaign(cb,
'SMC',
drug,
start_days=start_days,
repetitions=3,
interval=30,
coverage=coverage,
target_group={
'agemin': 0,
'agemax': agemax
})
return {
'Coverage': coverage,
'Start': start_days[0],
'Intervention_type': 'SMC%i' % agemax
}
def add_primaquine(cb):
add_drug_campaign(cb,
'MDA',
drug_code="PMQ",
start_days=[1],
trigger_condition_list=[
'Received_Campaign_Drugs', 'Received_RCD_Drugs'
],
receiving_drugs_event_name='Received_Primaquine')
def rcd_followthrough(cb, coverage=1, delivery_method="MTAT"):
# Listen for Diagnostic_Survey_0 and implement a diagnostic survey, then broadcast TestedPositive or TestedNegative.
# Then, if TestedPositive, then administer drugs and broadcast Received_RCD_Drugs
# Drug setup
drug_code = 'AL'
drug_configs = drug_configs_from_code(cb, drug_code=drug_code)
# set up events to broadcast when receiving reactive campaign drug
receiving_drugs_event = {
"class": "BroadcastEvent",
"Broadcast_Event": "Received_RCD_Drugs"
}
event_config = drug_configs + [receiving_drugs_event]
if delivery_method == "MTAT":
add_diagnostic_survey(cb,
coverage=coverage,
start_day=1,
diagnostic_type='BLOOD_SMEAR_PARASITES',
diagnostic_threshold=0,
trigger_condition_list=['Diagnostic_Survey_0'],
event_name='Reactive MSAT level 0',
positive_diagnosis_configs=event_config,
listening_duration=-1)
elif delivery_method == "MDA":
add_drug_campaign(cb,
coverage=coverage,
drug_code=drug_code,
start_days=[1],
campaign_type="MDA",
trigger_condition_list=["Diagnostic_Survey_0"],
listening_duration=-1
)
# def add_drug_campaign(cb, campaign_type: str = 'MDA', drug_code: str = None, start_days: list = None,
# coverage: float = 1.0,
# repetitions: int = 1, tsteps_btwn_repetitions: int = 60,
# diagnostic_type: str = 'BLOOD_SMEAR',
# diagnostic_threshold: float = 40, fmda_radius: int = 0,
# node_selection_type: str = 'DISTANCE_ONLY',
# trigger_coverage: float = 1.0, snowballs: int = 0, treatment_delay: int = 0,
# triggered_campaign_delay: int = 0, nodeIDs: list = None, target_group: any = 'Everyone',
# drug_ineligibility_duration: int = 0,
# node_property_restrictions: list = None, ind_property_restrictions: list = None,
# disqualifying_properties: list = None, trigger_condition_list: list = None,
# listening_duration: int = -1, adherent_drug_configs: list = None,
# target_residents_only: int = 1,
# check_eligibility_at_trigger: bool = False):
return {"delivery_method": delivery_method,
"coverage": coverage}
def add_mda(cb, events_df):
mda_field_list = ["cov_all"]
binned_intervene_events, binned_and_grouped, data_fields = try_campaign_compression(events_df, mda_field_list)
for table, group in binned_and_grouped:
table_dict = dict(zip((data_fields), table))
node_list = sorted(group['grid_cell'])
add_drug_campaign(cb,
campaign_type='MDA',
drug_code='DP',
start_days=[float(table_dict['simday'])],
coverage=table_dict['cov_all'],
repetitions=1,
interval=60,
nodes=node_list)
def add_smc(cb, u5_coverage, start_days, age_range="default"):
# Copied from HBHI setup
default_adherence = 0.8
default_sp_resist_day1_multiply = 0.87
adherent_drug_configs = smc_adherent_configuration(
cb=cb,
adherence=default_adherence,
sp_resist_day1_multiply=default_sp_resist_day1_multiply)
if age_range == "default":
add_drug_campaign(cb,
'SMC',
start_days=start_days,
coverage=u5_coverage,
target_group={
'agemin': 0.25,
'agemax': 5
},
adherent_drug_configs=[adherent_drug_configs],
receiving_drugs_event_name='Received_SMC')
o5_coverage = (0.2 / 0.9) * u5_coverage
add_drug_campaign(cb,
'SMC',
start_days=start_days,
coverage=o5_coverage,
target_group={
'agemin': 5,
'agemax': 10
},
adherent_drug_configs=[adherent_drug_configs],
receiving_drugs_event_name='Received_SMC')
elif age_range == "u10":
add_drug_campaign(cb,
'SMC',
start_days=start_days,
coverage=u5_coverage,
target_group={
'agemin': 0.25,
'agemax': 10
},
adherent_drug_configs=[adherent_drug_configs],
receiving_drugs_event_name='Received_SMC')
elif age_range == "u15":
add_drug_campaign(cb,
'SMC',
start_days=start_days,
coverage=u5_coverage,
target_group={
'agemin': 0.25,
'agemax': 15
},
adherent_drug_configs=[adherent_drug_configs],
receiving_drugs_event_name='Received_SMC')
def add_smc_group(cb, coverage=1.0, start_days=[60, 60+365], agemax=10, drug='DP', dp_gam_kill_on=1):
add_drug_campaign(cb, 'SMC', drug, start_days=start_days, repetitions=3, interval=30,
coverage=coverage,
target_group={'agemin': 0, 'agemax': agemax})
if not dp_gam_kill_on:
malaria_drug_params = update_drugs(['DHA', 'Drug_Gametocyte02_Killrate'], 0.0)
malaria_drug_params = update_drugs(['DHA', 'Drug_Gametocyte34_Killrate'], 0.0,
malaria_drug_params=malaria_drug_params)
malaria_drug_params = update_drugs(['DHA', 'Drug_GametocyteM_Killrate'], 0.0,
malaria_drug_params=malaria_drug_params)
malaria_drug_params = update_drugs(['Piperaquine', 'Drug_Gametocyte02_Killrate'], 0.0,
malaria_drug_params=malaria_drug_params)
cb.update_params({"Malaria_Drug_Params": malaria_drug_params})
return {'Coverage': coverage, 'Start': start_days[0], 'Intervention_type': 'SMC_GK%i_' %dp_gam_kill_on+str(agemax)+drug}
def __call__(self, cb):
from malaria.interventions.malaria_drug_campaigns import add_drug_campaign
return add_drug_campaign(cb, self.campaign_type, self.drug_code, start_days=self.start_days,
coverage=self.coverage, repetitions=self.repetitions, interval=self.interval,
diagnostic_threshold=self.diagnostic_threshold,
diagnostic_type=self.diagnostic_type,
drug_ineligibility_duration=self.drug_ineligibility_duration,
snowballs=self.snowballs, treatment_delay=self.delay, nodes=self.nodes,
target_group=self.target_group, node_property_restrictions=self.NP_restrictions)
def add_rcd(self, cb, filename=None):
if not filename:
# filename = self.dropbox_base + "inputs/grid_csv/grid_all_react_events.csv"
filename = self.dropbox_base + "inputs/grid_csv/grid_all_react_events_SIMPLE.csv"
print(
"WARNING: using simplified RCD file-- grid_all_react_events_SIMPLE.csv"
)
intervene_events = pd.read_csv(filename)
# Restrict to catchment of interest
intervene_events = intervene_events[np.in1d(
intervene_events['grid_cell'], self.catch_cells)]
intervene_events.reset_index(inplace=True)
# Compute simulation days relative to start date or use default in file
intervene_events['simday'] = [
convert_to_day_365(x, self.start_date, "%Y-%m-%d")
for x in intervene_events.fulldate
]
rcd_field_list = ["coverage", "trigger_coverage", "interval"]
binned_intervene_events, binned_and_grouped, data_fields = self.try_campaign_compression_v2(
intervene_events, rcd_field_list)
for table, group in binned_and_grouped:
table_dict = dict(zip((data_fields), table))
node_list = sorted(group['grid_cell'])
if node_list == sorted(self.catch_cells):
nodeIDs = []
else:
nodeIDs = node_list
for i in range(len(intervene_events)):
add_drug_campaign(cb,
campaign_type='rfMSAT',
drug_code='AL',
diagnostic_type='BLOOD_SMEAR_PARASITES',
diagnostic_threshold=0,
start_days=[float(table_dict['simday'])],
coverage=float(table_dict['coverage']),
trigger_coverage=float(
table_dict['trigger_coverage']),
interval=float(table_dict['interval']),
nodes=nodeIDs)
def add_rcd(cb, events_df):
rcd_field_list = ["coverage", "trigger_coverage", "interval"]
binned_intervene_events, binned_and_grouped, data_fields = try_campaign_compression(
events_df, rcd_field_list)
for table, group in binned_and_grouped:
table_dict = dict(zip((data_fields), table))
node_list = sorted(group['grid_cell'])
add_drug_campaign(cb,
campaign_type='rfMSAT',
drug_code='AL',
diagnostic_type='BLOOD_SMEAR_PARASITES',
diagnostic_threshold=0,
start_days=[float(table_dict['simday'])],
coverage=float(table_dict['coverage']),
trigger_coverage=float(
table_dict['trigger_coverage']),
nodeIDs=node_list)
def add_scenario_specific_ipt(
cb,
scenario_dict,
archetype,
receiving_drugs_event_name="Received_Campaign_Drugs"):
# scenario dict has drug_type,screen_type,interval,school_coverage
if scenario_dict["screen_type"] == "IPT":
dtk_campaign_type = "MDA"
elif scenario_dict["screen_type"] == "IST":
dtk_campaign_type = "MSAT"
else:
raise NotImplementedError
drug_code = scenario_dict["drug_type"]
if drug_code == "SPAQ":
drug_code = "SPA"
if drug_code == "ASAQ":
drug_code = "ASA"
# timing:
timings_df = pd.read_csv("ipt_schedule.csv")
timings_df = timings_df[np.logical_and(
timings_df["archetype"] == archetype,
timings_df["interval"] == scenario_dict["interval"])].reset_index(
drop=True)
campaign_days = np.array(timings_df["day"])
# Assuming that we do the same thing for 2 years:
campaign_days = np.append(campaign_days, campaign_days + 365)
add_drug_campaign(cb,
campaign_type=dtk_campaign_type,
drug_code=drug_code,
start_days=list(campaign_days),
coverage=scenario_dict["within_school_coverage"],
ind_property_restrictions=[{
"SchoolStatus":
"AttendsSchool"
}],
diagnostic_type='BLOOD_SMEAR_PARASITES',
diagnostic_threshold=0,
receiving_drugs_event_name=receiving_drugs_event_name)
def sample_point_fn(cb, sample_dimension_values):
# require multinode to read in burn-in
cb.update_params({'Simulation_Duration': sample_dimension_values['Serialization'],
'Spatial_Output_Channels': ['New_Diagnostic_Prevalence', 'Population', 'Prevalence'],
'Serialization_Time_Steps': [sample_dimension_values['Serialization']],
'New_Diagnostic_Sensitivity': 50
})
# also need to pick up serialization path to load serialized file for each iteration.
if sample_dimension_values['NodeIDs'] :
# adjust the start date of VMW campaigns
for event in cb.campaign['Events']:
if event['Start_Day'] < sample_dimension_values['Serialization']:
event['Start_Day'] = sample_dimension_values['Serialization']
add_drug_campaign(cb, 'MDA', 'DP', [sample_dimension_values['Serialization']],
coverage=0.5, nodes=sample_dimension_values['NodeIDs'], interval=30)
# for the second round, we want to set the start time equal to the last day of the old sim.
# We also want duration to be equal to the new duration value, and for it to serialize at the
# proper point.
cb.update_params({'Start_Time': sample_dimension_values['Serialization'],
'Simulation_Duration': sample_dimension_values['New_Duration'],
'Serialization_Time_Steps': [sample_dimension_values['Serialization']+
sample_dimension_values['New_Duration']]})
if 'Serialized_Population_Path' in sample_dimension_values:
cb.set_param('Serialized_Population_Path',sample_dimension_values['Serialized_Population_Path'])
if 'Serialized_Population_Filenames' in sample_dimension_values:
cb.set_param('Serialized_Population_Filenames',sample_dimension_values['Serialized_Population_Filenames'])
tags = {'Prevalence_date':sample_dimension_values['Prevalence_date'],
'Prevalence_threshold': sample_dimension_values['Prevalence_threshold'],
'Serialization': sample_dimension_values['New_Duration'] if sample_dimension_values['NodeIDs'] else
sample_dimension_values['Serialization']}
tags.update(cb.set_param('Run_Number', sample_dimension_values['Run_Number']))
return tags
def add_long_term_ipt(cb, ipt_on):
if ipt_on:
term_days = [15, 165, 254]
for y in range(num_years):
for t in [1, 2, 3]:
dtk_day = y * 365 + term_days[t - 1]
add_drug_campaign(
cb,
campaign_type="MDA",
drug_code="DP",
start_days=[dtk_day],
coverage=0.9,
ind_property_restrictions=[{
"SchoolStatus": "AttendsSchool"
}],
receiving_drugs_event_name="Received_Campaign_Drugs")
else:
pass
return {"ipt_on": ipt_on}
def add_smc_variant(cb, scenario_num):
'''
:param cb:
:param scenario_num:
0 = no intervention.
1 = regular SMC.
2 = SMC + iver.
3 = SMC + prim
4 = SMC + iver + prim
:return:
'''
if scenario_num == 0:
pass
else:
add_scenario_specific_smc(cb, age_range="default")
if scenario_num == 2 or scenario_num == 4:
add_ivermectin(
cb,
box_duration="WEEK",
start_days=[
1
], #Listening for drug delivery in other methods, then give this drug
trigger_condition_list=['Received_SMC'])
if scenario_num == 3 or scenario_num == 4:
add_drug_campaign(cb,
'MDA',
drug_code="PMQ",
start_days=[1],
trigger_condition_list=['Received_SMC'],
receiving_drugs_event_name='Received_Primaquine')
return {
"scenario_num": scenario_num,
"scenario_description": scenario_descriptions[scenario_num]
}
def add_msat(self, cb, filename=None):
if not filename:
filename = self.dropbox_base + "inputs/grid_csv/grid_all_msat_events.csv"
intervene_events = pd.read_csv(filename)
# Restrict to catchment of interest
intervene_events = intervene_events[np.in1d(
intervene_events['grid_cell'], self.catch_cells)]
intervene_events.reset_index(inplace=True)
# Compute simulation days relative to start date or use default in file
intervene_events['simday'] = [
convert_to_day_365(x, self.start_date, "%Y-%m-%d")
for x in intervene_events.fulldate
]
msat_field_list = ["cov_all"]
binned_intervene_events, binned_and_grouped, data_fields = self.try_campaign_compression_v2(
intervene_events, msat_field_list)
for table, group in binned_and_grouped:
table_dict = dict(zip((data_fields), table))
node_list = sorted(group['grid_cell'])
if node_list == sorted(self.catch_cells):
nodeIDs = []
else:
nodeIDs = node_list
add_drug_campaign(cb,
campaign_type='MSAT',
drug_code='AL',
diagnostic_type='BLOOD_SMEAR_PARASITES',
diagnostic_threshold=0,
start_days=[float(table_dict['simday'])],
coverage=table_dict['cov_all'],
repetitions=1,
interval=60,
nodes=nodeIDs)
def add_ipt_with_iver_skipping_one_term(cb, term_to_skip):
term_days = [15, 165, 254]
for y in [0, 1]:
for t in [1, 2, 3]:
dtk_day = y * 365 + term_days[t - 1]
add_drug_campaign(
cb,
campaign_type="MDA",
drug_code="DP",
start_days=[dtk_day],
coverage=scenario_dict["within_school_coverage"],
ind_property_restrictions=[{
"SchoolStatus": "AttendsSchool"
}],
diagnostic_type='BLOOD_SMEAR_PARASITES',
diagnostic_threshold=0,
receiving_drugs_event_name="Received_Campaign_Drugs_Term_{}".
format(t))
terms_with_iver = [1, 2, 3]
terms_with_iver.remove(term_to_skip)
iver_trigger_list = [
"Received_Campaign_Drugs_Term_{}".format(t) for t in terms_with_iver
]
add_ivermectin(
cb,
box_duration="WEEK",
start_days=[
1
], #Listening for drug delivery in other methods, then give this drug
trigger_condition_list=iver_trigger_list)
return {"term_skipped": term_to_skip}
def add_intervention(cb, intervention, coverage, target, ivm_duration) :
IPR = [{'HomeVillage' : 'Village1'}] if target == 'vill1' else None
if intervention == 'ivermectin_to_forest' :
add_ivermectin(cb, box_duration=ivm_duration, coverage=coverage, start_days=[365],
trigger_condition_list=['Immigrating'],
nodeIDs=forest_nodes,
target_group={'agemin': 5, 'agemax': 200, 'gender': 'Male'},
target_residents_only=False,
ind_property_restrictions=IPR
)
elif intervention == 'drug_to_forest' :
add_drug_campaign(cb, 'MDA', 'DP', coverage=coverage, start_days=[365],
repetitions=1,
trigger_condition_list=['Immigrating'],
nodeIDs=forest_nodes,
target_residents_only=False,
ind_property_restrictions=IPR
)
elif intervention == 'drug_MDA' :
add_drug_campaign(cb, 'MDA', 'DP', coverage=coverage, start_days=[365 + 152],
repetitions=3, tsteps_btwn_repetitions=30,
nodeIDs=village_nodes,
target_residents_only=False,
ind_property_restrictions=IPR
)
elif intervention == 'drug_ivermectin_MDA' :
add_drug_campaign(cb, 'MDA', 'DP', coverage=coverage, start_days=[365 + 152],
repetitions=3, tsteps_btwn_repetitions=30,
nodeIDs=village_nodes,
target_residents_only=False,
ind_property_restrictions=IPR
)
add_ivermectin(cb, box_duration=ivm_duration, coverage=1, start_days=[365],
trigger_condition_list=['Received_Campaign_Drugs'],
target_group={'agemin': 5, 'agemax': 200, 'gender': 'Male'},
nodeIDs=village_nodes,
target_residents_only=False
)
add_ivermectin(cb, box_duration=ivm_duration, coverage=1, start_days=[365],
trigger_condition_list=['Received_Campaign_Drugs'],
target_group={'agemin': 51, 'agemax': 200, 'gender': 'Female'},
nodeIDs=village_nodes,
target_residents_only=False
)
add_ivermectin(cb, box_duration=ivm_duration, coverage=1, start_days=[365],
trigger_condition_list=['Received_Campaign_Drugs'],
target_group={'agemin': 5, 'agemax': 12, 'gender': 'Female'},
nodeIDs=village_nodes,
target_residents_only=False
)
elif intervention == 'ivermectin_MDA' :
add_ivermectin(cb, box_duration=ivm_duration, coverage=coverage,
start_days=[365 + 152 + 30 * x for x in range(3)],
nodeIDs=village_nodes,
target_group={'agemin': 5, 'agemax': 200, 'gender': 'Male'},
target_residents_only=False,
ind_property_restrictions=IPR
)
add_ivermectin(cb, box_duration=ivm_duration, coverage=coverage,
start_days=[365 + 152 + 30 * x for x in range(3)],
nodeIDs=village_nodes,
target_group={'agemin': 51, 'agemax': 200, 'gender': 'Female'},
target_residents_only=False,
ind_property_restrictions=IPR
)
add_ivermectin(cb, box_duration=ivm_duration, coverage=coverage,
start_days=[365 + 152 + 30 * x for x in range(3)],
nodeIDs=village_nodes,
target_group={'agemin': 5, 'agemax': 12, 'gender': 'Female'},
target_residents_only=False,
ind_property_restrictions=IPR
)
forest_HS_coverage = coverage if intervention == 'forest_HS' else 0.4
add_health_seeking(cb, start_day=0,
drug=['Artemether', 'Lumefantrine'],
nodes=village_nodes,
targets=[
{'trigger': 'NewClinicalCase', 'coverage': forest_HS_coverage, 'seek': 1, 'rate': 0.2}],
ind_property_restrictions=[{'ForestGoing': 'LovesForest'}]
)
event_trigger_list=['Received_Treatment']
if 'ivermectin' in intervention :
event_trigger_list.append('Received_Ivermectin')
if 'drug' in intervention :
event_trigger_list.append('Received_Campaign_Drugs')
add_event_counter_report(cb, event_trigger_list=event_trigger_list)
return { 'intervention' : intervention,
'coverage' : coverage,
'target' : target,
'IVM duration' : ivm_duration}
