def fn(self, cb):
with open(self.reffname) as fin :
cov = json.loads(fin.read())
for hscov in cov['hscov']:
ad_cov = hscov['coverage']
kid_cov = min([1, hscov['coverage']*1.5])
sev_cov = 0.8
for start_month in range(len(self.scale_by_month)) :
start_day = self.start+np.cumsum(self.days_in_month)[start_month]
duration = self.days_in_month[start_month+1]
scale = self.scale_by_month[start_month]
targets = [
{'trigger': 'NewClinicalCase', 'coverage': 1, 'agemin': 15, 'agemax': 200,
'seek': min([1, ad_cov*scale]), 'rate': 0.3},
{'trigger': 'NewClinicalCase', 'coverage': 1, 'agemin': 0, 'agemax': 15,
'seek': min([1, kid_cov*scale]), 'rate': 0.3},
{'trigger': 'NewSevereCase', 'coverage': 1,
'seek': min([1, max([sev_cov*scale, kid_cov*scale])]), 'rate': 0.5}]
add_health_seeking(cb, start_day=start_day, targets=targets,
duration=duration, repetitions=-1,
drug_ineligibility_duration=14,
nodes={'Node_List': hscov['nodes'], "class": "NodeSetNodeList"})
def seasonal_health_seeking(self, cb):
with open(self.fname) as fin:
cov = json.loads(fin.read())
for i, hscov in enumerate(cov[self.channel]):
code = 'group%d' % i
ad_cov = hscov['coverage']
kid_cov = min([1, hscov['coverage'] * 1.5])
sev_cov = 0.8
for start_month in range(len(self.scale_by_month)):
start_day = int(self.date + np.cumsum(self.days_in_month)[start_month])
duration = self.days_in_month[start_month + 1]
scale = self.scale_by_month[start_month]
targets = [
{'trigger': 'NewClinicalCase', 'coverage': 1, 'agemin': 15, 'agemax': 200,
'seek': min([1, ad_cov * scale]), 'rate': 0.3},
{'trigger': 'NewClinicalCase', 'coverage': 1, 'agemin': 0, 'agemax': 15,
'seek': min([1, kid_cov * scale]), 'rate': 0.3},
{'trigger': 'NewSevereCase', 'coverage': 1,
'seek': min([1, max([sev_cov * scale, kid_cov * scale])]), 'rate': 0.5}]
add_health_seeking(cb, start_day=start_day, targets=targets,
duration=duration, repetitions=self.duration_years + 1,
drug_ineligibility_duration=14,
node_property_restrictions=[{self.prop_name: code}])
def fn(self, cb):
with open(self.reffname) as fin:
cov = json.loads(fin.read())
for hscov in cov['hscov']:
targets = [{
'trigger': 'NewClinicalCase',
'coverage': 1,
'agemin': 15,
'agemax': 200,
'seek': hscov['coverage'],
'rate': 0.3
}, {
'trigger': 'NewClinicalCase',
'coverage': 1,
'agemin': 0,
'agemax': 15,
'seek': min([1, hscov['coverage'] * 1.5]),
'rate': 0.3
}, {
'trigger': 'NewSevereCase',
'coverage': 1,
'seek': 0.8,
'rate': 0.5
}]
add_health_seeking(cb,
start_day=self.start,
targets=targets,
nodes={
'Node_List': hscov['nodes'],
"class": "NodeSetNodeList"
})
def fn(self, cb):
add_health_seeking(
cb,
start_day=self.start,
drug=self.drug,
targets=self.targets,
nodes=self.nodes,
node_property_restrictions=self.node_property_restrictions)
cb.update_params({'PKPD_Model': 'CONCENTRATION_VERSUS_TIME'})
def add_healthseeking(self):
# Implement basic health-seeking behavior for all individuals in simulation
# Event information files
healthseek_events = pd.read_csv(self.healthseek_filename)
healthseek_events['simday'] = [convert_to_day(x, self.sim_start_date, "%Y-%m-%d") for x in
healthseek_events.fulldate]
healthseek_events = healthseek_events[np.in1d(healthseek_events['grid_cell'], self.demo_cells)]
healthseek_events.reset_index(inplace=True, drop=True)
[binned_and_grouped, grouped_by_fields] = self.try_campaign_compression(healthseek_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:
node_dict = {"class": "NodeSetAll"}
else:
node_dict = {"class": "NodeSetNodeList", "Node_List": node_list}
add_health_seeking(self.cb,
start_day=float(table['simday']),
targets=[{'trigger': 'NewClinicalCase',
'coverage': float(table['cov_newclin_youth']),
'agemin': 0,
'agemax': 5,
'seek': 1,
'rate': 0.3},
{'trigger': 'NewClinicalCase',
'coverage': float(table['cov_newclin_adult']),
'agemin': 5,
'agemax': 100,
'seek': 1,
'rate': 0.3},
{'trigger': 'NewSevereCase',
'coverage': float(table['cov_severe_youth']),
'agemin': 0,
'agemax': 5,
'seek': 1, 'rate': 0.5},
{'trigger': 'NewSevereCase',
'coverage': float(table['cov_severe_adult']),
'agemin': 5,
'agemax': 100,
'seek': 1,
'rate': 0.5}],
drug=['Artemether', 'Lumefantrine'],
dosing='FullTreatmentNewDetectionTech',
nodes=node_dict,
duration=float(table['duration']))
def add_hs(cb, events_df):
hs_field_list = ["cov_newclin_youth", "cov_newclin_adult", "cov_severe_youth", "cov_severe_adult", "duration"]
binned_intervene_events, binned_and_grouped, data_fields = try_campaign_compression(events_df, hs_field_list)
for table, group in binned_and_grouped:
table_dict = dict(zip((data_fields), table))
node_list = sorted(group['grid_cell'])
node_dict = {"class": "NodeSetNodeList", "Node_List": node_list}
add_health_seeking(cb,
start_day=float(table_dict['simday']),
targets=[{'trigger': 'NewClinicalCase',
'coverage': float(table_dict['cov_newclin_youth']),
'agemin': 0,
'agemax': 5,
'seek': 1,
'rate': 0.3},
{'trigger': 'NewClinicalCase',
'coverage': float(table_dict['cov_newclin_adult']),
'agemin': 5,
'agemax': 100,
'seek': 1,
'rate': 0.3},
{'trigger': 'NewSevereCase',
'coverage': float(table_dict['cov_severe_youth']),
'agemin': 0,
'agemax': 5,
'seek': 1,
'rate': 0.5},
{'trigger': 'NewSevereCase',
'coverage': float(table_dict['cov_severe_adult']),
'agemin': 5,
'agemax': 100,
'seek': 1,
'rate': 0.5}],
drug=['Artemether', 'Lumefantrine'],
dosing='FullTreatmentNewDetectionTech',
nodes=node_dict,
duration=float(table_dict['duration']))
def add_hs(self, cb, filename=None):
if not filename:
filename = self.dropbox_base + "inputs/grid_csv/grid_all_healthseek_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
]
hs_field_list = [
"cov_newclin_youth", "cov_newclin_adult", "cov_severe_youth",
"cov_severe_adult", "duration"
]
binned_intervene_events, binned_and_grouped, data_fields = self.try_campaign_compression_v2(
intervene_events, hs_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):
node_dict = {"class": "NodeSetAll"}
else:
node_dict = {
"class": "NodeSetNodeList",
"Node_List": node_list
}
add_health_seeking(cb,
start_day=float(table_dict['simday']),
targets=[{
'trigger':
'NewClinicalCase',
'coverage':
float(table_dict['cov_newclin_youth']),
'agemin':
0,
'agemax':
5,
'seek':
1,
'rate':
0.3
}, {
'trigger':
'NewClinicalCase',
'coverage':
float(table_dict['cov_newclin_adult']),
'agemin':
5,
'agemax':
100,
'seek':
1,
'rate':
0.3
}, {
'trigger':
'NewSevereCase',
'coverage':
float(table_dict['cov_severe_youth']),
'agemin':
0,
'agemax':
5,
'seek':
1,
'rate':
0.5
}, {
'trigger':
'NewSevereCase',
'coverage':
float(table_dict['cov_severe_adult']),
'agemin':
5,
'agemax':
100,
'seek':
1,
'rate':
0.5
}],
drug=['Artemether', 'Lumefantrine'],
dosing='FullTreatmentNewDetectionTech',
nodes=node_dict,
duration=float(table_dict['duration']))
#add_summary_report(cb)
#add_patient_report(cb)
# add_health_seeking(cb, start_day=0, drug=['Lumefantrine', 'Artemether'],
# targets=[{'trigger': 'NewClinicalCase', 'coverage': .33, 'agemin': 0, 'agemax': 5, 'seek': 1,
# 'rate': 0.3}, {'trigger': 'NewClinicalCase', 'coverage': .33, 'agemin': 5, 'agemax': 200, 'seek': 1,
# 'rate': 0.3}])
add_health_seeking(cb,
start_day=0,
drug=['Lumefantrine', 'Artemether'],
targets=[{
'trigger': 'NewClinicalCase',
'coverage': .3,
'agemin': 0,
'agemax': 5,
'seek': 1.,
'rate': 0.
}, {
'trigger': 'NewClinicalCase',
'coverage': 1.,
'agemin': 5,
'agemax': 200,
'seek': 1.,
'rate': 0.
}])
#add_drug_campaign(cb, "SMC", "SPA", start_days=[230 + 365*x for x in range(5)], repetitions=4, interval=30, coverage=1., target_group={'agemin' : 0, 'agemax' : 5})
add_summary_report(cb,
interval=365 / 12.,
description='Monthly',
age_bins=[5, 15, 200])
# Add case management --------------------------------------------------------------------------------------------
add_health_seeking(cb,
start_day=0,
drug=['Artemether', 'Lumefantrine'],
nodes={
"class": "NodeSetNodeList",
"Node_List": village_nodes
},
targets=[{
'trigger': 'NewClinicalCase',
'coverage': 0.4,
'agemin': 0,
'agemax': 5,
'seek': 1,
'rate': 0.3
}, {
'trigger': 'NewClinicalCase',
'coverage': 0.2,
'agemin': 5,
'agemax': 100,
'seek': 1,
'rate': 0.3
}, {
'trigger': 'NewSevereCase',
'coverage': 0.5,
'agemin': 0,
'agemax': 100,
'seek': 1,
'rate': 0.3
}])
def perfect_health_seeking(cb):
add_health_seeking(cb, start_day=survey_day,
targets=[dict(trigger='NewClinicalCase', coverage=1.0, seek=1.0, rate=0.3)])
return cb.set_param('Config_Name', 'perfect_health_seeking')
#[ModFn(heg_release, num_released, d) for (num_released, d) in release_combs],
[ModFn(set_habs_scale, habs)
for habs in habitat_scales], # set habitat scales
[ModFn(DTKConfigBuilder.set_param, 'Run_Number', r) for r in range(1)])
cb = DTKConfigBuilder.from_defaults('MALARIA_SIM')
for year in range(0, nyears, 3):
add_ITN_age_season(cb,
start=(365 * (baseline_year + year)) + 1,
coverage_all=0.95,
seasonal_dep={
'times': [0, 365],
'values': [0.65, 0.65]
})
'''
for year in range(nyears):
add_IRS(cb, start=(365 * (baseline_year + year)) + 1,
coverage_by_ages=[{'min': 0, 'max': 100, 'coverage': 0.95}])
add_health_seeking(cb, start_day=(365 * baseline_year) + 1, targets=[{'trigger': 'NewClinicalCase', 'coverage': 0.95, 'agemin': 0, 'agemax': 100, 'seek': 1, 'rate': 0.3}])
'''
cb.update_params({
'Num_Cores': 1,
'Simulation_Duration': (nyears + baseline_year) * 365,
# 'Simulation_Duration' : 500,
'Enable_Demographics_Reporting': 1,
'New_Diagnostic_Sensitivity': 0.05, # 20/uL
#'Listed_Events': ['Bednet_Using', 'Bednet_Got_New_One', 'Bednet_Discarded', 'Received_Treatment', 'Received_IRS']
})
