def add_regional_EIR_node(cb):
regional_EIR_node_label = 100000
monthly_profile = 0.5*np.array([12.1, 23.9, 33.5, 14.8, 6.8, 4.9, 3.3, 3.8, 3.5, 3.3, 2.8, 4.3])
add_InputEIR(cb,
monthlyEIRs=list(monthly_profile),
nodes={'class': 'NodeSetNodeList', 'Node_List': [regional_EIR_node_label]},
start_day=0)
def set_up_EIR_node(self, cb, EIR_scale_factor=6, EIR_start_day=0):
# From Jaline
cb.update_params({
'Enable_Regional_Migration': 1,
'Regional_Migration_Roundtrip_Duration': 3,
'Regional_Migration_Roundtrip_Probability': 1,
'x_Regional_Migration': 0.0405,
# 'Regional_Migration_Filename': 'Regional_Migration.bin',
})
# EIR profile copied from Jaline's household model
monthly_profile = [
0.21, 0.28, 0.43, 0.65, 0.84, 1.21, 1.24, 1.04, 0.84, 0.57, 0.29,
0.17
]
add_InputEIR(
cb,
monthlyEIRs=[x * EIR_scale_factor for x in monthly_profile],
nodes={
'class': 'NodeSetNodeList',
'Node_List': [100000]
},
start_day=EIR_start_day)
return {"EIR_scale_factor": EIR_scale_factor}
def sweep_scale_factor(cb, scale_factor):
monthly_EIR = [
43.8, 68.5, 27.4, 46.6, 49.4, 24.7, 13.7, 11, 11, 2.74, 13.7, 16.5
]
add_InputEIR(cb, [x * scale_factor for x in monthly_EIR])
return {'annual EIR': scale_factor * sum(monthly_EIR)}
def add_regional_EIR_node(self):
# From Jaline
self.cb.update_params({
'Enable_Regional_Migration':
1,
'Regional_Migration_Roundtrip_Duration':
3,
'Regional_Migration_Roundtrip_Probability':
1,
'x_Regional_Migration':
0.0405,
'Regional_Migration_Filename':
'Migration/_Regional_Migration.bin',
})
monthly_profile = [
12.1, 23.9, 33.5, 14.8, 6.8, 4.9, 3.3, 3.8, 3.5, 3.3, 2.8, 4.3
]
add_InputEIR(
self.cb,
monthlyEIRs=[x * self.EIR_scale_factor for x in monthly_profile],
nodes={
'class': 'NodeSetNodeList',
'Node_List': [self.EIR_node_label]
},
start_day=self.EIR_start_day)
add_filtered_report(self.cb,
nodes=[self.EIR_node_label],
description='Work')
def add_regional_EIR_node(cb):
regional_EIR_node_label = 100000
monthly_profile = 0.5 * np.array(
[12.1, 23.9, 33.5, 14.8, 6.8, 4.9, 3.3, 3.8, 3.5, 3.3, 2.8, 4.3])
add_InputEIR(cb,
monthlyEIRs=list(monthly_profile),
nodeIDs=[regional_EIR_node_label],
start_day=0)
def add_regional_EIR_node(self):
# From Jaline
self.cb.update_params({
'Enable_Regional_Migration': 1,
'Regional_Migration_Roundtrip_Duration': 3,
'Regional_Migration_Roundtrip_Probability': 1,
'x_Regional_Migration': 0.0405,
'Regional_Migration_Filename': 'regional_migration.bin',
})
monthly_profile = [12.1, 23.9, 33.5, 14.8, 6.8, 4.9, 3.3, 3.8, 3.5, 3.3, 2.8, 4.3]
add_InputEIR(self.cb,
monthlyEIRs=[x * self.regional_EIR_scale_factor for x in monthly_profile],
nodes={'class': 'NodeSetNodeList', 'Node_List': [self.regional_EIR_node_label]},
start_day=0)
# Add filtered reports to separate this node out
add_filtered_report(self.cb, nodes=[self.regional_EIR_node_label], description='Work')
all_catch_nodes = get_ids_from_demographics_file(os.path.join(self.input_files_root,self.demo_fp))
all_catch_nodes.remove(self.regional_EIR_node_label)
add_filtered_report(self.cb, nodes= all_catch_nodes, description='Catchment')
def configure_site_EIR(cb,
site,
habitat=1,
circular_shift=0,
birth_cohort=True,
set_site_geography=True,
**geo_kwargs):
if site not in study_site_monthly_EIRs.keys():
raise Exception("Don't know how to configure site: %s " % site)
EIRs = [habitat * m for m in study_site_monthly_EIRs[site]]
# Calibration is done with CONSTANT_INITIAL_IMMUNITY on birth cohort
# but with a downscaling to account for maternal immunity levels
# Here, we'll keep the CONSTANT model and downscale as a function of annual EIR
annual_EIR = sum(EIRs)
mAb = cb.get_param('Maternal_Antibody_Protection') * mAb_vs_EIR(annual_EIR)
if birth_cohort:
set_geography(cb, "Birth_Cohort")
elif set_site_geography:
geo = geography_from_site(site)
set_geography(cb, geo, **geo_kwargs)
cb.update_params({
'Config_Name': site,
'Vector_Species_Names': [], # no mosquitoes
'Maternal_Antibodies_Type': 'CONSTANT_INITIAL_IMMUNITY',
'Maternal_Antibody_Protection': mAb
})
# Shift order of months according to circular_shift argument
EIR_deque = deque(EIRs)
EIR_deque.rotate(circular_shift)
monthlyEIRs = list(EIR_deque)
add_InputEIR(cb, monthlyEIRs=monthlyEIRs)
return {'monthlyEIRs': monthlyEIRs}
def __call__(self, cb):
return add_InputEIR(cb, monthlyEIRs=self.monthlyEIRs, start_day=self.start_day, nodes=self.nodes)
"Serialized_Population_Path":
"//internal.idm.ctr/IDM/Home/jsuresh/input/kariba_gridded_sims",
'Serialized_Population_Filenames':
['{}_2010-000.dtk'.format(catch), '{}_2010-001.dtk'.format(catch)]
})
# # Drop EIR in regional node by a factor of 10 at beginning of 2014: this will overwrite previous EIR intervention
monthly_profile = [
0.21, 0.28, 0.43, 0.65, 0.84, 1.21, 1.24, 1.04, 0.84, 0.57, 0.29, 0.17
]
EIR_scale_factor = 0.1
EIR_start_day = 365 * (2014 - sim_start_year)
add_InputEIR(cb,
monthlyEIRs=[x * EIR_scale_factor for x in monthly_profile],
nodes={
'class': 'NodeSetNodeList',
'Node_List': [100000]
},
start_day=EIR_start_day)
if __name__ == "__main__":
SetupParser.init()
SetupParser.set("HPC", "priority", priority)
SetupParser.set("HPC", "node_group", coreset)
modlists = []
# if num_seeds > 1:
# new_modlist = [ModFn(DTKConfigBuilder.set_param, 'Run_Number', seed) for seed in range(num_seeds)]
# modlists.append(new_modlist)
#
