def add_nets(cb, coverage=0.6, number=0, num_nodes=0, start_day=180):
# # ITN
add_ITN_age_season(
cb,
180,
demographic_coverage=coverage,
discard_times={
"Expiration_Distribution_Type": "EXPONENTIAL_DURATION",
"Expiration_Period": 639
},
blocking_config=WaningEffectExponential(Decay_Time_Constant=730,
Initial_Effect=0.6),
killing_config=WaningEffectExponential(Decay_Time_Constant=1460,
Initial_Effect=0.7))
add_ITN_age_season(
cb,
180 + 3 * 365,
demographic_coverage=coverage,
discard_times={
"Expiration_Distribution_Type": "EXPONENTIAL_DURATION",
"Expiration_Period": 639
},
blocking_config=WaningEffectExponential(Decay_Time_Constant=730,
Initial_Effect=0.6),
killing_config=WaningEffectExponential(Decay_Time_Constant=1460,
Initial_Effect=0.7))
return {
'ITN_Coverage': coverage,
'Release_Number': number,
'Num_Nodes': num_nodes,
'Start_Day': start_day
}
def fn(self, cb):
add_ITN_age_season(cb, start=self.start,
age_dep=self.age_dep,
coverage_all=self.coverage,
as_birth=False,
seasonal_dep=self.seasonal_dep,
discard=self.discard)
def add_bednets_for_population_and_births(
cb,
coverage,
start_day=1,
seasonal_dependence=None,
discard_config_type="default",
age_dependence=default_bednet_age_usage,
killing_config=None,
blocking_config=None):
if seasonal_dependence is None:
seasonal_dependence = {}
# regular_bednet_distribution
add_ITN_age_season(cb,
birth_triggered=False,
start=start_day,
age_dependence=age_dependence,
demographic_coverage=coverage,
seasonal_dependence=seasonal_dependence,
discard_times=discard_config[discard_config_type],
blocking_config=blocking_config,
killing_config=killing_config)
# birth_triggered_bednet_distribution
add_ITN_age_season(cb,
birth_triggered=True,
start=start_day,
age_dependence=age_dependence,
demographic_coverage=coverage,
seasonal_dependence=seasonal_dependence,
discard_times=discard_config[discard_config_type],
blocking_config=blocking_config,
killing_config=killing_config)
def add_itn(cb, events_df):
itn_field_list = ["age_cov", "cov_all", "min_season_cov", "fast_fraction"]
binned_intervene_events, binned_and_grouped, data_fields = try_campaign_compression(events_df, itn_field_list)
birthnet_df = events_df.copy(deep=True)
birthnet_df.sort_values(by='simday', inplace=True)
birthnet_df['duration'] = birthnet_df.groupby('grid_cell')['simday'].shift(-1).sub(birthnet_df['simday'])
birthnet_df['duration'].fillna(-1, inplace=True)
birthnet_field_list = itn_field_list + ["duration"]
BIRTH_binned_intervene_events, BIRTH_binned_and_grouped, BIRTH_data_fields = try_campaign_compression(birthnet_df, birthnet_field_list)
for table, group in binned_and_grouped:
table_dict = dict(zip((data_fields), table))
node_list = sorted(list(set(group['grid_cell']))) # fixme Needed to add this because sometimes there are duplicate nodes in the list, and this breaks things
start = float(table_dict['simday'])
if start >= 0:
# Regular bednet distribution
add_ITN_age_season(cb,
start=float(table_dict['simday']),
age_dep={'youth_cov': float(table_dict['age_cov']),
'youth_min_age': 5,
'youth_max_age': 20},
coverage_all=float(table_dict['cov_all']),
as_birth=False,
seasonal_dep={'min_cov': float(table_dict['min_season_cov']),
'max_day': 60},
discard={'halflife1': 260,
'halflife2': 2106,
'fraction1': float(table_dict['fast_fraction'])},
nodeIDs=node_list)
# Birthnet distribution
for table, group in BIRTH_binned_and_grouped:
table_dict = dict(zip((BIRTH_data_fields), table))
node_list = sorted(group['grid_cell'])
start = float(table_dict['simday'])
if start >= 0:
add_ITN_age_season(cb,
as_birth=True,
duration=table_dict['duration'],
start=float(table_dict['simday']),
age_dep={'youth_cov': float(table_dict['age_cov']),
'youth_min_age': 5,
'youth_max_age': 20},
coverage_all=float(table_dict['cov_all']),
seasonal_dep={'min_cov': float(table_dict['min_season_cov']),
'max_day': 60},
discard={'halflife1': 260,
'halflife2': 2106,
'fraction1': float(table_dict['fast_fraction'])},
nodeIDs=node_list)
def add_annual_itns(cb, year_count=1, coverage=0.8, start_day=0, initial_killing=0.3, discard_time=270, IP=[]):
for year in range(year_count):
add_ITN_age_season(cb,
coverage_all=coverage,
discard={"halflife": discard_time},
waning={'kill_initial': initial_killing},
start=(365 * year) + start_day,
ind_property_restrictions=IP)
return {"ITN_Coverage": coverage,
"ITN_Start": start_day,
"ITN_killing" : initial_killing,
'ITN_discard' : discard_time}
def add_ITNs(cb,
start=0,
coverage=0.8,
insecticide='',
label='No_nets',
killing=0.9):
add_ITN_age_season(
cb,
start=start,
demographic_coverage=coverage,
insecticide=insecticide,
discard_times={
"Expiration_Distribution_Type": "EXPONENTIAL_DISTRIBUTION",
"Expiration_Period_Exponential": 660
},
blocking_config=WaningEffectExponential(Initial_Effect=0.9,
Decay_Time_Constant=730),
killing_config=WaningEffectExponential(Initial_Effect=killing,
Decay_Time_Constant=1460))
cb.update_params({
"Report_Event_Recorder":
1,
"Report_Event_Recorder_Events":
["Bednet_Got_New_One", "Bednet_Discarded"],
"Report_Event_Recorder_Ignore_Events_In_List":
0,
"Report_Event_Recorder_Individual_Properties": []
})
return {
'Insecticide': insecticide,
'Coverage': coverage,
'Start': start,
'Label': label
}
def add_itn(self, cb, filename=None):
if not filename:
filename = self.dropbox_base + "inputs/grid_csv/grid_all_itn_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
]
itn_field_list = [
"age_cov", "cov_all", "min_season_cov", "fast_fraction"
]
binned_intervene_events, binned_and_grouped, data_fields = self.try_campaign_compression_v2(
intervene_events, itn_field_list)
birthnet_df = intervene_events.copy(deep=True)
birthnet_df['duration'] = birthnet_df.groupby(
'grid_cell')['simday'].shift(-1).sub(birthnet_df['simday'])
birthnet_df['duration'].fillna(-1, inplace=True)
birthnet_field_list = itn_field_list + ["duration"]
BIRTH_binned_intervene_events, BIRTH_binned_and_grouped, BIRTH_data_fields = self.try_campaign_compression_v2(
birthnet_df, birthnet_field_list)
for table, group in binned_and_grouped:
table_dict = dict(zip((data_fields), table))
node_list = sorted(
list(set(group['grid_cell']))
) #fixme Needed to add this because sometimes there are duplicate nodes in the list, and this breaks things
if node_list == sorted(self.catch_cells):
nodeIDs = []
else:
nodeIDs = node_list
start = float(table_dict['simday'])
if start >= 0:
# Regular bednet distribution
add_ITN_age_season(cb,
start=float(table_dict['simday']),
age_dep={
'youth_cov':
float(table_dict['age_cov']),
'youth_min_age': 5,
'youth_max_age': 20
},
coverage_all=float(table_dict['cov_all']),
as_birth=False,
seasonal_dep={
'min_cov':
float(table_dict['min_season_cov']),
'max_day':
60
},
discard={
'halflife1':
260,
'halflife2':
2106,
'fraction1':
float(table_dict['fast_fraction'])
},
nodeIDs=nodeIDs)
# Birthnet distribution
for table, group in BIRTH_binned_and_grouped:
table_dict = dict(zip((BIRTH_data_fields), table))
node_list = sorted(group['grid_cell'])
if node_list == sorted(self.catch_cells):
nodeIDs = []
else:
nodeIDs = node_list
start = float(table_dict['simday'])
if start >= 0:
add_ITN_age_season(cb,
as_birth=True,
duration=table_dict['duration'],
start=float(table_dict['simday']),
age_dep={
'youth_cov':
float(table_dict['age_cov']),
'youth_min_age': 5,
'youth_max_age': 20
},
coverage_all=float(table_dict['cov_all']),
seasonal_dep={
'min_cov':
float(table_dict['min_season_cov']),
'max_day':
60
},
discard={
'halflife1':
260,
'halflife2':
2106,
'fraction1':
float(table_dict['fast_fraction'])
},
nodeIDs=nodeIDs)
def add_itns(self):
# Add ITN events:
itn_events_list = ["Bednet_Got_New_One","Bednet_Using","Bednet_Discarded"]
self.cb.update_params({
"Report_Event_Recorder_Ignore_Events_In_List": 0,
"Listed_Events": itn_events_list,
"Report_Event_Recorder_Events": [],
"Report_Event_Recorder": 1
})
itn_events = pd.read_csv(self.itn_filename)
itn_events['simday'] = [convert_to_day(x, self.sim_start_date, "%Y-%m-%d") for x in itn_events.fulldate]
itn_events = itn_events[np.in1d(itn_events['grid_cell'], self.demo_cells)]
itn_events.reset_index(inplace=True, drop=True)
itn_events = itn_events[['event','fulldate','cov_all','age_cov','min_season_cov','fast_fraction','grid_cell','simday']]
birthnet_events = generate_birthnets_df_from_itn_event_file(itn_events)
birthnet_events = birthnet_events[['event','fulldate','cov_all','age_cov','min_season_cov','fast_fraction','grid_cell','simday','duration']]
[binned_and_grouped, grouped_by_fields] = self.try_campaign_compression(itn_events[itn_events['simday'] >= 0])
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
# Regular bednet distribution
cov_all = float(table['cov_all'])
if cov_all > 0:
add_ITN_age_season(self.cb,
start=float(table['simday']),
age_dep={'youth_cov': float(table['age_cov']),
'youth_min_age': 5,
'youth_max_age': 20},
coverage_all=float(table['cov_all']),
as_birth=False,
seasonal_dep={'min_cov': float(table['min_season_cov']),
'max_day': 1},
discard={'halflife1': 260,
'halflife2': 2106,
'fraction1': float(table['fast_fraction'])},
nodeIDs=nodeIDs)
# Separately handle birth nets:
[binned_and_grouped, grouped_by_fields] = self.try_campaign_compression(birthnet_events[birthnet_events['simday'] >= 0])
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_ITN_age_season(self.cb,
start=float(table['simday']),
age_dep={'youth_cov': float(table['age_cov']), 'youth_min_age': 5,
'youth_max_age': 20},
coverage_all=float(table['cov_all']),
seasonal_dep={'min_cov': float(table['min_season_cov']), 'max_day': 60},
discard={'halflife1': 260,
'halflife2': 2106,
'fraction1': float(table['fast_fraction'])},
nodeIDs=nodeIDs,
as_birth=True,
duration=table['duration'])
builder = ModBuilder.from_combos(
#[ModFn(DTKConfigBuilder.set_param, 'HEG_Infection_Modification', im) for im in [1.0]],
#[ModFn(DTKConfigBuilder.set_param, 'HEG_Fecundity_Limiting', fl) for fl in [0.05, 0.1, 0.15]],
#[ModFn(DTKConfigBuilder.set_param, 'HEG_Homing_Rate', hr) for hr in [0.9, 0.95, 1.0]],
#[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,
