def preload_intervention_csvs(catch,
simulation_start_date,
hs_file=hs_default,
itn_file=itn_default,
irs_file=irs_default,
mda_file=mda_default,
rcd_file=rcd_default):
hs_df = pd.read_csv(hs_file)
itn_df = pd.read_csv(itn_file)
irs_df = pd.read_csv(irs_file)
mda_df = pd.read_csv(mda_file)
rcd_df = pd.read_csv(rcd_file)
df_dict = {"hs": hs_df,
"itn": itn_df,
"irs": irs_df,
"mda": mda_df,
"rcd": rcd_df}
for key in list(df_dict.keys()):
df = df_dict[key]
# Add column which specifies which simulation day the intervention of that row is to be implemented
df['simday'] = [convert_to_day_365(x, simulation_start_date, "%Y-%m-%d") for x in df.fulldate]
return df_dict
def add_irs(self, cb, filename=None):
if not filename:
filename = self.dropbox_base + "inputs/grid_csv/grid_all_irs_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
]
irs_field_list = ["cov_all", "killing", "exp_duration", "box_duration"]
binned_intervene_events, binned_and_grouped, data_fields = self.try_campaign_compression_v2(
intervene_events, irs_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_IRS(cb,
start=int(table_dict['simday']),
coverage_by_ages=[{
'coverage': float(table_dict['cov_all'])
}],
waning={
"Killing_Config": {
"Initial_Effect":
float(table_dict['killing']),
"Decay_Time_Constant":
float(table_dict['exp_duration']),
"Box_Duration":
float(table_dict['box_duration']),
"class":
"WaningEffectBoxExponential"
}
},
nodeIDs=nodeIDs)
def prev_ref_data(catch, start_date='2009-01-01'):
reference_csv = pd.read_csv(
os.path.join(
project_folder,
"dtk_simulation_input/mozambique/grid_prevalence_with_dates.csv"))
lookup_csv = pd.read_csv(
os.path.join(
project_folder,
"dtk_simulation_input/mozambique/grid_lookup_with_neighborhood.csv"
))
return_data = reference_csv.merge(lookup_csv, how='left', on='grid_cell')
return_data['sim_date'] = return_data['date'].apply(
lambda x: convert_to_day_365(x, start_date))
return_data['sim_date'] = return_data['sim_date'] - 1
in_catch = return_data['catchment'] == catch
return return_data[in_catch]
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 preload_intervention_csvs(catch,
sim_start_date,
hs_file=hs_default,
itn_file=itn_default,
irs_file=irs_default,
mda_file=mda_default,
rcd_file=rcd_default):
hs_df = pd.read_csv(hs_file)
itn_df = pd.read_csv(itn_file)
irs_df = pd.read_csv(irs_file)
mda_df = pd.read_csv(mda_file)
rcd_df = pd.read_csv(rcd_file)
df_dict = {
"hs": hs_df,
"itn": itn_df,
"irs": irs_df,
"mda": mda_df,
"rcd": rcd_df
}
# Add simday column for adding to campaign file
for key in list(df_dict.keys()):
df = df_dict[key]
# Add column which specifies which simulation day the intervention of that row is to be implemented
df['simday'] = [
convert_to_day_365(x, sim_start_date, "%Y-%m-%d")
for x in df.fulldate
]
# Restrict to catchment of interest
catch_cells = find_cells_for_this_catchment(catch)
for key in list(df_dict.keys()):
df = df_dict[key]
df = df[np.in1d(df["grid_cell"], catch_cells)]
df.reset_index(inplace=True)
df_dict[key] = df
return df_dict
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_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_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']))
