def add_forest_migration(cb, start, years, forest, non_forest,
mig_type='one_season') :
if mig_type == 'one_season' :
months_of_mig = 5
elif mig_type == 'planting' :
months_of_mig = 1
second_season_offset = 6
else :
print('Warning: Unrecognized migration type %s' % mig_type)
return
# add seasonal migration to forest nodes
for node_id in forest:
nodes_from = non_forest
for year in range(years):
add_migration_event(cb, nodeto=node_id,
duration_at_node_distr_type='GAUSSIAN_DURATION',
start_day=year*365 + start,
coverage=0.5,
duration_of_stay=30,
duration_of_stay_2=10,
duration_before_leaving_distr_type='GAUSSIAN_DURATION',
duration_before_leaving=3,
duration_before_leaving_2=0.75,
repetitions=4*months_of_mig, # NORMALLY 5 months of migration (4*5)
tsteps_btwn=7, # send a pulse every week
# target=target_ages,
nodesfrom={'class': 'NodeSetNodeList',
'Node_List': nodes_from},
ind_property_restrictions=[{"ForestGoing": "LovesForest"}])
if mig_type == 'planting' :
add_migration_event(cb, nodeto=node_id,
duration_at_node_distr_type='GAUSSIAN_DURATION',
start_day=year * 365 + start + second_season_offset*30, # start in august of each year + 6m for end of planting
coverage=0.5,
duration_of_stay=30,
duration_of_stay_2=10,
duration_before_leaving_distr_type='GAUSSIAN_DURATION',
duration_before_leaving=3,
duration_before_leaving_2=0.75,
repetitions=4 * months_of_mig, # 1 month of migration
tsteps_btwn=7, # send a pulse every week
# target=target_ages,
nodesfrom={'class': 'NodeSetNodeList',
'Node_List': nodes_from},
ind_property_restrictions=[{"ForestGoing": "LovesForest"}])
return { 'Forest_Migration_Start_Day' : start}
def __call__(self, cb):
return add_migration_event(cb, self.nodeto, start_day=self.start_day, coverage=self.coverage,
repetitions=self.repetitions,
tsteps_btwn=self.tsteps_btwn,
duration_at_node_distr_type=self.duration_at_node_distr_type,
duration_of_stay=self.duration_of_stay,
duration_of_stay_2=self.duration_of_stay_2,
duration_before_leaving_distr_type=self.duration_before_leaving_distr_type,
duration_before_leaving=self.duration_before_leaving,
duration_before_leaving_2=self.duration_before_leaving_2,
target=self.target, nodesfrom=self.nodesfrom)
def add_travellers(cb, catch):
all_nodes = list(find_cells_for_this_catchment(catch))
add_migration_event(cb,
nodeto=100000,
coverage=0.5,
repetitions=500,
tsteps_btwn=30,
duration_of_stay=3,
duration_before_leaving_distr_type='UNIFORM_DURATION',
duration_before_leaving=0,
duration_before_leaving_2=30,
nodesfrom={
'class': 'NodeSetNodeList',
'Node_List': all_nodes
},
ind_property_restrictions=[{
'TravelerStatus':
'IsTraveler'
}])
def add_interventions_and_reports(cb, sim_time_dict):
sim_start_year = sim_time_dict["sim_start_year"]
sim_length_years = sim_time_dict["sim_length_years"]
sim_filter_start_time = sim_time_dict["sim_filter_start_time"]
sim_filter_duration = sim_time_dict["sim_filter_duration"]
mozamb_exp.start_year = sim_start_year
mozamb_exp.sim_length_years = sim_length_years
mozamb_exp.implement_baseline_healthseeking(
cb
) #fixme In future, pass start_year and sim_length_years to avoid race conditions
mozamb_exp.implement_interventions(cb, True, True, False, True, True)
all_nodes = list(mozamb_exp.desired_cells)
if importation_version == 0:
cb.update_params({
'x_Regional_Migration': 0.03,
})
add_migration_event(
cb,
nodeto=100000,
coverage=0.5,
repetitions=500,
tsteps_btwn=30,
duration_of_stay=3,
duration_before_leaving_distr_type='UNIFORM_DURATION',
duration_before_leaving=0,
duration_before_leaving_2=30,
nodesfrom={
'class': 'NodeSetNodeList',
'Node_List': all_nodes
},
ind_property_restrictions=[{
'TravelerStatus': 'IsTraveler'
}])
elif importation_version == 1:
cb.update_params({
'x_Regional_Migration': 0.0405,
})
# Add specific reports that we want (have to do it here because we need to know what times to filter for):
# Add filter report that has same length for both burn-in and non-burn-in runs (2010-2020).
# This version is for grid-level prevalence comparison
add_filtered_spatial_report(
cb,
start=sim_filter_start_time,
end=(sim_filter_start_time + sim_filter_duration),
# channels=['Population', 'New_Diagnostic_Prevalence'])
channels=['Population',
'True_Prevalence']) # 'New_Clinical_Cases','New_Infections'
# Add filter that has same length for both burn-in and non-burn-in runs (2010-2020)
# This version is for HF-level incidence comparison. Add a similar report for the migration node
add_filtered_report(cb,
start=sim_filter_start_time,
end=(sim_filter_start_time + sim_filter_duration),
nodes=all_nodes)
add_filtered_report(cb,
start=sim_filter_start_time,
end=(sim_filter_start_time + sim_filter_duration),
nodes=[EIR_node_label],
description='Work')
# # Add filter report for prevalence in each bairro
foo = bairro_df.groupby('bairro')
for (bairro_num, df) in foo:
add_filtered_report(cb,
start=sim_filter_start_time,
end=(sim_filter_start_time + sim_filter_duration),
nodes=[int(x) for x in df['grid_cell'].values],
description=str(int(bairro_num)))
# Add counter report for clinical incidence
add_event_counter_report(cb,
event_trigger_list=[
'Received_Treatment', 'Received_IRS',
'Received_Campaign_Drugs',
'Received_RCD_Drugs', 'Bednet_Got_New_One',
'Received_Test'
],
start=sim_filter_start_time,
duration=sim_filter_duration)
all_nodes = list(mozamb_exp.desired_cells)
cb.params['logLevel_JsonConfigurable'] = "WARNING"
#travelers:
cb.params['Disable_IP_Whitelist'] = 1
add_migration_event(
cb,
nodeto=100000,
# coverage=0.5,
coverage=0,
repetitions=500,
# tsteps_btwn=30,
tsteps_btwn=3000,
duration_of_stay=3,
duration_before_leaving_distr_type='UNIFORM_DURATION',
duration_before_leaving=0,
duration_before_leaving_2=30,
nodesfrom={
'class': 'NodeSetNodeList',
'Node_List': all_nodes
},
ind_property_restrictions=[{
'TravelerStatus': 'IsTraveler'
}])
add_filtered_report(cb, nodes=all_nodes, description='Catchment')
add_filtered_report(cb, nodes=[100000], description='Work')
# Add counter report for clinical incidence
