def vaccin_model(initial_states,
scenario,
effectivity=None,
injection_day=0,
injection_ratio=0,
Nc_fun=None,
N_vacc_fun=vacc_strategy,
levels=levels):
"""
Function to initialize the model given a certain vaccination strategy
"""
params = model_parameters.get_COVID19_SEIRD_parameters()
params.update({
'l': 5,
'tau': 5,
'prev_home': 0.5,
'prev_schools': 0.5,
'prev_work': 0.5,
'prev_rest': 0.5,
'scenario': scenario,
'injection_day': injection_day,
'injection_ratio': injection_ratio
})
tdp = {}
if Nc_fun is not None:
tdp.update({'Nc': Nc_fun})
if N_vacc_fun is not None:
tdp.update({'N_vacc': N_vacc_fun})
params.update({
'd': d,
'NH': NH,
'e': np.array([effectivity] * levels)
})
return models.COVID19_SEIRD(initial_states,
params,
time_dependent_parameters=tdp)
# Measures
start_measures = pd.to_datetime('2020-03-15')
if t < start_measures:
return param
elif start_measures < t <= start_measures + l_days:
return ramp_fun(param, effectivity * param, t, start_measures, l)
else:
return param * effectivity
# --------------------
# Initialize the model
# --------------------
# Load the model parameters dictionary
params = model_parameters.get_COVID19_SEIRD_parameters(VOC=False,
vaccination=False)
# Add the time-dependant parameter function arguments
params.update({'l': 15, 'effectivity': 0.5})
# Define initial states
initial_states = {"S": initN, "E": np.ones(9), "I": np.ones(9)}
# Initialize model
model = models.COVID19_SEIRD(initial_states,
params,
time_dependent_parameters={'Nc': compliance_func})
# -----------------------
# Define helper functions
# -----------------------
from covid19model.optimization.utils import assign_PSO, plot_PSO, perturbate_PSO
# Extract build contact matrix function
from covid19model.models.time_dependant_parameter_fncs import make_contact_matrix_function, ramp_fun
policies_WAVE1 = make_contact_matrix_function(df_google, Nc_all).policies_WAVE1
# ---------------------------------------------------
# Function to add poisson draws and sampling function
# ---------------------------------------------------
from covid19model.models.utils import output_to_visuals, draw_fcn_WAVE1
# --------------------
# Initialize the model
# --------------------
# Load the model parameters dictionary
params = model_parameters.get_COVID19_SEIRD_parameters(VOC=False)
# Add the time-dependant parameter function arguments
params.update({
'l': 21,
'prev_schools': 0,
'prev_work': 0.5,
'prev_rest': 0.5,
'prev_home': 0.5
})
# Define initial states
initial_states = {"S": initN, "E": np.ones(9), "I": np.ones(9)}
# Initialize model
model = models.COVID19_SEIRD(initial_states,
params,
time_dependent_parameters={'Nc': policies_WAVE1})
# --------------------------------------
# Time-dependant social contact function
# --------------------------------------
# Extract build contact matrix function
from covid19model.models.time_dependant_parameter_fncs import make_contact_matrix_function, delayed_ramp_fun, ramp_fun
contact_matrix_4prev = make_contact_matrix_function(df_google, Nc_all)
policies_WAVE2_full_relaxation = make_contact_matrix_function(
df_google, Nc_all).policies_WAVE2_full_relaxation
# --------------------
# Initialize the model
# --------------------
# Load the model parameters dictionary
params = model_parameters.get_COVID19_SEIRD_parameters(vaccination=True)
# Add the time-dependant parameter function arguments
# Social policies
params.update({
'l': 21,
'prev_schools': 0,
'prev_work': 0.5,
'prev_rest': 0.5,
'prev_home': 0.5,
'relaxdate': '2021-07-01',
'l_relax': 31
})
# VOC
params.update({'t_sig': '2021-08-01'}) # No new Indian variant currently
# Vaccination
params.update({
param_dict['prev_rest'] = samples_dict['prev_rest'][idx]
return param_dict
results = pd.DataFrame(columns=['Date'])
print('2) Starting scenario loop\n')
for scenario in scenarios:
print('\t# scenario ' + scenario)
# --------------------
# Initialize the model
# --------------------
# Load the model parameters using `get_COVID19_SEIRD_parameters()`.
params = model_parameters.get_COVID19_SEIRD_parameters()
# Add the time-dependant parameter function arguments
params.update({
'l': 5,
'tau': 5,
'prev_home': 0.5,
'prev_schools': 0.5,
'prev_work': 0.5,
'prev_rest': 0.5,
'scenario': scenario,
's': np.ones(9)
})
# Initialize
model = models.COVID19_SEIRD(
initial_states,
params,
from covid19model.models.time_dependant_parameter_fncs import make_contact_matrix_function, make_mobility_update_function
# Define contact matrix functions based on 4 prevention parameters (effectivity parameters)
policies_wave1 = make_contact_matrix_function(
df_google, Nc_all).policies_WAVE1 # with delayed-ramp function
# Mobility update function from class __call__ and function wrapper to get the right signature
mobility_wrapper_function = make_mobility_update_function(
proximus_mobility_data, proximus_mobility_data_avg).mobility_wrapper_func
# ---------------------
# Load model parameters
# ---------------------
# Load the model parameters dictionary
params = model_parameters.get_COVID19_SEIRD_parameters(spatial=agg, VOC=False)
# Add the time-dependant parameter function arguments
params.update({
'l':
5, # will be varied over in the full PSO/MCMC. Unimportant for pre-lockdown simulation
'prev_home':
0.5, # will be varied over in the full PSO/MCMC. Unimportant for pre-lockdown simulation
'prev_schools': 0, # fixed for wave 1
'prev_work':
0.5, # will be varied over in the full PSO/MCMC. Unimportant for pre-lockdown simulation
'prev_rest':
0.5, # will be varied over in the full PSO/MCMC. Unimportant for pre-lockdown simulation
'tau':
0.1 # tau has little to no influence. Fix it at low value in delayed_ramp_func
})
# Add parameters for the daily update of proximus mobility
