x0_vec = [
x00,
x00,
x00,
x00,
x00,
x00,
x00,
x00,
x00,
]
model_type = 'SEIR'
file_Bopt = Path('B_opt_SEIR_Ti_5_R0_2.95.mat')
file_B_opt = Path(dir_B_opt, file_Bopt)
Sigma = load_mat(file_B_opt, 'Sigma')
Gamma = load_mat(file_B_opt, 'Gamma')
B_opt_country = get_B_policy(
file_B_opt,
country,
'Uncontained',
)
Ng = B_opt_country.shape[0]
assert len(
age_groups
) == 9, 'Check the defined age-groups. Something is wrong there.'
ref_obj = epid_sim(
model_type=model_type,
B=B_opt_country,
Gamma=Gamma,
Sigma=Sigma,
dir_source_mat = Path(Path(__file__).resolve().parent)
file_data_opt_SIR = Path(dir_source_mat, 'Optimised_B', 'SIR_B_opt_normalised.mat')
dir_save_plots_main = Path(Path(__file__).resolve().parent, 'sample_output')
file_data_opt_SIS = Path(dir_source_mat, 'Optimised_B', 'SIS_B_opt_normalised.mat')
file_data_opt_SIR = Path(dir_source_mat, 'Optimised_B', 'SIR_B_opt_normalised.mat')
B_opt_SIS = get_B_policy(file_data_opt_SIS, country, 'Uncontained', )
B_opt_SIR = get_B_policy(file_data_opt_SIR, country, 'Uncontained', )
# age groups, used as legends in plots
age_groups = ['[0-10]', '[10-20]', '[20-30]', '[30-40]',
'[40-50]', '[50-60]', '[60-70]', '[70-80]', '80+',]
Ng = len(age_groups)
alpha = load_mat(file_data_opt_SIR, 'alpha')
alpha_vec = np.ones((Ng, 1)) * alpha
Gamma = np.zeros((Ng,Ng))
np.fill_diagonal(Gamma, alpha_vec)
#
Sigma = Gamma *10
model_type = 'SIR'
#%% reference policy: uncontained
policy_name = 'Uncontained'
list_t_switch = [0,]
all_containment_list = ['Uncontained',]
ref_obj = epid_sim(model_type=model_type,
B=B_opt_SIR,
def main(country,
policy_name,
policy_definition,
dir_save_plots_main='',
t_end=541,
x0_vec=[1e-3],
xternal_inputs={},
if_title=False):
"""
Simulate the epid model.
Calculate trajectory of an epid model for a country and for a defined policy.
Parameters
----------
country : str
Name of the country, for which population distribution is defined in the code.
policy_name : str
a given name to oplicy used as subfolder name.
policy_definition : dict
a dictionary of {time: basic_policy} format.
dir_save_plots_main : pathlib Path
main folder to save plots. Then a subfolder in country/policyname is used to save files.
xternal_inputs : dict
dictionary of time/xternal_input. To model new Infectiouss fro moutside the populations
example: incoming flights, etc.
t_end : float
end time of simulation, in days
x0_vec : list of floats
a list of size 1, Ng, or 2*Ng to specifiy initial conditions. Ng is number of groups.
Returns
-------
dict_out : dictionary
a dicitonary of all stratified and aggregated solutions of SIS and SIR models.
"""
# local imports
from mitepid.models import SIS, SIR
from mitepid.plots import bplot
from mitepid.utils import sol_aggregate, load_mat
from mitepid.utils import sort_out_t_policy_x0
from mitepid.policies import get_Bopt, str_policy_info, get_pop_distr
# external imports
from scipy.integrate import odeint
import numpy as np
from numpy.linalg import inv, eigvals
from pathlib import Path
print('***************************************************')
# assuming data files are where the .py file is:
dir_source_mat = Path(Path(__file__).resolve().parent)
file_data_opt_SIR = Path(dir_source_mat, 'Optimised_B',
'SIR_B_opt_normalised.mat')
file_data_opt_SIS = Path(dir_source_mat, 'Optimised_B',
'SIS_B_opt_normalised.mat')
dir_save_plots_country = Path(dir_save_plots_main, country)
B_opt_SIS_orig = get_Bopt(
file_data_opt_SIS,
country,
'Uncontained',
)
B_opt_SIR_orig = get_Bopt(
file_data_opt_SIR,
country,
'Uncontained',
)
Ng = B_opt_SIS_orig.shape[0] # number of age groups
# making the D matrix, alpha represents recovery rate
alpha = load_mat(file_data_opt_SIR, 'alpha')
ALPHA = np.ones((Ng, 1)) * alpha
D = np.zeros((Ng, Ng))
np.fill_diagonal(D, ALPHA)
# age groups, used as legends in plots
age_groups = [
'[0-10]',
'[10-20]',
'[20-30]',
'[30-40]',
'[40-50]',
'[50-60]',
'[60-70]',
'[70-80]',
'80+',
]
#
if policy_name == 'Uncontained':
print('policy is uncontained')
if_uncontained = True
else:
if_uncontained = False
# initial conditions, t_f
if len(x0_vec) == 1:
x0_SIS = np.ones(Ng) * x0_vec
x0_SIR = np.concatenate((np.ones(Ng) * x0_vec, np.zeros(Ng)))
elif len(x0_vec) == Ng:
x0_SIS = x0_vec
x0_SIR = np.concatenate((x0_vec, np.zeros(Ng)))
elif len(x0_vec) == 2 * Ng:
x0_SIS = x0_vec[:Ng]
x0_SIR = x0_vec
else:
raise ('Something wrong with initial conditions vector!')
# uncontained solution
t = np.arange(0, t_end + .01, step=0.1)
sol_SIS_orig = odeint(SIS, x0_SIS, t, args=(B_opt_SIS_orig, ALPHA))
sol_SIR_orig = odeint(SIR, x0_SIR, t, args=(B_opt_SIR_orig, ALPHA))
sol_SIR_I_orig = sol_SIR_orig[:, :Ng]
sol_SIR_R_orig = sol_SIR_orig[:, Ng:]
sol_agg_SIS_orig = sol_aggregate(sol_SIS_orig, get_pop_distr(country))
sol_agg_SIR_I_orig = sol_aggregate(sol_SIR_I_orig, get_pop_distr(country))
sol_agg_SIR_R_orig = sol_aggregate(sol_SIR_R_orig, get_pop_distr(country))
list_t1, list_t2, list_policies, list_x0, list_t_switch, list_all_policies = \
sort_out_t_policy_x0(policy_definition, xternal_inputs, t_end, Ng)
str_policy = policy_name # used to make subfolder name
dir_save_plots = Path(dir_save_plots_country, Path(str_policy))
dir_save_plots.mkdir(exist_ok=True, parents=True)
print('Saving in:')
print(dir_save_plots)
file_policy = Path(dir_save_plots, 'policy_details.txt')
with open(file_policy, 'w') as f:
f.write('\n time (day) ---> Policy')
f.write('\n ------------------------------------------')
for idx in np.arange(len(list_t1)):
t_switch1 = list_t1[idx]
t_switch2 = list_t2[idx]
policy = list_policies[idx]
x0_xtrnal = list_x0[idx]
with open(file_policy, 'a') as my_tex:
my_tex.write('\n %10.1f ---> %s' %
(t_switch1, policy)) # remove previous contents
B_opt_SIS = get_Bopt(file_data_opt_SIS, country, policy)
B_opt_SIR = get_Bopt(file_data_opt_SIR, country, policy)
#%% R_0 policy
rho = np.max(np.abs(eigvals(np.matmul(
-inv(D), B_opt_SIR)))) # spectral radius of -inv(D)*B
print('%s ---> R_0 = %2.2f' % (policy, rho))
t_step = np.arange(t_switch1, t_switch2, step=0.1)
x0_SIS = np.array(x0_SIS) + np.array(x0_xtrnal)
x0_SIR = np.array(x0_SIR) + np.concatenate((x0_xtrnal, np.zeros(Ng)))
# solve the ODE
sol_SIS_step = odeint(SIS, x0_SIS, t_step, args=(B_opt_SIS, ALPHA))
sol_SIR_step = odeint(SIR, x0_SIR, t_step, args=(B_opt_SIR, ALPHA))
# update x0
x0_SIS = sol_SIS_step[-1]
x0_SIR = sol_SIR_step[-1]
if idx == 0:
sol_SIR = sol_SIR_step
sol_SIS = sol_SIS_step
else:
sol_SIR = np.concatenate((sol_SIR, sol_SIR_step))
sol_SIS = np.concatenate((sol_SIS, sol_SIS_step))
sol_SIR_I = sol_SIR[:, :Ng]
sol_SIR_R = sol_SIR[:, Ng:]
with open(file_policy, 'a') as my_tex:
my_tex.write('\n \n \n ')
my_tex.write(str_policy_info()) # just to remove previous contents
# calculate aggregate solutions
sol_agg_SIS = sol_aggregate(sol_SIS, get_pop_distr(country))
sol_agg_SIR_I = sol_aggregate(sol_SIR_I, get_pop_distr(country))
sol_agg_SIR_R = sol_aggregate(sol_SIR_R, get_pop_distr(country))
# save various solutions in a sictioanry to return to the main function
dict_out = {}
dict_out['sol_agg_SIR_R'] = sol_agg_SIR_R
dict_out['sol_agg_SIR_I'] = sol_agg_SIR_I
dict_out['sol_agg_SIS'] = sol_agg_SIS
dict_out['sol_SIR_R'] = sol_SIR_R
dict_out['sol_SIR_I'] = sol_SIR_I
dict_out['sol_SIS'] = sol_SIS
dict_out['t'] = t
sol_SIR_I_plot = np.concatenate((sol_SIR_I, sol_SIR_I_orig), axis=1)
sol_SIR_R_plot = np.concatenate((sol_SIR_R, sol_SIR_R_orig), axis=1)
sol_SIS_plot = np.concatenate((sol_SIS, sol_SIS_orig), axis=1)
if not dir_save_plots_main:
if_plot = False # dir_save_plots_main=='' means don't plot
else:
plot_type = 1 # can be 1 -> all in one subplots, or 2 -> each age group in one subplot
if_plot = True
if if_plot:
### SIS
filesave = Path(
dir_save_plots,
'SIS_groups_' + str_policy + '_tf_' + str(int(t_end)) + '.png')
suptitle = country + ' --- SIS'
bplot(t,
sol_SIS_plot,
plot_type=1,
filesave=filesave,
labels=age_groups,
suptitle=suptitle,
list_vl=list_t_switch,
list_all_policies=list_all_policies,
ylabel='Infectious Ratio',
Ng=9,
cmap='viridis')
### SIR_I
suptitle = ''
filesave = Path(
dir_save_plots,
'SIR_I_groups_' + str_policy + '_tf_' + str(int(t_end)) + '.png')
bplot(t,
sol_SIR_I_plot,
plot_type=1,
filesave=filesave,
labels=age_groups,
suptitle=suptitle,
list_vl=list_t_switch,
list_all_policies=list_all_policies,
ylabel='Infectious Ratio',
Ng=9,
cmap='viridis')
filesave = Path(
dir_save_plots, 'SIR_I_groups_multi_ax_' + str_policy + '_tf_' +
str(int(t_end)) + '.png')
bplot(t,
sol_SIR_I_plot,
plot_type=2,
filesave=filesave,
labels=age_groups,
suptitle=suptitle,
list_vl=list_t_switch,
list_all_policies=list_all_policies,
ylabel='Infectious Ratio',
Ng=9,
cmap='Dark2')
### SIR_R
suptitle = ''
filesave = Path(
dir_save_plots,
'SIR_R_groups_' + str_policy + '_tf_' + str(int(t_end)) + '.png')
bplot(t,
sol_SIR_R_plot,
plot_type=1,
filesave=filesave,
labels=age_groups,
suptitle=suptitle,
list_vl=list_t_switch,
list_all_policies=list_all_policies,
ylabel='Recoverd Ratio',
Ng=9,
cmap='viridis',
policy_name_pos=0.5)
suptitle = ''
filesave = Path(
dir_save_plots, 'SIR_R_groups_multi_ax_' + str_policy + '_tf_' +
str(int(t_end)) + '.png')
bplot(t,
sol_SIR_R_plot,
plot_type=2,
filesave=filesave,
labels=age_groups,
suptitle=suptitle,
list_vl=list_t_switch,
list_all_policies=list_all_policies,
ylabel='Recoverd Ratio',
Ng=9,
cmap='Dark2',
policy_name_pos=0.5)
### Aggregate SIS
if if_uncontained:
my_labels_I = ['Uncontained']
my_labels_R = ['Uncontained']
else:
my_labels_I = [
'Uncontained',
'policy',
]
my_labels_R = [
'Uncontained',
'policy',
]
filesave = Path(
dir_save_plots,
'SIS_AGG_' + str_policy + '_tf_' + str(int(t_end)) + '.png')
str_max1 = "{:2.2f}".format((sol_agg_SIS_orig[-1, 0]) * 100) + '%'
if if_uncontained:
sol_agg_SIS_plot = sol_agg_SIS
str_max2 = ''
else:
sol_agg_SIS_plot = np.concatenate((sol_agg_SIS, sol_agg_SIS_orig),
axis=1)
str_max2 = ", {:2.2f}".format((sol_agg_SIS[-1, 0]) * 100) + '%'
suptitle = '\nMaximum Ratio of Total Infectious: ' \
+ str_max1 + str_max2
bplot(t,
sol_agg_SIS_plot,
plot_type=1,
filesave=filesave,
suptitle=suptitle,
labels=my_labels_I,
list_vl=list_t_switch,
list_all_policies=list_all_policies,
ylabel='Infectious Ratio')
### Aggregate SIR
filesave_I = Path(
dir_save_plots,
'SIR_I_AGG_' + str_policy + '_tf_' + str(int(t_end)) + '.png')
filesave_R = Path(
dir_save_plots,
'SIR_R_AGG_' + str_policy + '_tf_' + str(int(t_end)) + '.png')
str_max_I_1 = "{:2.2f}".format(np.max(sol_agg_SIR_I_orig) * 100) + '%'
str_max_R_1 = "{:2.2f}".format(np.max(sol_agg_SIR_R_orig) * 100) + '%'
# Aggregate SIR I
if if_uncontained:
sol_agg_SIR_I_plot = sol_agg_SIR_I
str_max2 = ''
else:
sol_agg_SIR_I_plot = np.concatenate((
sol_agg_SIR_I_orig,
sol_agg_SIR_I,
),
axis=1)
str_max2 = ", {:2.2f}".format(np.max(sol_agg_SIR_I) * 100) + '%'
if if_uncontained:
t_max_I = t[np.argmax(sol_agg_SIR_I_orig)]
str_print_I = str_max_I_1
str_print_R = str_max_R_1
else:
t_max_I = t[np.argmax(sol_agg_SIR_I)]
str_print_I = ", {:2.2f}".format(np.max(sol_agg_SIR_I) * 100) + '%'
str_print_R = ", {:2.2f}".format(np.max(sol_agg_SIR_R) * 100) + '%'
print('x0= ', x0_vec)
print('Max instantaneous Infectious ---> ', str_print_I[1:])
print('Max Removed Population ---> ', str_print_R[1:])
print('TIME to reach max Infectious ---> ', t_max_I, ' days')
if if_title:
suptitle = '\nPeak/Maximum of Infectious Ratio in the population: ' + \
str_max_I_1 + str_max2
else:
suptitle = ''
bplot(t,
sol_agg_SIR_I_plot,
plot_type=1,
filesave=filesave_I,
suptitle=suptitle,
labels=my_labels_I,
list_vl=list_t_switch,
list_all_policies=list_all_policies,
ylabel='Infectious Ratio',
cmap='Dark2')
### Aggregate difference between SIS and SIR outputs
suptitle = ' Difference between solutions to SIS and SIR models'
filesave_diff = Path(
dir_save_plots, 'diff_SIS_SIR_agg_' + str_policy + '_tf_' +
str(int(t_end)) + '.png')
bplot(t,
sol_agg_SIS_plot - sol_agg_SIR_I_plot,
plot_type=1,
filesave=filesave_diff,
suptitle=suptitle,
labels=my_labels_I,
list_vl=list_t_switch,
list_all_policies=list_all_policies,
ylabel='Infectious Ratio')
if if_uncontained:
sol_agg_SIR_R_plot = sol_agg_SIR_R
str_max2 = ''
else:
sol_agg_SIR_R_plot = np.concatenate(
(sol_agg_SIR_R_orig, sol_agg_SIR_R), axis=1)
str_max2 = ", {:2.2f}".format(np.max(sol_agg_SIR_R) * 100) + '%'
if if_title:
suptitle = '\nMaximum Ratio of Removed Compartment in the population: ' + \
str_max_R_1 + str_max2
else:
suptitle = ''
bplot(t,
sol_agg_SIR_R_plot,
plot_type=1,
filesave=filesave_R,
suptitle=suptitle,
labels=my_labels_R,
list_vl=list_t_switch,
list_all_policies=list_all_policies,
ylabel='Removed Ratio',
cmap='Dark2')
return dict_out
def get_Bopt(
file_data_opt,
country='Germany',
policy='Uncontained',
):
"""
Return the contact rates for the specified model (SIS/SIR), country, pre-defined policy.
Parameters
----------
file_data_opt : TYPE
DESCRIPTION.
country : TYPE, optional
DESCRIPTION. The default is 'Germany'.
policy : TYPE, optional
DESCRIPTION. The default is 'Uncontained'.
: TYPE
DESCRIPTION.
Returns
-------
B_opt : TYPE
DESCRIPTION.
"""
# load original (uncontained) Bopt obtained from optimisation performed in matlab
# variable names in saved files is 'B_opt_' + country, example: B_opt_Germany
from mitepid.utils import load_mat, scale_B_opt
varname = 'B_opt_noramlised'
B_opt_normalised = load_mat(file_data_opt, varname)
B_opt_orig = Bopt_normalised_2_country(B_opt_normalised,
get_pop_distr(country))
# these policies are defined intuitively,
# all suggestions to make them more accurate are welcome
#------------------------
if policy == 'Uncontained':
w_kids = 1
w_adults = 1
w_old = 1
list_scales = [
w_kids,
w_kids,
w_adults,
w_adults,
w_adults,
w_adults,
w_adults,
w_old,
w_old,
]
#------------------------
elif policy == 'Schools_closed':
w_kids = 0.2
w_adults = 1
w_old = 1
list_scales = [
w_kids,
w_kids,
w_adults,
w_adults,
w_adults,
w_adults,
w_adults,
w_old,
w_old,
]
#------------------------
elif policy == 'Elderly_self_isolate':
w_kids = 1.0
w_adults = 1.0
w_old = 0.25
list_scales = [
w_kids,
w_kids,
w_adults,
w_adults,
w_adults,
w_adults,
w_adults,
w_old,
w_old,
]
#------------------------
elif policy == 'Kids_Elderly_self_isolate':
w_kids = 0.2
w_adults = 1.0
w_old = 0.25
list_scales = [
w_kids,
w_kids,
w_adults,
w_adults,
w_adults,
w_adults,
w_adults,
w_old,
w_old,
]
#------------------------
elif policy == 'Social_Distancing':
w_kids = 0.2
w_adults = 0.2
w_old = 0.25
list_scales = [
w_kids,
w_kids,
w_adults,
w_adults,
w_adults,
w_adults,
w_adults,
w_old,
w_old,
]
#------------------------
elif policy == 'Lockdown':
w_kids = 0.1
w_adults = 0.1
w_old = 0.1
list_scales = [
w_kids,
w_kids,
w_adults,
w_adults,
w_adults,
w_adults,
w_adults,
w_old,
w_old,
]
#------------------------
elif policy == 'R0_is_1':
w_kids = 0.2984
w_adults = 0.6047
w_old = 0.1015
list_scales = [
w_kids,
w_kids,
w_adults,
w_adults,
w_adults,
w_adults,
w_adults,
w_old,
w_old,
]
#------------------------
elif policy == 'Schools_Offices_closed':
w_kids = 0.2
w_adults = 0.5
w_old = 1
list_scales = [
w_kids,
w_kids,
w_adults,
w_adults,
w_adults,
w_adults,
w_adults,
w_old,
w_old,
]
#------------------------
elif policy == 'Adults_self_isolate':
w_adults = 0.4
list_scales = [
1,
1,
w_adults,
w_adults,
w_adults,
w_adults,
w_adults,
1,
1,
]
#------------------------
elif policy == 'Adults_Elderly_Self_isolate':
w_kids = 1
w_adults = 0.2
w_old = 0.25
list_scales = [
w_kids,
w_kids,
w_adults,
w_adults,
w_adults,
w_adults,
w_adults,
w_old,
w_old,
]
#------------------------
elif policy == 'Lockdown_but_kids':
w_kids = 1
w_adults = 0.1
w_old = 0.1
list_scales = [
w_kids,
w_kids,
w_adults,
w_adults,
w_adults,
w_adults,
w_adults,
w_old,
w_old,
]
elif not policy == 'Uncontained':
raise ('Policy was not recognized.')
if policy == 'Uncontained':
B_opt = B_opt_orig
else:
B_opt = scale_B_opt(B_opt_orig, list_scales)
return B_opt
def get_B_policy(
file_data_opt=None,
country='Germany',
policy='Uncontained',
B=None,
):
"""
Return the contact rates for the specified model (SIS/SIR), country, pre-defined policy.
Parameters
----------
file_data_opt : pathlib.Path
file containing the basic B matrix.
country : str, optional
DESCRIPTION. The default is 'Germany'.
policy : str or (int, float), optional
either a float, in which case assumed to be desired R0. or a str as defined.
the default is 'Uncontained'.
B : numpy 2d array
The B matrix if not to be read from file.
Returns
-------
B_opt : numpy 2d array
The matrix of contact rates.
"""
# load original (uncontained) Bopt obtained from optimisation performed in matlab
# variable names in saved files is 'B_opt_' + country, example: B_opt_Germany
from mitepid.utils import load_mat, scale_B_opt
if not file_data_opt is None:
varname = 'B_opt'
print(file_data_opt)
B_opt_normalised = load_mat(file_data_opt, varname)
B = Bopt_normalised_2_country(B_opt_normalised, get_pop_distr(country))
# these policies are defined intuitively,
# chnage as you wish
R0_used_in_opt = 2.95
if isinstance(policy, (int, float)):
w_kids = policy / R0_used_in_opt
w_adults = policy / R0_used_in_opt
w_old = policy / R0_used_in_opt
list_scales = [
w_kids,
w_kids,
w_adults,
w_adults,
w_adults,
w_adults,
w_adults,
w_old,
w_old,
]
else:
#------------------------
if policy == 'Uncontained':
w_kids = 1
w_adults = 1
w_old = 1
list_scales = [
w_kids,
w_kids,
w_adults,
w_adults,
w_adults,
w_adults,
w_adults,
w_old,
w_old,
]
#------------------------
elif policy == 'Schools_closed':
w_kids = 0.2
w_adults = 1
w_old = 1
list_scales = [
w_kids,
w_kids,
w_adults,
w_adults,
w_adults,
w_adults,
w_adults,
w_old,
w_old,
]
#------------------------
elif policy == 'Elderly_self_isolate':
w_kids = 1.0
w_adults = 1.0
w_old = 0.25
list_scales = [
w_kids,
w_kids,
w_adults,
w_adults,
w_adults,
w_adults,
w_adults,
w_old,
w_old,
]
#------------------------
elif policy == 'Kids_Elderly_self_isolate':
w_kids = 0.2
w_adults = 1.0
w_old = 0.25
list_scales = [
w_kids,
w_kids,
w_adults,
w_adults,
w_adults,
w_adults,
w_adults,
w_old,
w_old,
]
#------------------------
elif policy == 'Social_Distancing' or policy == 'SD':
w_kids = 0.2
w_adults = 0.2
w_old = 0.25
list_scales = [
w_kids,
w_kids,
w_adults,
w_adults,
w_adults,
w_adults,
w_adults,
w_old,
w_old,
]
#------------------------
elif policy == 'Lockdown':
w_kids = 0.1
w_adults = 0.1
w_old = 0.1
list_scales = [
w_kids,
w_kids,
w_adults,
w_adults,
w_adults,
w_adults,
w_adults,
w_old,
w_old,
]
#------------------------
elif policy == 'R0_is_1':
w_kids = 0.2984
w_adults = 0.6047
w_old = 0.1015
list_scales = [
w_kids,
w_kids,
w_adults,
w_adults,
w_adults,
w_adults,
w_adults,
w_old,
w_old,
]
#------------------------
elif policy == 'Schools_Offices_closed':
w_kids = 0.2
w_adults = 0.5
w_old = 1
list_scales = [
w_kids,
w_kids,
w_adults,
w_adults,
w_adults,
w_adults,
w_adults,
w_old,
w_old,
]
#------------------------
elif policy == 'Adults_self_isolate':
w_adults = 0.4
list_scales = [
1,
1,
w_adults,
w_adults,
w_adults,
w_adults,
w_adults,
1,
1,
]
#------------------------
elif policy == 'Adults_Elderly_Self_isolate':
w_kids = 1
w_adults = 0.2
w_old = 0.25
list_scales = [
w_kids,
w_kids,
w_adults,
w_adults,
w_adults,
w_adults,
w_adults,
w_old,
w_old,
]
#------------------------
elif policy == 'Lockdown_but_kids':
w_kids = 1
w_adults = 0.1
w_adults = 0.1
list_scales = [
w_kids,
w_kids,
w_adults,
w_adults,
w_adults,
w_adults,
w_adults,
w_old,
w_old,
]
elif not policy == 'Uncontained':
raise ('Policy was not recognized.')
B_policy = scale_B_opt(B, list_scales)
return B_policy