def test_import2strains(do_plot=False, do_show=True, do_save=False):
sc.heading('Test introducing 2 new strains partway through a sim')
b117 = cv.strain('b117', days=1, n_imports=20)
p1 = cv.strain('sa variant', days=2, n_imports=20)
sim = cv.Sim(use_waning=True,
strains=[b117, p1],
label='With imported infections',
**base_pars)
sim.run()
return sim
def test_msim(do_plot=False):
sc.heading('Testing multisim...')
# basic test for vaccine
b117 = cv.strain('b117', days=0)
sim = cv.Sim(use_waning=True, strains=[b117], **base_pars)
msim = cv.MultiSim(sim, n_runs=2)
msim.run()
msim.reduce()
to_plot = sc.objdict({
'Total infections': ['cum_infections'],
'New infections per day': ['new_infections'],
'New Re-infections per day': ['new_reinfections'],
})
if do_plot:
msim.plot(to_plot=to_plot,
do_save=0,
do_show=1,
legend_args={'loc': 'upper left'},
axis_args={'hspace': 0.4},
interval=35)
return msim
def test_strains(do_plot=False):
sc.heading('Testing strains...')
b117 = cv.strain('b117', days=10, n_imports=20)
p1 = cv.strain('sa variant', days=20, n_imports=20)
cust = cv.strain(label='Custom',
days=40,
n_imports=20,
strain={
'rel_beta': 2,
'rel_symp_prob': 1.6
})
sim = cv.Sim(base_pars, use_waning=True, strains=[b117, p1, cust])
sim.run()
if do_plot:
sim.plot('overview-strain')
return sim
def test_vaccines(do_plot=False):
sc.heading('Testing vaccines...')
p1 = cv.strain('sa variant', days=20, n_imports=20)
pfizer = cv.vaccinate(vaccine='pfizer', days=30)
sim = cv.Sim(base_pars, use_waning=True, strains=p1, interventions=pfizer)
sim.run()
if do_plot:
sim.plot('overview-strain')
return sim
def test_import2strains_changebeta(do_plot=False, do_show=True, do_save=False):
sc.heading(
'Test introducing 2 new strains partway through a sim, with a change_beta intervention'
)
strain2 = {'rel_beta': 1.5, 'rel_severe_prob': 1.3}
strain3 = {'rel_beta': 2, 'rel_symp_prob': 1.6}
intervs = cv.change_beta(days=[5, 20, 40], changes=[0.8, 0.7, 0.6])
strains = [
cv.strain(strain=strain2, days=10, n_imports=20),
cv.strain(strain=strain3, days=30, n_imports=20),
]
sim = cv.Sim(use_waning=True,
interventions=intervs,
strains=strains,
label='With imported infections',
**base_pars)
sim.run()
return sim
def test_import1strain(do_plot=False, do_show=True, do_save=False):
sc.heading('Test introducing a new strain partway through a sim')
strain_pars = {
'rel_beta': 1.5,
}
pars = {'beta': 0.01}
strain = cv.strain(strain_pars,
days=1,
n_imports=20,
label='Strain 2: 1.5x more transmissible')
sim = cv.Sim(use_waning=True,
pars=pars,
strains=strain,
analyzers=cv.snapshot(30, 60),
**pars,
**base_pars)
sim.run()
return sim
def test_importstrain_longerdur(do_plot=False, do_show=True, do_save=False):
sc.heading(
'Test introducing a new strain with longer duration partway through a sim'
)
pars = sc.mergedicts(base_pars, {
'n_days': 120,
})
strain_pars = {
'rel_beta': 1.5,
}
strain = cv.strain(strain=strain_pars,
label='Custom strain',
days=10,
n_imports=30)
sim = cv.Sim(use_waning=True,
pars=pars,
strains=strain,
label='With imported infections')
sim.run()
return sim
def make_sim(seed,
beta,
calibration=True,
future_symp_test=None,
scenario=None,
end_day='2021-10-30',
verbose=0):
# Set the parameters
#total_pop = 67.86e6 # UK population size
total_pop = 55.98e6 # UK population size
pop_size = 100e3 # Actual simulated population
pop_scale = int(total_pop / pop_size)
pop_type = 'hybrid'
pop_infected = 1000
beta = beta
asymp_factor = 2
contacts = {'h': 3.0, 's': 20, 'w': 20, 'c': 20}
beta_layer = {'h': 3.0, 's': 1.0, 'w': 0.6, 'c': 0.3}
if end_day is None: end_day = '2021-05-05'
pars = sc.objdict(
use_waning=True,
pop_size=pop_size,
pop_infected=pop_infected,
pop_scale=pop_scale,
pop_type=pop_type,
start_day=start_day,
end_day=end_day,
beta=beta,
asymp_factor=asymp_factor,
contacts=contacts,
rescale=True,
rand_seed=seed,
verbose=verbose,
#rel_symp_prob = 1.1,
#rel_severe_prob = 0.45,
#rel_crit_prob = 1.0,
#rel_death_prob=1.15,
)
sim = cv.Sim(pars=pars, datafile=data_path, location='uk')
sim['prognoses']['sus_ORs'][0] = 1.0 # ages 20-30
sim['prognoses']['sus_ORs'][1] = 1.0 # ages 20-30
# ADD BETA INTERVENTIONS
sbv = 0.63
beta_past = sc.odict({
'2020-02-14': [1.00, 1.00, 0.90, 0.90],
'2020-03-16': [1.00, 0.90, 0.80, 0.80],
'2020-03-23': [1.29, 0.02, 0.20, 0.20],
'2020-06-01': [1.00, 0.23, 0.40, 0.40],
'2020-06-15': [1.00, 0.38, 0.50, 0.50],
'2020-07-22': [1.29, 0.00, 0.30, 0.50],
'2020-09-02': [1.25, sbv, 0.50, 0.70],
'2020-10-01': [1.25, sbv, 0.50, 0.70],
'2020-10-16': [1.25, sbv, 0.50, 0.70],
'2020-10-26': [1.00, 0.00, 0.50, 0.70],
'2020-11-05': [1.25, sbv, 0.30, 0.40],
'2020-11-14': [1.25, sbv, 0.30, 0.40],
'2020-11-21': [1.25, sbv, 0.30, 0.40],
'2020-11-30': [1.25, sbv, 0.30, 0.40],
'2020-12-03': [1.50, sbv, 0.50, 0.70],
'2020-12-20': [1.25, 0.00, 0.50, 0.70],
'2020-12-25': [1.50, 0.00, 0.20, 0.70],
'2020-12-26': [1.50, 0.00, 0.20, 0.70],
'2020-12-31': [1.50, 0.00, 0.20, 0.70],
'2021-01-01': [1.50, 0.00, 0.20, 0.70],
'2021-01-04': [1.25, 0.14, 0.30, 0.40],
'2021-01-11': [1.25, 0.14, 0.30, 0.40],
'2021-01-18': [1.25, 0.14, 0.30, 0.40],
'2021-01-18': [1.25, 0.14, 0.30, 0.40],
'2021-01-30': [1.25, 0.14, 0.30, 0.40],
'2021-02-08': [1.25, 0.14, 0.30, 0.40],
'2021-02-15': [1.25, 0.14, 0.30, 0.40],
'2021-02-22': [1.25, 0.14, 0.30, 0.40],
'2021-03-08': [1.25, sbv, 0.30, 0.50],
'2021-03-15': [1.25, sbv, 0.30, 0.50],
'2021-03-22': [1.25, sbv, 0.30, 0.50],
'2021-03-29': [1.25, 0.02, 0.30, 0.50],
'2021-04-05': [1.25, 0.02, 0.30, 0.50],
'2021-04-12': [1.25, 0.02, 0.40, 0.60],
'2021-04-19': [1.25, sbv, 0.40, 0.60],
'2021-04-26': [1.25, sbv, 0.40, 0.60],
'2021-05-03': [1.25, sbv, 0.40, 0.60],
'2021-05-10': [1.25, sbv, 0.40, 0.60],
'2021-05-17': [1.25, sbv, 0.50, 0.70],
'2021-05-21': [1.25, sbv, 0.50, 0.70],
'2021-05-28': [1.25, 0.02, 0.50, 0.70],
})
if not calibration:
##no schools until 8th March but assue 20% (1 in 5) in schools between 04/01-22/02;
##model transmission remaining at schools as 14% (to account for 30% reduction due to school measures)
## reopening schools on 8th March, society stage 1 29th March, society stage 2 12th April,
## society some more (stage 3) 17th May and everything (stage 4) 21st June 2021.
## Projecting until end of 2021.
if scenario == 'Roadmap_All':
beta_scens = sc.odict({
'2021-05-03': [1.25, sbv, 0.40, 0.60],
'2021-05-10': [1.25, sbv, 0.40, 0.60],
'2021-05-17': [1.25, sbv, 0.50, 0.70],
'2021-05-21': [1.25, sbv, 0.50, 0.70],
'2021-05-28': [1.25, 0.02, 0.50, 0.70],
'2021-06-07': [1.25, sbv, 0.50, 0.70],
'2021-06-14': [1.25, sbv, 0.50, 0.70],
'2021-06-21': [1.25, sbv, 0.70, 0.90],
'2021-06-28': [1.25, sbv, 0.70, 0.90],
'2021-07-05': [1.25, sbv, 0.70, 0.90],
'2021-07-12': [1.25, sbv, 0.70, 0.90],
'2021-07-19': [1.25, 0.00, 0.70, 0.90],
'2021-07-26': [1.25, 0.00, 0.70, 0.90],
'2021-08-02': [1.25, 0.00, 0.70, 0.90],
'2021-08-16': [1.25, 0.00, 0.70, 0.90],
'2021-09-01': [1.25, 0.63, 0.70, 0.90],
'2021-09-15': [1.25, 0.63, 0.70, 0.90],
'2021-09-29': [1.25, 0.63, 0.70, 0.90],
'2021-10-13': [1.25, 0.63, 0.70, 0.90],
'2021-10-27': [1.25, 0.02, 0.70, 0.90],
'2021-11-08': [1.25, 0.63, 0.70, 0.90],
'2021-11-23': [1.25, 0.63, 0.70, 0.90],
'2021-11-30': [1.25, 0.63, 0.70, 0.90],
'2021-12-07': [1.25, 0.63, 0.70, 0.90],
'2021-12-21': [1.25, 0.63, 0.70, 0.90],
})
## reopening schools on 8th March, society stage 1 29th March, society stage 2 12th April ONLY
## NO (stage 3) 17th May and NO stage 4 21st June 2021.
## Projecting until end of 2021.
#elif scenario == 'Roadmap_Stage2':
#beta_scens = sc.odict({'2021-04-12': [1.25, 0.02, 0.40, 0.70],
# '2021-04-19': [1.25, sbv, 0.40, 0.70],
# '2021-04-26': [1.25, sbv, 0.40, 0.70],
# '2021-05-03': [1.25, sbv, 0.40, 0.70],
# '2021-05-10': [1.25, sbv, 0.40, 0.70],
# '2021-05-17': [1.25, sbv, 0.40, 0.70],
# '2021-05-21': [1.25, sbv, 0.40, 0.70],
# '2021-05-28': [1.25, 0.02, 0.40, 0.70],
# '2021-06-07': [1.25, sbv, 0.40, 0.70],
# '2021-06-21': [1.25, sbv, 0.40, 0.70],
# '2021-06-28': [1.25, sbv, 0.40, 0.70],
# '2021-07-05': [1.25, sbv, 0.40, 0.70],
# '2021-07-12': [1.25, sbv, 0.40, 0.70],
# '2021-07-19': [1.25, 0.00, 0.40, 0.70],
# '2021-07-26': [1.25, 0.00, 0.40, 0.70],
# '2021-08-02': [1.25, 0.00, 0.40, 0.70],
# '2021-08-16': [1.25, 0.00, 0.40, 0.70],
# '2021-09-01': [1.25, 0.63, 0.70, 0.90],
# '2021-09-15': [1.25, 0.63, 0.70, 0.90],
# '2021-09-29': [1.25, 0.63, 0.70, 0.90],
# '2021-10-13': [1.25, 0.63, 0.70, 0.90],
# '2021-10-27': [1.25, 0.02, 0.70, 0.90],
# '2021-11-08': [1.25, 0.63, 0.70, 0.90],
# '2021-11-23': [1.25, 0.63, 0.70, 0.90],
# '2021-11-30': [1.25, 0.63, 0.70, 0.90],
# '2021-12-07': [1.25, 0.63, 0.70, 0.90],
# '2021-12-21': [1.25, 0.63, 0.70, 0.90],
# })
## reopening schools on 8th March, society stage 1 29th March, society stage 2 12th April,
## and society some more (stage 3) 17th May but NO stage 4 21st June 2021.
## Projecting until end of 2021.
elif scenario == 'Roadmap_Stage3':
beta_scens = sc.odict({
'2021-04-12': [1.25, 0.02, 0.40, 0.60],
'2021-04-19': [1.25, sbv, 0.40, 0.60],
'2021-04-26': [1.25, sbv, 0.40, 0.60],
'2021-05-03': [1.25, sbv, 0.40, 0.60],
'2021-05-10': [1.25, sbv, 0.40, 0.60],
'2021-05-17': [1.25, sbv, 0.50, 0.70],
'2021-05-21': [1.25, sbv, 0.50, 0.70],
'2021-05-28': [1.25, 0.02, 0.50, 0.70],
'2021-06-07': [1.25, sbv, 0.50, 0.70],
'2021-06-14': [1.25, sbv, 0.50, 0.70],
'2021-06-21': [1.25, sbv, 0.50, 0.70],
'2021-06-28': [1.25, sbv, 0.50, 0.70],
'2021-07-05': [1.25, sbv, 0.50, 0.70],
'2021-07-12': [1.25, sbv, 0.50, 0.70],
'2021-07-19': [1.25, 0.00, 0.50, 0.70],
'2021-07-26': [1.25, 0.00, 0.50, 0.70],
'2021-08-02': [1.25, 0.00, 0.50, 0.70],
'2021-08-16': [1.25, 0.00, 0.50, 0.70],
'2021-09-01': [1.25, 0.63, 0.70, 0.90],
'2021-09-15': [1.25, 0.63, 0.70, 0.90],
'2021-09-29': [1.25, 0.63, 0.70, 0.90],
'2021-10-13': [1.25, 0.63, 0.70, 0.90],
'2021-10-27': [1.25, 0.02, 0.70, 0.90],
'2021-11-08': [1.25, 0.63, 0.70, 0.90],
'2021-11-23': [1.25, 0.63, 0.70, 0.90],
'2021-11-30': [1.25, 0.63, 0.70, 0.90],
'2021-12-07': [1.25, 0.63, 0.70, 0.90],
'2021-12-21': [1.25, 0.63, 0.70, 0.90],
})
beta_dict = sc.mergedicts(beta_past, beta_scens)
else:
beta_dict = beta_past
beta_days = list(beta_dict.keys())
h_beta = cv.change_beta(days=beta_days,
changes=[c[0] for c in beta_dict.values()],
layers='h')
s_beta = cv.change_beta(days=beta_days,
changes=[c[1] for c in beta_dict.values()],
layers='s')
w_beta = cv.change_beta(days=beta_days,
changes=[c[2] for c in beta_dict.values()],
layers='w')
c_beta = cv.change_beta(days=beta_days,
changes=[c[3] for c in beta_dict.values()],
layers='c')
# Add B.1.117 strain
b117 = cv.strain('b117',
days=np.arange(sim.day('2020-09-01'),
sim.day('2020-09-10')),
n_imports=100)
sim['strains'] += [b117]
# Add B.1.1351 strain
b1351 = cv.strain('b1351',
days=np.arange(sim.day('2020-11-20'),
sim.day('2020-11-30')),
n_imports=600)
sim['strains'] += [b1351]
# Add B.X.XXX strain starting middle of April
custom_strain = cv.strain(label='custom',
strain=cvp.get_strain_pars()['p1'],
days=np.arange(sim.day('2021-03-10'),
sim.day('2021-03-10')),
n_imports=600)
sim['strains'] += [custom_strain]
# seems like we need to do this to deal with cross immunity?!
sim.init_strains()
sim.init_immunity()
sim['immunity']
prior = {'wild': 0.5, 'b117': 0.8, 'b1351': 0.8, 'custom': 0.8}
pre = {'wild': 0.5, 'b117': 0.8, 'b1351': 0.8, 'custom': 0.8}
cross_immunities = cvp.get_cross_immunity()
for k, v in sim['strain_map'].items():
if v == 'custom':
for j, j_lab in sim['strain_map'].items():
sim['immunity'][k][j] = prior[j_lab]
sim['immunity'][j][k] = pre[j_lab]
#if j != k:
# sim['immunity'][k][j] = cross_immunities['b117'][j_lab]
# sim['immunity'][j][k] = cross_immunities[j_lab]['b117']
# # Add a new change in beta to represent the takeover of the novel variant VOC B117 202012/01
# # Assume that the new variant is 60% more transmisible (https://cmmid.github.io/topics/covid19/uk-novel-variant.html,
# # Assume that between Nov 1 and Jan 30, the new variant grows from 0-100% of cases
interventions = [h_beta, w_beta, s_beta, c_beta]
# ADD TEST AND TRACE INTERVENTIONS
tc_day = sim.day(
'2020-03-16'
) #intervention of some testing (tc) starts on 16th March and we run until 1st April when it increases
te_day = sim.day(
'2020-04-01'
) #intervention of some testing (te) starts on 1st April and we run until 1st May when it increases
tt_day = sim.day(
'2020-05-01'
) #intervention of increased testing (tt) starts on 1st May
tti_day = sim.day(
'2020-06-01'
) #intervention of tracing and enhanced testing (tti) starts on 1st June
tti_day_july = sim.day(
'2020-07-01'
) #intervention of tracing and enhanced testing (tti) at different levels starts on 1st July
tti_day_august = sim.day(
'2020-08-01'
) #intervention of tracing and enhanced testing (tti) at different levels starts on 1st August
tti_day_sep = sim.day('2020-09-01')
tti_day_oct = sim.day('2020-10-01')
tti_day_nov = sim.day('2020-11-01')
tti_day_dec = sim.day('2020-12-01')
tti_day_jan = sim.day('2021-01-01')
tti_day_feb = sim.day('2021-02-01')
tti_day_march = sim.day('2021-03-08')
tti_day_april = sim.day('2021-03-01')
#start of vaccinating those 75years+
tti_day_vac1 = sim.day('2021-01-03')
#start of vaccinating 60+ old
tti_day_vac2 = sim.day('2021-02-03')
#start of vaccinating 55+ years old
tti_day_vac3 = sim.day('2021-02-28')
#start of vaccination 50+ years old
tti_day_vac4 = sim.day('2021-03-10')
#start vaccinating of 45+
tti_day_vac5 = sim.day('2021-03-30')
#start vaccinating of 40+
tti_day_vac6 = sim.day('2021-04-20')
#start vaccinating of 35+
tti_day_vac7 = sim.day('2021-05-05')
#start vaccinating of 30+
tti_day_vac8 = sim.day('2021-05-30')
#start vaccinating of 25+
tti_day_vac9 = sim.day('2021-06-10')
#start vaccinating of 18+
tti_day_vac10 = sim.day('2021-06-30')
#start vaccinating of 11-17
tti_day_vac11 = sim.day('2021-07-10')
s_prob_april = 0.009
s_prob_may = 0.012
s_prob_june = 0.02769
s_prob_july = 0.02769
s_prob_august = 0.03769
tn = 0.09
s_prob_sep = 0.08769
s_prob_oct = 0.08769
s_prob_nov = 0.08769
s_prob_dec = 0.08769
s_prob_jan = 0.08769
#0.114=70%; 0.149=80%; 0.205=90%
if future_symp_test is None: future_symp_test = s_prob_jan
t_delay = 1.0
#isolation may-july
iso_vals = [{k: 0.1 for k in 'hswc'}]
#isolation august
iso_vals1 = [{k: 0.7 for k in 'hswc'}]
#isolation september
iso_vals2 = [{k: 0.7 for k in 'hswc'}]
#isolation october
iso_vals3 = [{k: 0.7 for k in 'hswc'}]
#isolation november
iso_vals4 = [{k: 0.7 for k in 'hswc'}]
#isolation december
iso_vals5 = [{k: 0.7 for k in 'hswc'}]
#isolation January-April
#iso_vals6 = [{k:0.3 for k in 'hswc'}]
#testing and isolation intervention
interventions += [
cv.test_prob(symp_prob=0.009,
asymp_prob=0.0,
symp_quar_prob=0.0,
start_day=tc_day,
end_day=te_day - 1,
test_delay=t_delay),
cv.test_prob(symp_prob=s_prob_april,
asymp_prob=0.0,
symp_quar_prob=0.0,
start_day=te_day,
end_day=tt_day - 1,
test_delay=t_delay),
cv.test_prob(symp_prob=s_prob_may,
asymp_prob=0.00076,
symp_quar_prob=0.0,
start_day=tt_day,
end_day=tti_day - 1,
test_delay=t_delay),
cv.test_prob(symp_prob=s_prob_june,
asymp_prob=0.00076,
symp_quar_prob=0.0,
start_day=tti_day,
end_day=tti_day_july - 1,
test_delay=t_delay),
cv.test_prob(symp_prob=s_prob_july,
asymp_prob=0.00076,
symp_quar_prob=0.0,
start_day=tti_day_july,
end_day=tti_day_august - 1,
test_delay=t_delay),
cv.test_prob(symp_prob=s_prob_august,
asymp_prob=0.0028,
symp_quar_prob=0.0,
start_day=tti_day_august,
end_day=tti_day_sep - 1,
test_delay=t_delay),
cv.test_prob(symp_prob=s_prob_sep,
asymp_prob=0.0028,
symp_quar_prob=0.0,
start_day=tti_day_sep,
end_day=tti_day_oct - 1,
test_delay=t_delay),
cv.test_prob(symp_prob=s_prob_oct,
asymp_prob=0.0028,
symp_quar_prob=0.0,
start_day=tti_day_oct,
end_day=tti_day_nov - 1,
test_delay=t_delay),
cv.test_prob(symp_prob=s_prob_nov,
asymp_prob=0.0063,
symp_quar_prob=0.0,
start_day=tti_day_nov,
end_day=tti_day_dec - 1,
test_delay=t_delay),
cv.test_prob(symp_prob=s_prob_dec,
asymp_prob=0.0063,
symp_quar_prob=0.0,
start_day=tti_day_dec,
end_day=tti_day_jan - 1,
test_delay=t_delay),
cv.test_prob(symp_prob=s_prob_jan,
asymp_prob=0.0063,
symp_quar_prob=0.0,
start_day=tti_day_jan,
end_day=tti_day_feb - 1,
test_delay=t_delay),
cv.test_prob(symp_prob=s_prob_jan,
asymp_prob=0.012,
symp_quar_prob=0.0,
start_day=tti_day_feb,
end_day=tti_day_march - 1,
test_delay=t_delay),
cv.test_prob(symp_prob=s_prob_jan,
asymp_prob=0.012,
symp_quar_prob=0.0,
start_day=tti_day_march,
end_day=tti_day_april - 1,
test_delay=t_delay),
cv.test_prob(symp_prob=s_prob_jan,
asymp_prob=0.012,
symp_quar_prob=0.0,
start_day=tti_day_april,
test_delay=t_delay),
cv.contact_tracing(trace_probs={
'h': 1,
's': 0.8,
'w': 0.8,
'c': 0.05
},
trace_time={
'h': 0,
's': 1,
'w': 1,
'c': 2
},
start_day='2020-06-01',
end_day='2023-06-30',
quar_period=10),
#cv.contact_tracing(trace_probs={'h': 1, 's': 0.5, 'w': 0.5, 'c': 0.05},
# trace_time={'h': 0, 's': 1, 'w': 1, 'c': 2},
# start_day='2021-03-08',
# quar_period=5),
cv.dynamic_pars({'iso_factor': {
'days': te_day,
'vals': iso_vals
}}),
cv.dynamic_pars(
{'iso_factor':
{
'days': tti_day_august,
'vals': iso_vals1
}}),
cv.dynamic_pars(
{'iso_factor':
{
'days': tti_day_sep,
'vals': iso_vals2
}}),
cv.dynamic_pars(
{'iso_factor':
{
'days': tti_day_oct,
'vals': iso_vals3
}}),
cv.dynamic_pars(
{'iso_factor':
{
'days': tti_day_nov,
'vals': iso_vals4
}}),
cv.dynamic_pars(
{'iso_factor':
{
'days': tti_day_dec,
'vals': iso_vals5
}})
]
#cv.dynamic_pars({'rel_death_prob': {'days': tti_day_vac, 'vals': 0.9}})]
#cv.vaccine(days=[0,14], rel_sus=0.4, rel_symp=0.2, cumulative=[0.7, 0.3])]
# derived from AZ default params (3.0.2) with increased interval between doses)
# dose_pars = cvp.get_vaccine_dose_pars()['az']
# dose_pars.update({'nab_interval': 14, 'interval':7*9})
# strain_pars = cvp.get_vaccine_strain_pars()['az']
# hard code them in
dose_pars = {
'nab_eff': {
'sus': {
'slope': 1.6,
'n_50': 0.05
}
},
'nab_init': {
'dist': 'normal',
'par1': -0.85,
'par2': 2
},
'nab_boost': 3,
'doses': 2,
'interval': 7 * 12,
'nab_interval': 14
}
strain_pars = {
'wild': 1.0,
'b117': 1 / 2.3,
'b1351': 1 / 9,
'p1': 1 / 2.9,
'custom': 1 / 2.3
}
vaccine = sc.mergedicts({'label': 'az_uk'},
sc.mergedicts(dose_pars, strain_pars))
# age targeted vaccination
def subtarget_75_100(sim):
inds = cv.true(sim.people.age >= 75)
return {'inds': inds, 'vals': 0.020 * np.ones(len(inds))}
interventions += [
utils_vac.vaccinate(vaccine=vaccine,
prob=0.1,
subtarget=subtarget_75_100,
days=np.arange(sim.day('2020-12-20'),
sim.day('2023-01-01')))
]
def subtarget_60_75(sim):
inds = cv.true((sim.people.age >= 60) & (sim.people.age < 75))
return {'inds': inds, 'vals': 0.020 * np.ones(len(inds))}
interventions += [
utils_vac.vaccinate(vaccine=vaccine,
prob=0.1,
subtarget=subtarget_60_75,
days=np.arange(sim.day('2021-01-28'),
sim.day('2023-01-01')))
]
def subtarget_50_60(sim):
inds = cv.true((sim.people.age >= 50) & (sim.people.age < 60))
return {'inds': inds, 'vals': 0.010 * np.ones(len(inds))}
interventions += [
utils_vac.vaccinate(vaccine=vaccine,
prob=0.1,
subtarget=subtarget_50_60,
days=np.arange(sim.day('2021-02-10'),
sim.day('2023-01-01')))
]
def subtarget_40_50(sim):
inds = cv.true((sim.people.age >= 40) & (sim.people.age < 50))
return {'inds': inds, 'vals': 0.005 * np.ones(len(inds))}
interventions += [
utils_vac.vaccinate(vaccine=vaccine,
prob=0.1,
subtarget=subtarget_40_50,
days=np.arange(sim.day('2021-04-10'),
sim.day('2023-01-01')))
]
def subtarget_30_40(sim):
inds = cv.true((sim.people.age >= 30) & (sim.people.age < 40))
return {'inds': inds, 'vals': 0.003 * np.ones(len(inds))}
interventions += [
utils_vac.vaccinate(vaccine=vaccine,
prob=0.1,
subtarget=subtarget_30_40,
days=np.arange(sim.day('2021-05-10'),
sim.day('2023-01-01')))
]
def subtarget_18_30(sim):
inds = cv.true((sim.people.age >= 18) & (sim.people.age < 30))
return {'inds': inds, 'vals': 0.003 * np.ones(len(inds))}
interventions += [
utils_vac.vaccinate(vaccine=vaccine,
prob=0.01,
subtarget=subtarget_18_30,
days=np.arange(sim.day('2021-06-10'),
sim.day('2023-01-01')))
]
analyzers = []
#analyzers += [cv.age_histogram(datafile='uk_stats_by_age.xlsx', edges=np.concatenate([np.linspace(0, 90, 19),np.array([100])]))]
# Finally, update the parameters
sim.update_pars(interventions=interventions, analyzers=analyzers)
# Change death and critical probabilities
# interventions += [cv.dynamic_pars({'rel_death_prob':{'days':sim.day('2020-07-01'), 'vals':0.6}})]
# Finally, update the parameters
#sim.update_pars(interventions=interventions)
for intervention in sim['interventions']:
intervention.do_plot = False
sim.initialize()
return sim
import covasim as cv
import numpy as np
import covasim.parameters as cvp
pars = {'use_waning': True, 'pop_infected': 100}
sim = cv.Sim(pars=pars)
#custom_strain = cv.strain(label='custom', strain=cvp.get_strain_pars()['b117'],
# days=np.arange(20, 21), n_imports=40)
#sim['strains'] += [custom_strain]
b117 = cv.strain('b117', days=np.arange(sim.day('2020-03-01'), sim.day('2020-03-10')), n_imports=100)
#im['strains'] += [b117]
# Add B.1.1351 strain
b1351 = cv.strain('b1351', days=np.arange(sim.day('2020-04-05'), sim.day('2020-04-20')), n_imports=200)
#sim['strains'] += [b1351]
custom_strain = cv.strain(label='custom', strain = cvp.get_strain_pars()['b1351'],
days=np.arange(sim.day('2020-03-15'), sim.day('2020-03-30')), n_imports=40)
sim['strains'] += [b117, b1351, custom_strain]
sim.init_strains()
sim.init_immunity()
sim['immunity']
pre = {'wild': 0.0, 'b117': 0.0, 'b1351': 0.0, 'custom':1.0}
prior = {'wild': 0.0, 'b117': 0.0, 'b1351': 0.0, 'custom': 1.0}
cross_immunities = cvp.get_cross_immunity()
for k, v in sim['strain_map'].items():
if v == 'custom':
for j, j_lab in sim['strain_map'].items():
sim['immunity'][k][j] = prior[j_lab]
sim['immunity'][j][k] = pre[j_lab]
sim.run()
sim.plot('strains', do_save=True, do_show=False, fig_path=f'uk_strain.png')
def test_varyingimmunity(do_plot=False, do_show=True, do_save=False):
sc.heading('Test varying properties of immunity')
# Define baseline parameters
n_runs = 3
base_sim = cv.Sim(use_waning=True, n_days=400, pars=base_pars)
# Define the scenarios
b1351 = cv.strain('b1351', days=100, n_imports=20)
scenarios = {
'baseline': {
'name': 'Default Immunity (decay at log(2)/90)',
'pars': {
'nab_decay':
dict(form='nab_decay',
decay_rate1=np.log(2) / 90,
decay_time1=250,
decay_rate2=0.001),
},
},
'faster_immunity': {
'name': 'Faster Immunity (decay at log(2)/30)',
'pars': {
'nab_decay':
dict(form='nab_decay',
decay_rate1=np.log(2) / 30,
decay_time1=250,
decay_rate2=0.001),
},
},
'baseline_b1351': {
'name': 'Default Immunity (decay at log(2)/90), B1351 on day 100',
'pars': {
'nab_decay':
dict(form='nab_decay',
decay_rate1=np.log(2) / 90,
decay_time1=250,
decay_rate2=0.001),
'strains': [b1351],
},
},
'faster_immunity_b1351': {
'name': 'Faster Immunity (decay at log(2)/30), B1351 on day 100',
'pars': {
'nab_decay':
dict(form='nab_decay',
decay_rate1=np.log(2) / 30,
decay_time1=250,
decay_rate2=0.001),
'strains': [b1351],
},
},
}
metapars = {'n_runs': n_runs}
scens = cv.Scenarios(sim=base_sim, metapars=metapars, scenarios=scenarios)
scens.run(debug=debug)
to_plot = sc.objdict({
'New infections': ['new_infections'],
'New re-infections': ['new_reinfections'],
'Population Nabs': ['pop_nabs'],
'Population Immunity': ['pop_protection'],
})
if do_plot:
scens.plot(do_save=do_save,
do_show=do_show,
fig_path='results/test_basic_immunity.png',
to_plot=to_plot)
return scens
def test_vaccine_2strains_scen(do_plot=False, do_show=True, do_save=False):
sc.heading(
'Run a basic sim with b117 strain on day 10, pfizer vaccine day 20')
# Define baseline parameters
n_runs = 3
base_sim = cv.Sim(use_waning=True, pars=base_pars)
# Vaccinate 75+, then 65+, then 50+, then 18+ on days 20, 40, 60, 80
base_sim.vxsubtarg = sc.objdict()
base_sim.vxsubtarg.age = [75, 65, 50, 18]
base_sim.vxsubtarg.prob = [.01, .01, .01, .01]
base_sim.vxsubtarg.days = subtarg_days = [60, 150, 200, 220]
jnj = cv.vaccinate(days=subtarg_days,
vaccine='j&j',
subtarget=vacc_subtarg)
b1351 = cv.strain('b1351', days=10, n_imports=20)
p1 = cv.strain('p1', days=100, n_imports=100)
# Define the scenarios
scenarios = {
'baseline': {
'name': 'B1351 on day 10, No Vaccine',
'pars': {
'strains': [b1351]
}
},
'b1351': {
'name': 'B1351 on day 10, J&J starting on day 60',
'pars': {
'interventions': [jnj],
'strains': [b1351],
}
},
'p1': {
'name': 'B1351 on day 10, J&J starting on day 60, p1 on day 100',
'pars': {
'interventions': [jnj],
'strains': [b1351, p1],
}
},
}
metapars = {'n_runs': n_runs}
scens = cv.Scenarios(sim=base_sim, metapars=metapars, scenarios=scenarios)
scens.run(debug=debug)
to_plot = sc.objdict({
'New infections': ['new_infections'],
'Cumulative infections': ['cum_infections'],
'New reinfections': ['new_reinfections'],
# 'Cumulative reinfections': ['cum_reinfections'],
})
if do_plot:
scens.plot(do_save=do_save,
do_show=do_show,
fig_path='results/test_vaccine_b1351.png',
to_plot=to_plot)
return scens