def create_sim(x):
beta = x[0]
pop_infected = x[1]
s_prob_sep = x[2]
s_prob_oct = x[3]
s_prob_nov = x[4]
iso_vals1 = x[5]
iso_vals2 = x[6]
iso_vals3 = x[7]
iso_vals4 = x[8]
start_day = '2020-01-21'
end_day = '2020-12-07'
data_path = 'UK_Covid_cases_december6.xlsx'
# Set the parameters
total_pop = 67.86e6 # UK population size
pop_size = 100e3 # Actual simulated population
pop_scale = int(total_pop / pop_size)
pop_type = 'hybrid'
asymp_factor = 2
contacts = {'h': 3.0, 's': 20, 'w': 20, 'c': 20}
pars = sc.objdict(
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,
verbose=0.1,
)
# Create the baseline simulation
sim = cv.Sim(pars=pars, datafile=data_path, location='uk')
sim['prognoses']['sus_ORs'][0] = 1 # ages 0-10
sim['prognoses']['sus_ORs'][1] = 1 # ages 10-20
sim['rel_severe_prob'] = 1.0
sim['rel_crit_prob'] = 1.0 # Scale factor for proportion of severe cases that become critical
sim['rel_death_prob'] = 1.0 # Scale factor for proportion of critical cases that result in death
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') #some schools reopen 1st June and start of enhanced TTI
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')
ti_day = sim.day('2020-12-07')
beta_days = [
'2020-02-14', '2020-03-16', '2020-03-23', '2020-04-30', '2020-05-15',
'2020-06-01', '2020-06-15', '2020-07-22', '2020-08-01', '2020-09-02',
'2020-10-01', '2020-10-16', '2020-10-26', '2020-11-01', '2020-11-05',
'2020-11-14', '2020-11-21', '2020-11-30', '2020-12-03'
]
h_beta_changes = [
1.00, 1.00, 1.29, 1.29, 1.29, 1.00, 1.00, 1.29, 1.29, 1.29, 1.29, 1.29,
1.29, 1.29, 1.50, 1.50, 1.50, 1.29, 1.29
]
s_beta_changes = [
1.00, 0.90, 0.02, 0.02, 0.02, 0.23, 0.38, 0.00, 0.00, 0.63, 0.63, 0.63,
0.00, 0.63, 0.63, 0.63, 0.63, 0.63, 0.63
]
#w_beta_changes =[14feb16march23marh30apr 5may 1june15june22july 1aug 2sep 1octoct16oct 26oct 1nov 5nov 14nov 21nov 30nov dec03
w_beta_changes = [
0.90, 0.80, 0.20, 0.20, 0.20, 0.30, 0.30, 0.40, 0.40, 0.50, 0.50, 0.50,
0.50, 0.50, 0.40, 0.40, 0.40, 0.40, 0.60
]
c_beta_changes = [
0.90, 0.80, 0.20, 0.20, 0.20, 0.30, 0.30, 0.40, 0.40, 0.60, 0.60, 0.60,
0.60, 0.60, 0.40, 0.40, 0.40, 0.40, 0.70
]
# Define the beta changes
h_beta = cv.change_beta(days=beta_days, changes=h_beta_changes, layers='h')
s_beta = cv.change_beta(days=beta_days, changes=s_beta_changes, layers='s')
w_beta = cv.change_beta(days=beta_days, changes=w_beta_changes, layers='w')
c_beta = cv.change_beta(days=beta_days, changes=c_beta_changes, layers='c')
#next line to save the intervention
interventions = [h_beta, w_beta, s_beta, c_beta]
s_prob_march = 0.012
s_prob_april = 0.017
s_prob_may = 0.02769
s_prob_june = 0.02769
s_prob_july = 0.02769
s_prob_august = 0.02769
s_prob_sept = 0.05769
s_prob_oct = 0.05769
s_prob_nov = 0.05769
t_delay = 1.0
#isolation until august
iso_vals = [{k: 0.1 for k in 'hswc'}]
#isolation in august
iso_vals1 = [{k: 0.3 for k in 'hswc'}]
#isolation in september
iso_vals2 = [{k: 0.4 for k in 'hswc'}]
#isolation in october
iso_vals3 = [{k: 0.7 for k in 'hswc'}]
#isolation in november
iso_vals4 = [{k: 0.7 for k in 'hswc'}]
#tracing level at 42.35% in June; 47.22% in July, 44.4% in August and 49.6% in Septembre (until 16th Sep)
t_eff_june = 0.42
t_eff_july = 0.47
t_eff_august = 0.44
t_eff_sep = 0.50
t_eff_oct = 0.50
t_eff_nov = 0.60
t_probs_june = {k: t_eff_june for k in 'hwsc'}
t_probs_july = {k: t_eff_july for k in 'hwsc'}
t_probs_august = {k: t_eff_august for k in 'hwsc'}
t_probs_sep = {k: t_eff_sep for k in 'hwsc'}
t_probs_oct = {k: t_eff_oct for k in 'hwsc'}
t_probs_nov = {k: t_eff_nov for k in 'hwsc'}
trace_d_1 = {'h': 0, 's': 1, 'w': 1, 'c': 2}
#testing and isolation intervention
interventions += [
cv.test_prob(symp_prob=0.009,
asymp_prob=0.0,
symp_quar_prob=0.0,
asymp_quar_prob=0.0,
start_day=tc_day,
end_day=te_day - 1,
test_delay=t_delay,
test_sensitivity=0.97),
cv.test_prob(symp_prob=s_prob_april,
asymp_prob=0.0,
symp_quar_prob=0.0,
asymp_quar_prob=0.0,
start_day=te_day,
end_day=tt_day - 1,
test_delay=t_delay,
test_sensitivity=0.97),
cv.test_prob(symp_prob=s_prob_may,
asymp_prob=0.0075,
symp_quar_prob=0.0,
asymp_quar_prob=0.0,
start_day=tt_day,
end_day=tti_day - 1,
test_delay=t_delay,
test_sensitivity=0.97),
cv.test_prob(symp_prob=s_prob_june,
asymp_prob=0.0075,
symp_quar_prob=0.0,
asymp_quar_prob=0.0,
start_day=tti_day,
end_day=tti_day_july - 1,
test_delay=t_delay,
test_sensitivity=0.97),
cv.test_prob(symp_prob=s_prob_july,
asymp_prob=0.0075,
symp_quar_prob=0.0,
asymp_quar_prob=0.0,
start_day=tti_day_july,
end_day=tti_day_august - 1,
test_delay=t_delay,
test_sensitivity=0.97),
cv.test_prob(symp_prob=s_prob_august,
asymp_prob=0.0075,
symp_quar_prob=0.0,
asymp_quar_prob=0.0,
start_day=tti_day_august,
end_day=tti_day_sep - 1,
test_delay=t_delay,
test_sensitivity=0.97),
cv.test_prob(symp_prob=s_prob_sept,
asymp_prob=0.0175,
symp_quar_prob=0.0,
asymp_quar_prob=0.0,
start_day=tti_day_sep,
end_day=tti_day_oct - 1,
test_delay=t_delay,
test_sensitivity=0.97),
cv.test_prob(symp_prob=s_prob_oct,
asymp_prob=0.0175,
symp_quar_prob=0.0,
asymp_quar_prob=0.0,
start_day=tti_day_oct,
end_day=tti_day_nov - 1,
test_delay=t_delay,
test_sensitivity=0.97),
cv.test_prob(symp_prob=s_prob_nov,
asymp_prob=0.0275,
symp_quar_prob=0.0,
asymp_quar_prob=0.0,
start_day=tti_day_nov,
test_delay=t_delay,
test_sensitivity=0.97),
cv.dynamic_pars({'iso_factor': {
'days': te_day,
'vals': iso_vals
}}),
cv.contact_tracing(trace_probs=t_probs_june,
trace_time=trace_d_1,
start_day=tti_day,
end_day=tti_day_july - 1),
cv.contact_tracing(trace_probs=t_probs_july,
trace_time=trace_d_1,
start_day=tti_day_july,
end_day=tti_day_august - 1),
cv.contact_tracing(trace_probs=t_probs_august,
trace_time=trace_d_1,
start_day=tti_day_august,
end_day=tti_day_sep - 1),
cv.contact_tracing(trace_probs=t_probs_sep,
trace_time=trace_d_1,
start_day=tti_day_sep,
end_day=tti_day_oct - 1),
cv.contact_tracing(trace_probs=t_probs_oct,
trace_time=trace_d_1,
start_day=tti_day_oct,
end_day=tti_day_nov - 1),
cv.contact_tracing(trace_probs=t_probs_nov,
trace_time=trace_d_1,
start_day=tti_day_nov),
cv.dynamic_pars({'iso_factor': {
'days': tti_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({'rel_death_prob': {'days': tti_day_august, 'vals': 2.0}}),
#cv.dynamic_pars({'rel_death_prob': {'days': [tti_day_july, tti_day_sep], 'vals': [0.5, 0.5]}}),
]
sim.update_pars(interventions=interventions)
for intervention in sim['interventions']:
intervention.do_plot = False
return sim
test_delay=t_delay),
cv.test_prob(symp_prob=s_prob_july,
asymp_prob=0.00075,
symp_quar_prob=0.0,
asymp_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=0.00075,
symp_quar_prob=0.0,
asymp_quar_prob=0.0,
start_day=tti_day_august,
test_delay=t_delay),
cv.dynamic_pars({'iso_factor': {
'days': te_day,
'vals': iso_vals
}}),
cv.contact_tracing(trace_probs=t_probs_june,
trace_time=trace_d_1,
start_day=tti_day,
end_day=tti_day_july - 1),
cv.contact_tracing(trace_probs=t_probs_july,
trace_time=trace_d_1,
start_day=tti_day_july),
]
elif tti_scen == 'optimal_masks15':
# Tracing and enhanced testing strategy of symptimatics from 1st June with tracing at 40%
#testing in June
s_prob_march = 0.012
def make_ints(make_future_ints=True, mask_uptake=None, venue_trace_prob=None, future_test_prob=None, mask_eff=0.3):
# Make historical interventions
initresponse = '2020-03-15'
lockdown = '2020-03-23'
reopen1 = '2020-05-01' # Two adults allowed to visit a house
reopen2 = '2020-05-15' # Up to 5 adults can visit a house; food service and non-essential retail start to resume
reopen3 = '2020-06-01' # Pubs and regional travel open, plus more social activities
reopen4 = '2020-07-01' # large events, cinemas, museums, open; fewer restrictions on cafes/pubs/etc,
school_dates = ['2020-05-11', '2020-05-18', '2020-05-25']
comm_beta_aug = 0.7
ints = [cv.clip_edges(days=[initresponse,lockdown]+school_dates, changes=[0.75, 0.05, 0.8, 0.9, 1.0], layers=['S'], do_plot=False),
cv.clip_edges(days=[lockdown, reopen2, reopen3, reopen4], changes=[0.5, 0.65, 0.75, 0.85], layers=['W'], do_plot=False),
cv.clip_edges(days=[lockdown, reopen2, reopen4], changes=[0, 0.5, 1.0], layers=['pSport'], do_plot=False),
cv.clip_edges(days=[lockdown, '2020-06-22'], changes=[0, 1.0], layers=['cSport'], do_plot=False),
cv.change_beta(days=[lockdown, reopen2, reopen4], changes=[1.2, 1.1, 1.], layers=['H'], do_plot=True),
cv.change_beta(days=[lockdown, reopen2], changes=[0, comm_beta_aug], layers=['church'], do_plot=False),
cv.change_beta(days=[lockdown, reopen1, reopen2, reopen3, reopen4], changes=[0.0, 0.3, 0.4, 0.5, comm_beta_aug], layers=['social'], do_plot=False),
# Dynamic layers ['C', 'entertainment', 'cafe_restaurant', 'pub_bar', 'transport', 'public_parks', 'large_events']
cv.change_beta(days=[lockdown], changes=[comm_beta_aug], layers=['C'], do_plot=True),
cv.change_beta(days=[lockdown, reopen4], changes=[0, comm_beta_aug], layers=['entertainment'], do_plot=False),
cv.change_beta(days=[lockdown, reopen2], changes=[0, comm_beta_aug], layers=['cafe_restaurant'], do_plot=False),
cv.change_beta(days=[lockdown, reopen3, reopen4], changes=[0, 0.5, comm_beta_aug], layers=['pub_bar'], do_plot=False),
cv.change_beta(days=[lockdown, reopen2, reopen4], changes=[0.2, 0.3, comm_beta_aug], layers=['transport'], do_plot=False),
cv.change_beta(days=[lockdown, reopen2, reopen4], changes=[0.4, 0.5, comm_beta_aug], layers=['public_parks'], do_plot=False),
cv.change_beta(days=[lockdown, reopen4], changes=[0.0, comm_beta_aug], layers=['large_events'], do_plot=False),
]
# Approximate a mask intervention by changing beta in all layers where people would wear masks - assuming not in schools, sport, social gatherings, or home
mask_uptake_aug = 0.15
mask_uptake_sep = 0.3
mask_beta_change_aug = (1-mask_uptake_aug)*comm_beta_aug + mask_uptake_aug*mask_eff*comm_beta_aug
mask_beta_change_sep = (1-mask_uptake_sep)*comm_beta_aug + mask_uptake_sep*mask_eff*comm_beta_aug
ints += [cv.change_beta(days=['2020-08-01', '2020-08-31'] * 8, changes=[mask_beta_change_aug, 0.7] * 8,
layers=['church', 'C', 'entertainment', 'cafe_restaurant', 'pub_bar', 'transport',
'public_parks', 'large_events']),
cv.change_beta(days=['2020-09-01', today] * 8, changes=[mask_beta_change_sep, 0.7] * 8,
layers=['church', 'C', 'entertainment', 'cafe_restaurant', 'pub_bar', 'transport',
'public_parks', 'large_events'])
]
if make_future_ints:
ints += [cv.change_beta(days=[today] * 8, changes=[(1 - mask_uptake) * comm_beta_aug + mask_uptake * mask_eff * comm_beta_aug] * 8,
layers=['church', 'C', 'entertainment', 'cafe_restaurant', 'pub_bar', 'transport',
'public_parks', 'large_events'])]
# Testing
symp_prob_prelockdown = 0.04 # Limited testing pre lockdown
symp_prob_lockdown = 0.07 # 0.065 #Increased testing during lockdown
symp_prob_postlockdown = 0.19 # 0.165 # Testing since lockdown
asymp_quar_prob_postlockdown = (1-(1-symp_prob_postlockdown)**10)
future_asymp_test_prob = (1-(1-future_test_prob)**10)/2
ints += [cv.test_prob(start_day=0, end_day=lockdown, symp_prob=symp_prob_prelockdown, asymp_quar_prob=0.01, do_plot=False),
cv.test_prob(start_day=lockdown, end_day=reopen2, symp_prob=symp_prob_lockdown, asymp_quar_prob=0.01,do_plot=False),
cv.test_prob(start_day=reopen2, end_day=today, symp_prob=symp_prob_postlockdown, asymp_quar_prob=asymp_quar_prob_postlockdown,do_plot=True)]
if make_future_ints:
ints += [cv.test_prob(start_day=tomorrow, symp_prob=future_test_prob, asymp_quar_prob=future_asymp_test_prob, do_plot=True)]
# Tracing
trace_probs = {'H': 1, 'S': 0.95, # Home and school
'W': 0.8, 'pSport': 0.8, 'cSport': 0.8, 'social': 0.8, # Work and social
'C': 0.05, 'public_parks': 0.05, # Non-venue-based
'church': 0.5, 'entertainment': 0.5, 'cafe_restaurant': 0.5, 'pub_bar': 0.5, 'large_events': 0.5, # Venue-based
'transport': 0.1, # Transport
}
trace_time = {'H': 0, 'S': 0.5, # Home and school
'W': 1, 'pSport': 1, 'cSport': 1, 'social': 1, # Work and social: [0.29, 0.59, 0.74, 0.78, 0.80, 0.80, 0.80, 0.80]
'C': 2, 'public_parks': 2, # Non-venue-based: [0.0068, 0.0203, 0.0338, 0.0429, 0.0474, 0.0492, 0.0498, 0.0499]
'church': 1, 'entertainment': 1, 'cafe_restaurant': 1, 'pub_bar': 1, 'large_events': 1, # Venue-based: [0.068, 0.203, 0.338, 0.429, 0.474, 0.492, 0.498, 0.499]
'transport': 1, # Transport: [0.014, 0.041, 0.068, 0.086, 0.095, 0.098, 0.100, 0.100]
}
trace_time_f = {'H': 0, 'S': 0.5, # Home and school
'W': 1, 'pSport': 1, 'cSport': 1, 'social': 1, # Work and social: [0.29, 0.59, 0.74, 0.78, 0.80, 0.80, 0.80, 0.80]
'C': 2, 'public_parks': 2, # Non-venue-based: [0.0068, 0.0203, 0.0338, 0.0429, 0.0474, 0.0492, 0.0498, 0.0499]
'church': 1, 'entertainment': 1, 'cafe_restaurant': 1, 'pub_bar': 1, 'large_events': 1, # Venue-based: [0.068, 0.203, 0.338, 0.429, 0.474, 0.492, 0.498, 0.499]
'transport': 2, # Transport: [0.014, 0.041, 0.068, 0.086, 0.095, 0.098, 0.100, 0.100]
}
ints += [cv.contact_tracing(trace_probs=trace_probs, trace_time=trace_time, distribute_times=True, start_day=0, end_day=today, do_plot=False)]
if make_future_ints:
ints += [cv.contact_tracing(trace_probs={'H': 1, 'S': 0.95, 'W': 0.8, 'pSport': 0.8, 'cSport': 0.8, 'social': 0.8,
'C': 0.05, 'public_parks': 0.05,
'church': venue_trace_prob, 'entertainment': venue_trace_prob, 'cafe_restaurant': venue_trace_prob, 'pub_bar': venue_trace_prob, 'large_events': venue_trace_prob, 'transport': 0.1},
trace_time=trace_time_f, distribute_times=True, start_day=tomorrow, do_plot=False)]
# Close borders, then open them again to account for Victorian imports and leaky quarantine
ints += [cv.dynamic_pars({'n_imports': {'days': [14, 112, 116], 'vals': [0, 8, 0]}}, do_plot=False)]
return ints
def create_sim(seed):
beta = 0.014 #0.011
pop_infected = 10 # initial cases of infection
start_day = '2020-02-01'
#end_day = '2020-11-30'
#end_day = '2021-03-31'
end_day = '2021-06-30'
data_file = df
# Set the parameters
total_pop = 5.8e6 # Denmark population size
pop_size = 100e3 # Actual simulated population
pop_scale = int(total_pop / pop_size)
pop_type = 'hybrid'
asymp_factor = 2
pars = sc.objdict(
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,
rescale=True,
verbose=0.1,
rand_seed=seed,
)
# Create the baseline simulation
sim = cv.Sim(pars=pars,
datafile=data_file,
location='denmark',
analyzers=cv.age_histogram(
edges=[0, 16, 65, 130],
states=['exposed', 'symptomatic',
'dead'])) #edges = [0,16,65,130],
relative_death = cv.dynamic_pars(rel_death_prob=dict(days=[
sim.day('2020-02-01'),
sim.day('2020-06-09'),
sim.day('2020-09-17')
],
vals=[1.3, 0.8, 0.4]))
interventions = [relative_death]
### Change beta ###
beta_days = [
'2020-03-15', '2020-04-15', '2020-05-10', '2020-06-22', '2020-07-20',
'2020-08-22', '2020-09-01', '2020-09-22', '2020-10-01', '2020-10-15',
'2020-11-01', '2020-11-20', '2020-11-30', '2020-12-14', '2021-01-01',
'2021-03-01', '2021-04-06'
]
h_beta_changes = [
1.10, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.10, 1.10, 1.10, 1.20,
1.30, 1.20, 1.10, 1.00, 1.00
]
s_beta_changes = [
0.80, 0.50, 0.50, 0.40, 0.60, 0.60, 1.00, 0.80, 0.80, 1.00, 0.80, 1.00,
1.20, 1.00, 0.80, 0.70, 0.60
]
w_beta_changes = [
0.80, 0.50, 0.50, 0.40, 0.60, 0.60, 1.00, 0.80, 0.80, 1.00, 0.80, 1.00,
1.20, 1.00, 0.80, 0.70, 0.50
]
c_beta_changes = [
0.90, 0.60, 0.50, 0.70, 0.90, 0.80, 1.00, 0.70, 0.70, 1.00, 0.80, 1.10,
1.30, 1.10, 1.00, 0.80, 0.70
]
# Define the beta changes
h_beta = cv.change_beta(days=beta_days, changes=h_beta_changes, layers='h')
s_beta = cv.change_beta(days=beta_days, changes=s_beta_changes, layers='s')
w_beta = cv.change_beta(days=beta_days, changes=w_beta_changes, layers='w')
c_beta = cv.change_beta(days=beta_days, changes=c_beta_changes, layers='c')
### edge clipping ###
clip_days = [
'2020-03-15', '2020-04-15', '2020-05-10', '2020-06-08', '2020-06-22',
'2020-08-17', '2020-09-01', '2020-09-15', '2020-10-05', '2020-11-05',
'2020-11-20', '2020-12-09', '2020-12-19', '2020-12-25', '2021-01-04',
'2021-02-01', '2021-03-01', '2021-04-06'
]
s_clip_changes = [
0.01, 0.20, 0.40, 0.70, 0.05, 0.10, 0.90, 0.80, 0.70, 0.70, 0.70, 0.40,
0.05, 0.05, 0.05, 0.30, 0.50, 0.80
]
w_clip_changes = [
0.10, 0.30, 0.50, 0.70, 0.60, 0.80, 1.00, 0.80, 0.70, 0.70, 0.70, 0.60,
0.40, 0.10, 0.50, 0.60, 0.60, 0.80
]
c_clip_changes = [
0.20, 0.40, 0.60, 0.85, 1.00, 1.00, 1.00, 0.80, 0.80, 0.90, 0.90, 0.70,
0.80, 0.50, 0.60, 0.70, 0.80, 0.90
]
# Define the edge clipping
s_clip = cv.clip_edges(days=clip_days, changes=s_clip_changes, layers='s')
w_clip = cv.clip_edges(days=clip_days, changes=w_clip_changes, layers='w')
c_clip = cv.clip_edges(days=clip_days, changes=c_clip_changes, layers='c')
interventions += [h_beta, w_beta, s_beta, c_beta, w_clip, s_clip, c_clip]
# Add a new change in beta to represent the takeover of the new variant B.1.1.7
nv_days = np.linspace(sim.day('2020-12-14'), sim.day('2021-03-28'), 15 * 7)
nv_prop = 0.952 / (1 + np.exp(-0.099 *
(nv_days - sim.day('2020-12-14') - 59)))
nv_change = nv_prop * 1.5 + (1 - nv_prop) * 1.0 #r = 1.5
nv_beta = cv.change_beta(days=nv_days, changes=nv_change)
c = np.r_[0.8 * np.ones(sim.day('2021-02-13') - sim.day('2020-12-14')),
0.4 * np.ones(sim.day('2021-03-29') - sim.day('2021-02-13'))]
relative_severe = cv.dynamic_pars(rel_severe_prob=dict(
days=nv_days, vals=nv_prop * 1.2 + (1 - nv_prop) * 1))
relative_critical = cv.dynamic_pars(rel_crit_prob=dict(
days=nv_days, vals=nv_prop * 1.2 + (1 - nv_prop) * 1))
relative_death_nv = cv.dynamic_pars(rel_death_prob=dict(
days=nv_days, vals=nv_prop * c * 1.2 + (1 - nv_prop) * c))
interventions += [
nv_beta, relative_severe, relative_critical, relative_death_nv
]
# import infections from 2020-02-20 to 2020-03-01
imports1 = cv.dynamic_pars(n_imports=dict(days=[25, 35], vals=[2, 0]))
imports2 = cv.dynamic_pars(n_imports=dict(days=[171, 190], vals=[2, 0]))
interventions += [imports1, imports2]
iso_vals = [{
'h': 0.5,
's': 0.05,
'w': 0.05,
'c': 0.1
}] #dict(h=0.5, s=0.05, w=0.05, c=0.1)
interventions += [
cv.dynamic_pars(
{'iso_factor': {
'days': sim.day('2020-03-15'),
'vals': iso_vals
}})
]
iso_vals2 = [{'h': 0.7, 's': 0.1, 'w': 0.1, 'c': 0.3}]
interventions += [
cv.dynamic_pars(
{'iso_factor': {
'days': sim.day('2021-05-01'),
'vals': iso_vals2
}})
]
# From May 12, starting tracing and isolation strategy
tracing_prob = dict(h=1.0, s=0.5, w=0.5, c=0.2)
trace_time = {'h': 0, 's': 1, 'w': 1, 'c': 2}
interventions += [
cv.contact_tracing(trace_probs=tracing_prob,
trace_time=trace_time,
start_day='2020-05-12')
]
interventions += [
cv.test_num(daily_tests=sim.data['new_tests'],
start_day=0,
end_day=sim.day('2021-03-31'),
test_delay=1,
symp_test=50,
sensitivity=0.97,
subtarget=prior_test)
]
vaccine1 = vaccine_plan(daily1,
start_day='2021-01-06',
end_day='2021-01-24',
delay=22,
rel_symp=0.5,
rel_sus=0.2,
subtarget=vaccinate_by_age)
vaccine2 = vaccine_plan(daily2,
start_day='2021-01-25',
end_day='2021-02-15',
delay=27,
rel_symp=0.5,
rel_sus=0.2,
subtarget=vaccinate_by_age)
vaccine3 = vaccine_plan(daily3,
start_day='2021-02-16',
end_day='2021-03-07',
delay=24,
rel_symp=0.5,
rel_sus=0.2,
subtarget=vaccinate_by_age)
vaccine4 = vaccine_plan(daily4,
start_day='2021-03-08',
end_day='2021-04-10',
delay=37,
rel_symp=0.5,
rel_sus=0.2,
subtarget=vaccinate_by_age)
vaccine5 = vaccine_plan(vn,
start_day='2021-04-11',
end_day='2021-06-30',
delay=37,
rel_symp=0.5,
rel_sus=0.2,
subtarget=vaccinate_by_age)
interventions += [
cv.test_num(daily_tests=t,
start_day=sim.day('2021-04-01'),
end_day=sim.day('2021-06-30'),
test_delay=1,
symp_test=50,
sensitivity=0.97,
subtarget=prior_test)
]
interventions += [vaccine1, vaccine2, vaccine3, vaccine4, vaccine5]
sim.update_pars(interventions=interventions)
for intervention in sim['interventions']:
intervention.do_plot = False
sim.initialize()
return sim
def make_sim(seed,
beta,
calibration=True,
scenario=None,
delta_beta=1.6,
future_symp_test=None,
end_day=None,
verbose=0):
# Set the parameters
total_pop = 67.86e6 # UK population size
pop_size = 100e3 # Actual simulated population
pop_scale = int(total_pop / pop_size)
pop_type = 'hybrid'
pop_infected = 1500
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 = '2021-04-10'
pars = sc.objdict(
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_severe_prob=0.4,
rel_crit_prob=2.3,
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, 0.90],
'2020-12-26': [1.50, 0.00, 0.20, 0.90],
'2020-12-31': [1.50, 0.00, 0.20, 0.90],
'2021-01-01': [1.50, 0.00, 0.20, 0.90],
'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]
})
if not calibration:
##no schools until 1st 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)
if scenario == 'fullSchools':
beta_scens = sc.odict({
'2021-03-08': [1.25, sbv, 0.30, 0.40],
'2021-03-15': [1.25, sbv, 0.30, 0.40],
'2021-03-22': [1.25, sbv, 0.30, 0.40],
'2021-03-29': [1.25, 0.02, 0.30, 0.50],
'2021-04-01': [1.25, 0.02, 0.30, 0.50],
'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],
'2021-05-14': [1.25, sbv, 0.40, 0.70],
'2021-05-28': [1.25, sbv, 0.40, 0.70],
'2021-06-10': [1.25, sbv, 0.40, 0.70],
'2021-06-21': [1.25, sbv, 0.40, 0.80],
'2021-06-28': [1.25, sbv, 0.50, 0.80],
'2021-07-05': [1.25, sbv, 0.50, 0.80],
'2021-07-12': [1.25, sbv, 0.50, 0.80],
'2021-07-19': [1.25, 0.00, 0.50, 0.80],
'2021-07-26': [1.25, 0.00, 0.50, 0.80],
'2021-08-02': [1.25, 0.00, 0.50, 0.80],
'2021-08-16': [1.25, 0.00, 0.50, 0.80],
'2021-09-01': [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 a new change in beta to represent the takeover of the novel variant VOC 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
voc_days = np.linspace(sim.day('2020-08-01'), sim.day('2021-02-10'), 31)
voc_prop = 0.52 / (
1 + np.exp(-0.075 * (voc_days - sim.day('2020-09-30')))
) # Use a logistic growth function to approximate fig 2A of https://cmmid.github.io/topics/covid19/uk-novel-variant.html
voc_change = voc_prop * 1.60 + (1 - voc_prop) * 1.
voc_beta = cv.change_beta(days=voc_days, changes=voc_change)
interventions = [h_beta, w_beta, s_beta, c_beta, voc_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_vac1 = sim.day('2020-12-20')
tti_day_vac2 = sim.day('2021-03-08')
tti_day_change = sim.day('2021-03-08')
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
#0.114=70%; 0.149=80%; 0.205=90%
if future_symp_test is None: future_symp_test = s_prob_dec
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'}]
#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=future_symp_test,
asymp_prob=0.0063,
symp_quar_prob=0.0,
start_day=tti_day_jan,
end_day=tti_day_change - 1,
test_delay=t_delay),
cv.test_prob(symp_prob=0.114,
asymp_prob=0.0063,
symp_quar_prob=0.0,
start_day=tti_day_change,
test_delay=t_delay),
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='2020-06-01',
end_day='2021-02-28',
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])]
# vaccination interventions
interventions += [
utils.two_dose_daily_delayed(300e3,
start_day='2020-12-20',
dose_delay=21,
delay=7 * 7,
take_prob=1.0,
rel_symp=0.05,
age_priority=50,
rel_trans=0.5,
cumulative=[0.7, 1.0],
dose_priority=[1, 0.2])
]
#interventions += [utils.two_dose_daily_delayed(300e3, start_day='2021-03-09', dose_delay=21, delay=10*7,
# take_prob=1.0, rel_symp=0.05, age_priority=50,
# rel_trans=0.5, cumulative=[0.7, 1.0], dose_priority=[1, 0.2])]
analyzers = []
analyzers += [
utils.record_dose_flows(vacc_class=utils.two_dose_daily_delayed)
]
# 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
def test_all_interventions(do_plot=False):
''' Test all interventions supported by Covasim '''
sc.heading('Testing default interventions')
# Default parameters, using the random layer
pars = sc.objdict(
pop_size=1e3,
pop_infected=10,
n_days=90,
verbose=verbose,
)
hpars = sc.mergedicts(
pars,
{'pop_type': 'hybrid'}) # Some, but not all, tests require layers
rsim = cv.Sim(pars)
hsim = cv.Sim(hpars)
def make_sim(which='r', interventions=None):
''' Helper function to avoid having to recreate the sim each time '''
if which == 'r': sim = sc.dcp(rsim)
elif which == 'h': sim = sc.dcp(hsim)
sim['interventions'] = interventions
sim.initialize()
return sim
#%% Define the interventions
# 1. Dynamic pars
i1a = cv.test_prob(start_day=5, symp_prob=0.3)
i1b = cv.dynamic_pars({
'beta': {
'days': [40, 50],
'vals': [0.005, 0.015]
},
'rel_death_prob': {
'days': 30,
'vals': 2.0
}
}) # Starting day 30, make diagnosed people stop transmitting
# 2. Sequence
i2 = cv.sequence(days=[15, 30, 45],
interventions=[
cv.test_num(daily_tests=[20] * pars.n_days),
cv.test_prob(symp_prob=0.0),
cv.test_prob(symp_prob=0.2),
])
# 3. Change beta
i3a = cv.change_beta([30, 50], [0.0, 1.0], layers='h')
i3b = cv.change_beta([30, 40, 60], [0.0, 1.0, 0.5])
# 4. Clip edges -- should match the change_beta scenarios -- note that intervention i07 was removed
i4a = cv.clip_edges([30, 50], [0.0, 1.0], layers='h')
i4b = cv.clip_edges([30, 40, 60], [0.0, 1.0, 0.5])
# 5. Test number
i5 = cv.test_num(daily_tests=[100, 100, 100, 0, 0, 0] * (pars.n_days // 6))
# 6. Test probability
i6 = cv.test_prob(symp_prob=0.1)
# 7. Contact tracing
i7a = cv.test_prob(start_day=20,
symp_prob=0.01,
asymp_prob=0.0,
symp_quar_prob=1.0,
asymp_quar_prob=1.0,
test_delay=0)
i7b = cv.contact_tracing(start_day=20,
trace_probs=dict(h=0.9, s=0.7, w=0.7, c=0.3),
trace_time=dict(h=0, s=1, w=1, c=3))
# 8. Combination, with dynamically set days
def check_inf(interv, sim, thresh=10, close_day=18):
days = close_day if sim.people.infectious.sum() > thresh else np.nan
return days
i8a = cv.clip_edges(days=check_inf, changes=0.0,
layers='s') # Close schools
i8b = cv.clip_edges(days=[20, 32, 45],
changes=[0.7, 0.3, 0.9],
layers=['w', 'c']) # Reduce work and community
i8c = cv.test_prob(start_day=38,
symp_prob=0.01,
asymp_prob=0.0,
symp_quar_prob=1.0,
asymp_quar_prob=1.0,
test_delay=2) # Start testing for TTQ
i8d = cv.contact_tracing(start_day=40,
trace_probs=dict(h=0.9, s=0.7, w=0.7, c=0.3),
trace_time=dict(h=0, s=1, w=1,
c=3)) # Start tracing for TTQ
# 9. Vaccine
i9a = cv.simple_vaccine(days=20, prob=1.0, rel_sus=1.0, rel_symp=0.0)
i9b = cv.simple_vaccine(days=50, prob=1.0, rel_sus=0.0, rel_symp=0.0)
#%% Create the simulations
sims = sc.objdict()
sims.dynamic = make_sim('r', [i1a, i1b])
sims.sequence = make_sim('r', i2)
sims.change_beta1 = make_sim('h', i3a)
sims.clip_edges1 = make_sim('h', i4a) # Roughly equivalent to change_beta1
sims.change_beta2 = make_sim('r', i3b)
sims.clip_edges2 = make_sim('r', i4b) # Roughly equivalent to change_beta2
sims.test_num = make_sim('r', i5)
sims.test_prob = make_sim('r', i6)
sims.tracing = make_sim('h', [i7a, i7b])
sims.combo = make_sim('h', [i8a, i8b, i8c, i8d])
sims.vaccine = make_sim('r', [i9a, i9b])
# Run the simualations
for key, sim in sims.items():
sim.label = key
sim.run()
# Test intervention retrieval methods
sim = sims.combo
ce1, ce2 = sim.get_interventions(cv.clip_edges)
ce, tp = sim.get_interventions([0, 2])
inds = sim.get_interventions(cv.clip_edges, as_inds=True) # Returns [0,1]
assert inds == [0, 1]
sim.get_interventions('summary') # Prints a summary
#%% Plotting
if do_plot:
for sim in sims.values():
print(f'Running {sim.label}...')
sim.plot()
fig = pl.gcf()
try:
fig.axes[0].set_title(f'Simulation: {sim.label}')
except:
pass
return
def test_interventions(do_plot=False,
do_show=True,
do_save=False,
fig_path=None):
sc.heading('Test of testing interventions')
sc.heading('Setting up...')
sc.tic()
n_runs = 3
verbose = 1
base_pars = {
'pop_size': 1000,
'use_layers': True,
}
base_sim = cv.Sim(base_pars) # create sim object
n_people = base_sim['pop_size']
npts = base_sim.npts
# Define overall testing assumptions
# As the most optimistic case, we assume countries could get to South Korea's testing levels. S Korea has tested
# an average of 10000 people/day over March, or 270,000 in total. This is ~200 people per million every day (0.02%).
max_optimistic_testing = 0.0002
optimistic_daily_tests = [
max_optimistic_testing * n_people
] * npts # Very best-case scenario for asymptomatic testing
# Define the scenarios
scenarios = {
'baseline': {
'name': 'Status quo, no testing',
'pars': {
'interventions': None,
}
},
'test_skorea': {
'name':
'Assuming South Korea testing levels of 0.02% daily (untargeted); isolate positives',
'pars': {
'interventions':
cv.test_num(daily_tests=optimistic_daily_tests)
}
},
'tracing': {
'name':
'Assuming South Korea testing levels of 0.02% daily (with contact tracing); isolate positives',
'pars': {
'interventions': [
cv.test_num(daily_tests=optimistic_daily_tests),
cv.dynamic_pars({'cont_factor': {
'days': 20,
'vals': 0.1
}})
] # This means that people who've been in contact with known positives isolate with 90% effectiveness
}
},
'floating': {
'name': 'Test with constant probability based on symptoms',
'pars': {
'interventions':
cv.test_prob(symptomatic_prob=max_optimistic_testing,
asymptomatic_prob=0.0)
}
},
'historical': {
'name': 'Test a known number of positive cases',
'pars': {
'interventions':
cv.test_historical(n_tests=[100] * npts, n_positive=[1] * npts)
}
},
'sequence': {
'name': 'Historical switching to probability',
'pars': {
'interventions':
cv.sequence(days=[10, 51],
interventions=[
cv.test_historical(n_tests=[100] * npts,
n_positive=[1] * npts),
cv.test_prob(symptomatic_prob=0.2,
asymptomatic_prob=0.002),
])
}
},
}
metapars = {'n_runs': n_runs}
scens = cv.Scenarios(sim=base_sim, metapars=metapars, scenarios=scenarios)
scens.run(verbose=verbose, debug=debug)
if do_plot:
scens.plot(do_save=do_save, do_show=do_show, fig_path=fig_path)
return scens
def create_sim(x):
beta = x[0]
pop_infected = x[1]
start_day = '2020-01-21'
end_day = '2020-05-31'
data_path = 'UK_Covid_cases_may21.xlsx'
# Set the parameters
total_pop = 67.86e6 # UK population size
pop_size = 100e3 # Actual simulated population
pop_scale = int(total_pop / pop_size)
pop_type = 'hybrid'
asymp_factor = 2
contacts = {'h': 3.0, 's': 20, 'w': 20, 'c': 20}
pars = sc.objdict(
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,
verbose=0.1,
)
# Create the baseline simulation
sim = cv.Sim(pars=pars, datafile=data_path, location='uk')
sim['prognoses']['sus_ORs'][0] = 1.0 # ages 0-10
sim['prognoses']['sus_ORs'][1] = 1.0 # ages 10-20
beta_days = [
'2020-02-14', '2020-03-16', '2020-03-23', '2020-04-30', '2020-05-15',
'2020-06-08'
]
#June opening with society opening
h_beta_changes = [1.00, 1.00, 1.29, 1.29, 1.29, 1.00]
s_beta_changes = [1.00, 0.90, 0.02, 0.02, 0.02, 0.80]
w_beta_changes = [0.90, 0.80, 0.20, 0.20, 0.20, 0.70]
c_beta_changes = [0.90, 0.80, 0.20, 0.20, 0.20, 0.80]
# Define the beta changes
h_beta = cv.change_beta(days=beta_days, changes=h_beta_changes, layers='h')
s_beta = cv.change_beta(days=beta_days, changes=s_beta_changes, layers='s')
w_beta = cv.change_beta(days=beta_days, changes=w_beta_changes, layers='w')
c_beta = cv.change_beta(days=beta_days, changes=c_beta_changes, layers='c')
#next line to save the intervention
interventions = [h_beta, w_beta, s_beta, c_beta]
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
# Tracing and enhanced testing strategy of symptimatics from 1st June
#testing in June remains the same as before June under this scenario
s_prob_march = 0.009
s_prob_april = 0.012
s_prob_may = 0.012
#no change in daily symptmatic probability in this scenario
s_prob_june = 0.012
t_delay = 1.0
iso_vals = [{k: 0.1 for k in 'hswc'}]
#tracing not on under this scenario
t_eff_june = 0.0
t_probs_june = {k: t_eff_june for k in 'hwsc'}
trace_d_1 = {'h': 0, 's': 1, 'w': 1, 'c': 2}
#testing and isolation intervention
interventions += [
cv.test_prob(symp_prob=s_prob_march,
asymp_prob=0.0,
symp_quar_prob=0.0,
asymp_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,
asymp_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=0.00075,
symp_quar_prob=0.0,
asymp_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=0.00075,
symp_quar_prob=0.0,
asymp_quar_prob=0.0,
start_day=tti_day,
test_delay=t_delay),
cv.dynamic_pars({'iso_factor': {
'days': te_day,
'vals': iso_vals
}}),
cv.contact_tracing(trace_probs=t_probs_june,
trace_time=trace_d_1,
start_day=tti_day),
]
sim.update_pars(interventions=interventions)
for intervention in sim['interventions']:
intervention.do_plot = False
return sim
def make_sim(seed,
beta,
calibration=True,
scenario=None,
future_symp_test=None,
future_t_eff=None,
end_day=None,
verbose=0):
# Set the parameters
total_pop = 67.86e6 # UK population size
pop_size = 100e3 # Actual simulated population
pop_scale = int(total_pop / pop_size)
pop_type = 'hybrid'
pop_infected = 1500
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 = '2020-12-31'
pars = sc.objdict(
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_severe_prob = 0.4,
#rel_crit_prob = 2.3,
#rel_death_prob=1.5,
)
sim = cv.Sim(pars=pars, datafile=data_path, location='uk')
sim['prognoses']['sus_ORs'][0] = 1.0 # ages 0-10
sim['prognoses']['sus_ORs'][1] = 1.0 # ages 10-20
# ADD BETA INTERVENTIONS
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,
],
'2020-04-30': [
1.29,
0.02,
0.20,
0.20,
],
'2020-05-15': [
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,
],
'2020-08-22': [
1.29,
0.00,
0.30,
0.50,
]
})
if not calibration:
if scenario == 'masks15':
sbv1, sbv2, wbv1, wbv2, cbv1, cbv2 = 0.765, 0.90, 0.595, 0.425, 0.765, 0.595
elif scenario == 'masks30':
sbv1, sbv2, wbv1, wbv2, cbv1, cbv2 = 0.63, 0.90, 0.49, 0.35, 0.63, 0.49
elif scenario == 'masks15_notschools':
sbv1, sbv2, wbv1, wbv2, cbv1, cbv2 = 0.90, 0.90, 0.595, 0.425, 0.765, 0.595
elif scenario == 'masks30_notschools':
sbv1, sbv2, wbv1, wbv2, cbv1, cbv2 = 0.90, 0.90, 0.49, 0.35, 0.63, 0.49
beta_scens = sc.odict({
'2020-09-02': [1.25, sbv1, wbv1, cbv1],
'2020-10-28': [1.25, 0.00, wbv2, cbv2],
'2020-11-01': [1.25, sbv1, wbv1, cbv1],
'2020-12-23': [1.25, 0.00, wbv2, cbv2],
'2020-12-25': [1.50, 0.00, 0.20, 0.70],
'2021-01-03': [1.25, sbv1, wbv1, cbv1],
'2021-02-17': [1.25, 0.00, wbv2, cbv2],
'2021-02-21': [1.25, sbv1, wbv1, cbv1],
'2021-04-06': [1.25, 0.00, wbv2, cbv2],
'2021-04-19': [1.25, sbv2, wbv1, cbv1]
})
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 a new change in beta to represent the takeover of the novel variant VOC 202012/01
# Assume that the new variant is 60% more transmisible (https://cmmid.github.io/topics/covid19/uk-novel-variant.html,
# Assume that between August and Jan 30, the new variant grows from 0-100% of cases
#voc_days = np.linspace(sim.day('2020-08-01'), sim.day('2021-01-30'), 31)
#voc_prop = 0.6/(1+np.exp(-0.075*(voc_days-sim.day('2020-09-30')))) # Use a logistic growth function to approximate fig 2A of https://cmmid.github.io/topics/covid19/uk-novel-variant.html
#voc_change = voc_prop*1.63 + (1-voc_prop)*1.
#voc_beta = cv.change_beta(days=voc_days,
# changes=voc_change)
#interventions = [h_beta, w_beta, s_beta, c_beta, voc_beta]
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_vac = sim.day('2020-12-20')
s_prob_april = 0.009
s_prob_may = 0.017
s_prob_june = 0.02769
s_prob_july = 0.02769
s_prob_august = 0.02769
s_prob_sep = 0.02769
if future_symp_test is None: future_symp_test = s_prob_sep
t_delay = 1.0
#isolation may-july
iso_vals = [{k: 0.7 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'}]
#testing and isolation intervention
interventions += [
cv.test_prob(symp_prob=0.0075,
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=0.00075,
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=0.00075,
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=0.00075,
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=0.00075,
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=future_symp_test,
asymp_prob=0.00075,
symp_quar_prob=0.0,
start_day=tti_day_sep,
test_delay=t_delay),
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='2020-06-01',
quar_period=10),
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
}})
]
# Finally, update the parameters
sim.update_pars(interventions=interventions)
for intervention in sim['interventions']:
intervention.do_plot = False
sim.initialize()
return sim
def test_all_interventions():
''' Test all interventions supported by Covasim '''
pars = sc.objdict(
pop_size=1e3,
pop_infected=10,
pop_type='hybrid',
n_days=90,
)
#%% Define the interventions
# 1. Dynamic pars
i1a = cv.test_prob(start_day=5, symp_prob=0.3)
i1b = cv.dynamic_pars({
'beta': {
'days': [40, 50],
'vals': [0.005, 0.015]
},
'rel_death_prob': {
'days': 30,
'vals': 2.0
}
}) # Starting day 30, make diagnosed people stop transmitting
# 2. Sequence
i2 = cv.sequence(days=[15, 30, 45],
interventions=[
cv.test_num(daily_tests=[20] * pars.n_days),
cv.test_prob(symp_prob=0.0),
cv.test_prob(symp_prob=0.2),
])
# 3. Change beta
i3a = cv.change_beta([30, 50], [0.0, 1.0], layers='h')
i3b = cv.change_beta([30, 40, 60], [0.0, 1.0, 0.5])
# 4. Clip edges -- should match the change_beta scenarios -- note that intervention i07 was removed
i4a = cv.clip_edges([30, 50], [0.0, 1.0], layers='h')
i4b = cv.clip_edges([30, 40, 60], [0.0, 1.0, 0.5])
# 5. Test number
i5 = cv.test_num(daily_tests=[100, 100, 100, 0, 0, 0] * (pars.n_days // 6))
# 6. Test probability
i6 = cv.test_prob(symp_prob=0.1)
# 7. Contact tracing
i7a = cv.test_prob(start_day=20,
symp_prob=0.01,
asymp_prob=0.0,
symp_quar_prob=1.0,
asymp_quar_prob=1.0,
test_delay=0)
i7b = cv.contact_tracing(start_day=20,
trace_probs=dict(h=0.9, s=0.7, w=0.7, c=0.3),
trace_time=dict(h=0, s=1, w=1, c=3))
# 8. Combination
i8a = cv.clip_edges(days=18, changes=0.0, layers='s') # Close schools
i8b = cv.clip_edges(days=[20, 32, 45],
changes=[0.7, 0.3, 0.9],
layers=['w', 'c']) # Reduce work and community
i8c = cv.test_prob(start_day=38,
symp_prob=0.01,
asymp_prob=0.0,
symp_quar_prob=1.0,
asymp_quar_prob=1.0,
test_delay=2) # Start testing for TTQ
i8d = cv.contact_tracing(start_day=40,
trace_probs=dict(h=0.9, s=0.7, w=0.7, c=0.3),
trace_time=dict(h=0, s=1, w=1,
c=3)) # Start tracing for TTQ
#%% Create and run the simulations
sims = sc.objdict()
sims.dynamic = cv.Sim(pars=pars, interventions=[i1a, i1b])
sims.sequence = cv.Sim(pars=pars, interventions=i2)
sims.change_beta1 = cv.Sim(pars=pars, interventions=i3a)
sims.clip_edges1 = cv.Sim(
pars=pars, interventions=i4a) # Roughly equivalent to change_beta1
sims.change_beta2 = cv.Sim(pars=pars, interventions=i3b)
sims.clip_edges2 = cv.Sim(
pars=pars, interventions=i4b) # Roughly euivalent to change_beta2
sims.test_num = cv.Sim(pars=pars, interventions=i5)
sims.test_prob = cv.Sim(pars=pars, interventions=i6)
sims.tracing = cv.Sim(pars=pars, interventions=[i7a, i7b])
sims.combo = cv.Sim(pars=pars, interventions=[i8a, i8b, i8c, i8d])
for key, sim in sims.items():
sim.label = key
sim.run(verbose=verbose)
#%% Plotting
if do_plot:
for sim in sims.values():
print(f'Running {sim.label}...')
sim.plot()
fig = pl.gcf()
fig.axes[0].set_title(f'Simulation: {sim.label}')
return
def create_base_sim():
# Create a sim calibrated to the UK epidemic to date, including interventions in effect until May 31
start_day = '2020-02-01'
end_day = '2020-07-31' # We're running the analysis steps, which means running til July 2021
# Set the parameters
total_pop = 67.86e6 # UK population size
pop_size = 100e3 # Actual simulated population
pop_scale = int(total_pop / pop_size)
pop_type = 'hybrid'
pop_infected = 2000
beta = 0.0070
asymp_factor = 2.0
contacts = {'h': 3.0, 's': 20, 'w': 20, 'c': 20}
pars = sc.objdict(
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,
)
# Create the baseline simulation
sim = cv.Sim(pars=pars, datafile=data_path, location='uk')
# Create the distancing interventions that were in effect from the beginning of the epidemic until May 31
beta_days = [
'2020-02-14', '2020-03-16', '2020-03-23', '2020-04-30', '2020-05-15'
]
h_beta_changes = [
1.00, 1.00, 1.29, 1.29, 1.29
] # Assume increase household transmission after schools closed
s_beta_changes = [
1.00,
0.90,
0.02,
0.02,
0.02,
] # Schools closed March 23
w_beta_changes = [
0.90,
0.80,
0.20,
0.20,
0.20,
] # Workplace closures
c_beta_changes = [0.90, 0.80, 0.20, 0.20,
0.20] # Reduce effective community contacts
h_beta = cv.change_beta(days=beta_days, changes=h_beta_changes, layers='h')
s_beta = cv.change_beta(days=beta_days, changes=s_beta_changes, layers='s')
w_beta = cv.change_beta(days=beta_days, changes=w_beta_changes, layers='w')
c_beta = cv.change_beta(days=beta_days, changes=c_beta_changes, layers='c')
interventions = [h_beta, w_beta, s_beta, c_beta]
# Create the testing interventions that were in place from the beginning of the epidemic until May 31
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
s_prob_march = 0.009
s_prob_april = 0.012
s_prob_may = 0.0139
t_delay = 1.0
iso_vals = [{k: 0.1 for k in 'hswc'}]
interventions += [
cv.test_prob(symp_prob=s_prob_march,
asymp_prob=0.0,
symp_quar_prob=0.0,
asymp_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,
asymp_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=0.00075,
symp_quar_prob=0.0,
asymp_quar_prob=0.0,
start_day=tt_day,
end_day=tti_day - 1,
test_delay=t_delay),
cv.dynamic_pars({'iso_factor': {
'days': te_day,
'vals': iso_vals
}}),
]
sim.update_pars(interventions=interventions)
for intervention in sim['interventions']:
intervention.do_plot = False
return sim
''' Demonstrate dynamic parameters ''' import covasim as cv # Define the dynamic parameters imports = cv.dynamic_pars(n_imports=dict(days=[15, 30], vals=[100, 0])) # Create, run, and plot the simulations sim1 = cv.Sim(label='Baseline') sim2 = cv.Sim(interventions=imports, label='With imported infections') msim = cv.MultiSim([sim1, sim2]) msim.run() msim.plot()
asymp_prob=0.0175,
symp_quar_prob=0.0,
asymp_quar_prob=0.0,
start_day=tti_day_oct,
end_day=tti_day_nov - 1,
test_delay=t_delay,
sensitivity=0.97),
cv.test_prob(symp_prob=s_prob_nov,
asymp_prob=0.0275,
symp_quar_prob=0.0,
asymp_quar_prob=0.0,
start_day=tti_day_nov,
test_delay=t_delay,
sensitivity=0.97),
cv.dynamic_pars({'iso_factor': {
'days': te_day,
'vals': iso_vals
}}),
cv.contact_tracing(trace_probs=t_probs_june,
trace_time=trace_d_1,
start_day=tti_day,
end_day=tti_day_july - 1),
cv.contact_tracing(trace_probs=t_probs_july,
trace_time=trace_d_1,
start_day=tti_day_july,
end_day=tti_day_august - 1),
cv.contact_tracing(trace_probs=t_probs_august,
trace_time=trace_d_1,
start_day=tti_day_august,
end_day=tti_day_sep - 1),
cv.contact_tracing(trace_probs=t_probs_sep,
trace_time=trace_d_1,
def test_all_interventions():
''' Test all interventions supported by Covasim '''
pars = sc.objdict(
pop_size=1e3,
pop_infected=10,
pop_type='hybrid',
n_days=90,
)
#%% Define the interventions
# 1. Dynamic pars
i00 = cv.test_prob(start_day=5, symp_prob=0.3)
i01 = cv.dynamic_pars({
'beta': {
'days': [40, 50],
'vals': [0.005, 0.015]
},
'diag_factor': {
'days': 30,
'vals': 0.0
}
}) # Starting day 30, make diagnosed people stop transmitting
# 2. Sequence
i02 = cv.sequence(days=[15, 30, 45],
interventions=[
cv.test_num(daily_tests=[20] * pars.n_days),
cv.test_prob(symp_prob=0.0),
cv.test_prob(symp_prob=0.2),
])
# 3. Change beta
i03 = cv.change_beta([30, 50], [0.0, 1], layers='h')
i04 = cv.change_beta([30, 40, 60], [0.0, 1.0, 0.5])
# 4. Clip edges -- should match the change_beta scenarios
i05 = cv.clip_edges(start_day=30, end_day=50, change={'h': 0.0})
i06 = cv.clip_edges(start_day=30, end_day=40, change=0.0)
i07 = cv.clip_edges(start_day=60, end_day=None, change=0.5)
# 5. Test number
i08 = cv.test_num(daily_tests=[100, 100, 100, 0, 0, 0] *
(pars.n_days // 6))
# 6. Test probability
i09 = cv.test_prob(symp_prob=0.1)
# 7. Contact tracing
i10 = cv.test_prob(start_day=20,
symp_prob=0.01,
asymp_prob=0.0,
symp_quar_prob=1.0,
asymp_quar_prob=1.0,
test_delay=0)
i11 = cv.contact_tracing(start_day=20,
trace_probs=dict(h=0.9, s=0.7, w=0.7, c=0.3),
trace_time=dict(h=0, s=1, w=1, c=3))
# 8. Combination
i12 = cv.clip_edges(start_day=18, change={'s': 0.0}) # Close schools
i13 = cv.clip_edges(start_day=20, end_day=32, change={
'w': 0.7,
'c': 0.7
}) # Reduce work and community
i14 = cv.clip_edges(start_day=32, end_day=45, change={
'w': 0.3,
'c': 0.3
}) # Reduce work and community more
i15 = cv.clip_edges(start_day=45,
end_day=None,
change={
'w': 0.9,
'c': 0.9
}) # Reopen work and community more
i16 = cv.test_prob(start_day=38,
symp_prob=0.01,
asymp_prob=0.0,
symp_quar_prob=1.0,
asymp_quar_prob=1.0,
test_delay=2) # Start testing for TTQ
i17 = cv.contact_tracing(start_day=40,
trace_probs=dict(h=0.9, s=0.7, w=0.7, c=0.3),
trace_time=dict(h=0, s=1, w=1,
c=3)) # Start tracing for TTQ
#%% Create and run the simulations
sims = sc.objdict()
sims.dynamic = cv.Sim(pars=pars, interventions=[i00, i01])
sims.sequence = cv.Sim(pars=pars, interventions=i02)
sims.change_beta1 = cv.Sim(pars=pars, interventions=i03)
sims.clip_edges1 = cv.Sim(
pars=pars, interventions=i05) # Roughly equivalent to change_beta1
sims.change_beta2 = cv.Sim(pars=pars, interventions=i04)
sims.clip_edges2 = cv.Sim(
pars=pars, interventions=[i06,
i07]) # Roughly euivalent to change_beta2
sims.test_num = cv.Sim(pars=pars, interventions=i08)
sims.test_prob = cv.Sim(pars=pars, interventions=i09)
sims.tracing = cv.Sim(pars=pars, interventions=[i10, i11])
sims.combo = cv.Sim(pars=pars,
interventions=[i12, i13, i14, i15, i16, i17])
for key, sim in sims.items():
sim.label = key
sim.run(verbose=verbose)
#%% Plotting
if do_plot:
for sim in sims.values():
print(f'Running {sim.label}...')
sim.plot()
fig = pl.gcf()
fig.axes[0].set_title(f'Simulation: {sim.label}')
return
def create_sim(beta,n0,rd1,rd2,rd3,seed):
beta = beta
pop_infected = n0 # initial cases of infection
start_day = '2020-02-01'
end_day = '2020-11-30'
data_file = df
# Set the parameters
total_pop = 5.8e6 # Denmark population size
pop_size = 100e3 # Actual simulated population
pop_scale = int(total_pop/pop_size)
pop_type = 'hybrid'
asymp_factor = 2
pars = sc.objdict(
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,
rescale = True,
verbose = 0.1,
rand_seed = seed,
)
# Create the baseline simulation
sim = cv.Sim(pars=pars, datafile=data_file, location='denmark')
relative_death = cv.dynamic_pars(rel_death_prob=dict(days=[1,130,230], vals=[rd1,rd2,rd3]))
interventions = [relative_death]
### beta changes ###
beta_days = ['2020-03-15','2020-04-15','2020-05-10','2020-06-22','2020-07-20','2020-08-22','2020-09-01','2020-09-22','2020-10-01','2020-10-15','2020-11-01',
'2020-11-20']
h_beta_changes = [1.10, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.10, 1.10, 1.10, 1.20]
s_beta_changes = [0.80, 0.50, 0.50, 0.40, 0.60, 0.60, 1.00, 0.80, 0.80, 1.00, 0.80, 0.90]
w_beta_changes = [0.80, 0.50, 0.50, 0.40, 0.60, 0.60, 1.00, 0.80, 0.80, 1.00, 0.80, 0.90]
c_beta_changes = [0.90, 0.60, 0.50, 0.70, 0.90, 0.80, 1.00, 0.70, 0.70, 1.00, 0.80, 1.10]
# Define the beta changes
h_beta = cv.change_beta(days=beta_days, changes=h_beta_changes, layers='h')
s_beta = cv.change_beta(days=beta_days, changes=s_beta_changes, layers='s')
w_beta = cv.change_beta(days=beta_days, changes=w_beta_changes, layers='w')
c_beta = cv.change_beta(days=beta_days, changes=c_beta_changes, layers='c')
### edge clipping ###
clip_days = ['2020-03-15','2020-04-15','2020-05-10','2020-06-08','2020-06-22','2020-08-17','2020-09-01','2020-09-15','2020-10-05',
'2020-11-05','2020-11-20','2020-12-07','2020-12-19','2020-12-25']
s_clip_changes = [0.01, 0.20, 0.40, 0.70, 0.05, 0.10, 0.90, 0.80, 0.70, 0.70, 0.70, 0.60, 0.15, 0.05]
w_clip_changes = [0.10, 0.30, 0.50, 0.70, 0.60, 0.80, 1.00, 0.80, 0.70, 0.70, 0.70, 0.70, 0.70, 0.10]
c_clip_changes = [0.20, 0.40, 0.60, 0.85, 1.00, 1.00, 1.00, 0.80, 0.80, 0.90, 0.90, 0.90, 1.00, 0.30]
# Define the edge clipping
s_clip = cv.clip_edges(days=clip_days, changes=s_clip_changes, layers='s')
w_clip = cv.clip_edges(days=clip_days, changes=w_clip_changes, layers='w')
c_clip = cv.clip_edges(days=clip_days, changes=c_clip_changes, layers='c')
interventions += [h_beta, w_beta, s_beta, c_beta, w_clip, s_clip, c_clip]
# import infections from 2020-02-20 to 2020-03-01
imports1 = cv.dynamic_pars(n_imports=dict(days=[25, 35], vals=[2,0]))
imports2 = cv.dynamic_pars(n_imports=dict(days=[171, 190], vals=[2,0]))
interventions += [imports1,imports2]
iso_vals = [{'h': 0.5, 's': 0.05, 'w': 0.05, 'c': 0.1}] #dict(h=0.5, s=0.05, w=0.05, c=0.1)
interventions += [cv.dynamic_pars({'iso_factor': {'days': sim.day('2020-03-15'), 'vals': iso_vals }})]
# From May 12, starting tracing and isolation strategy
tracing_prob = dict(h=1.0, s=0.5, w=0.5, c=0.2)
trace_time = {'h':0, 's':1, 'w':1, 'c':2}
interventions += [cv.contact_tracing(trace_probs=tracing_prob, trace_time=trace_time, start_day='2020-05-12')]
interventions += [cv.test_num(daily_tests=sim.data['new_tests'], start_day=0, end_day=sim.day(end_day), test_delay=1, symp_test=50,
sensitivity=0.97,subtarget= prior_test)]
sim.update_pars(interventions=interventions)
for intervention in sim['interventions']:
intervention.do_plot = False
sim.initialize()
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=1.0,
rel_severe_prob=0.9,
rel_crit_prob=1.4,
rel_death_prob=1.2,
)
sim = cv.Sim(pars=pars, datafile=data_path, location='uk')
#sim['prognoses']['sus_ORs'][0] = 0.5 # 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, 0.20, 0.40],
'2020-12-25': [1.50, 0.00, 0.20, 0.40],
'2020-12-26': [1.50, 0.00, 0.20, 0.40],
'2020-12-31': [1.50, 0.00, 0.20, 0.40],
'2021-01-01': [1.50, 0.00, 0.20, 0.40],
'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],
'2021-01-30': [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, 0.00, 0.30, 0.50],
'2021-04-05': [1.25, 0.00, 0.30, 0.50],
'2021-04-12': [1.25, 0.00, 0.40, 0.50],
'2021-04-19': [1.25, sbv, 0.40, 0.50],
'2021-04-26': [1.25, sbv, 0.40, 0.50],
'2021-05-03': [1.25, sbv, 0.40, 0.50],
'2021-05-10': [1.25, sbv, 0.40, 0.50],
'2021-05-17': [1.15, sbv, 0.40, 0.60],
'2021-05-21': [1.15, sbv, 0.40, 0.60],
'2021-05-28': [1.15, 0.00, 0.40, 0.60],
'2021-06-07': [1.15, sbv, 0.40, 0.60],
'2021-06-14': [1.15, sbv, 0.40, 0.60],
'2021-06-19': [1.15, sbv, 0.40, 0.60],
'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, 0.50, 0.80],
'2021-07-26': [1.25, 0.00, 0.50, 0.80],
'2021-08-02': [1.25, 0.00, 0.50, 0.80],
'2021-08-16': [1.25, 0.00, 0.50, 0.80],
'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],
'2021-10-22': [1.25, 0.02, 0.50, 0.90],
'2021-11-01': [1.25, 0.63, 0.70, 0.90],
'2021-11-08': [1.25, 0.63, 0.70, 0.90],
'2021-11-15': [1.25, 0.63, 0.70, 0.90],
'2021-11-22': [1.25, 0.63, 0.70, 0.90],
'2021-12-01': [1.25, 0.63, 0.70, 0.90],
#'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],
})
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({
'2021-06-21': [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],
'2021-10-22': [1.25, 0.02, 0.50, 0.90],
'2021-11-01': [1.25, 0.63, 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],
'2021-12-20': [1.25, 0.02, 0.50, 0.80],
'2022-01-05': [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({
'2021-06-21': [1.25, sbv, 0.40, 0.60],
'2021-06-28': [1.25, sbv, 0.40, 0.60],
'2021-07-05': [1.25, sbv, 0.40, 0.60],
'2021-07-12': [1.25, sbv, 0.40, 0.60],
'2021-07-19': [1.25, 0.00, 0.50, 0.80],
'2021-07-26': [1.25, 0.00, 0.50, 0.80],
'2021-08-02': [1.25, 0.00, 0.50, 0.80],
'2021-08-16': [1.25, 0.00, 0.50, 0.80],
'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],
'2021-10-22': [1.25, 0.02, 0.50, 0.90],
'2021-11-01': [1.25, 0.63, 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],
'2021-12-20': [1.25, 0.02, 0.50, 0.80],
'2022-01-05': [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.variant('b117',
days=np.arange(sim.day('2020-08-01'),
sim.day('2020-08-10')),
n_imports=500)
b117.p['rel_beta'] = 1.5
b117.p['rel_crit_prob'] = 1.3
b117.p['rel_death_prob'] = 1.6
b117.p['rel_severe_prob'] = 0.3
sim['variants'] += [b117]
# Add B.1.1351 strain
b1351 = cv.variant('b1351',
days=np.arange(sim.day('2020-01-10'),
sim.day('2020-01-20')),
n_imports=500)
b1351.p['rel_beta'] = 1.0
b1351.p['rel_death_prob'] = 1.0
b1351.p['rel_severe_prob'] = 1.0
sim['variants'] += [b1351]
# Add B.X.XXX strain starting middle of March
custom_strain = cv.variant(label='delta',
variant=cvp.get_variant_pars()['p1'],
days=np.arange(sim.day('2021-03-17'),
sim.day('2021-03-24')),
n_imports=2000)
custom_strain.p['rel_beta'] = 2.2
custom_strain.p['rel_crit_prob'] = 1.3
custom_strain.p['rel_death_prob'] = 1.1
custom_strain.p['rel_severe_prob'] = 1.0
sim['variants'] += [custom_strain]
# seems like we need to do this to deal with cross immunity?
sim.initialize()
sim['immunity']
prior = {'wild': 0.8, 'b117': 0.8, 'b1351': 0.8, 'delta': 0.8}
pre = {'wild': 0.8, 'b117': 0.8, 'b1351': 0.8, 'delta': 0.8}
for k, v in sim['variant_map'].items():
if v == 'delta':
for j, j_lab in sim['variant_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: 0.6 for k in 'hswc'}]
#isolation october
iso_vals3 = [{k: 0.6 for k in 'hswc'}]
#isolation november
iso_vals4 = [{k: 0.5 for k in 'hswc'}]
#isolation december
iso_vals5 = [{k: 0.4 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=0.008,
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=0.008,
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=0.008,
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
dose_pars = cvp.get_vaccine_dose_pars()['az']
dose_pars['interval'] = 7 * 12
variant_pars = cvp.get_vaccine_variant_pars()['az']
az_vaccine = sc.mergedicts({'label': 'az_uk'},
sc.mergedicts(dose_pars, variant_pars))
dose_pars = cvp.get_vaccine_dose_pars()['pfizer']
dose_pars['interval'] = 7 * 12
variant_pars = cvp.get_vaccine_variant_pars()['pfizer']
pfizer_vaccine = sc.mergedicts({'label': 'pfizer_uk'},
sc.mergedicts(dose_pars, variant_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')))]
# age targeted vaccination
def subtarget_75_100(sim):
inds = cv.true(sim.people.age >= 75)
if not hasattr(sim, 'subtarget_75_100'):
sim.subtarget_75_100 = cv.binomial_filter(
0.05,
inds) # 30% of 70+ years olds will not agree to get vaccinated
inds = np.setdiff1d(inds, sim.subtarget_75_100)
return {'inds': inds, 'vals': 0.02 * np.ones(len(inds))}
interventions += [
cv.vaccinate(vaccine=pfizer_vaccine,
prob=0.1,
subtarget=subtarget_75_100,
days=np.arange(sim.day('2020-12-20'),
sim.day('2021-10-30')))
]
def subtarget_60_75(sim):
inds = cv.true((sim.people.age >= 60) & (sim.people.age < 75))
if not hasattr(sim, 'subtarget_60_75'):
sim.subtarget_60_75 = cv.binomial_filter(
0.05,
inds) # 5% of 70+ years olds will not agree to get vaccinated
inds = np.setdiff1d(inds, sim.subtarget_60_75)
return {'inds': inds, 'vals': 0.02 * np.ones(len(inds))}
interventions += [
cv.vaccinate(vaccine=pfizer_vaccine,
prob=0.1,
subtarget=subtarget_60_75,
days=np.arange(sim.day('2021-01-28'),
sim.day('2021-10-30')))
]
def subtarget_50_60(sim):
inds = cv.true((sim.people.age >= 50) & (sim.people.age < 60))
if not hasattr(sim, 'subtarget_50_60'):
sim.subtarget_50_60 = cv.binomial_filter(
0.25, inds
) # 20% of 50-60 years olds will not agree to get vaccinated
inds = np.setdiff1d(inds, sim.subtarget_50_60)
return {'inds': inds, 'vals': 0.005 * np.ones(len(inds))}
interventions += [
cv.vaccinate(vaccine=az_vaccine,
prob=0.1,
subtarget=subtarget_50_60,
days=np.arange(sim.day('2021-02-10'),
sim.day('2021-10-30')))
]
#def subtarget_40_50(sim):
# inds = cv.true((sim.people.age >= 40) & (sim.people.age < 50))
# if not hasattr(sim, 'subtarget_40_50'):
# sim.subtarget_40_50 = cv.binomial_filter(0.07, inds) # 30% of 40-50 years olds will not agree to get vaccinated
# inds = np.setdiff1d(inds, sim.subtarget_40_50)
# return {'inds': inds, 'vals': 0.005*np.ones(len(inds))}
#interventions += [cv.vaccinate(vaccine=az_vaccine, prob=0.1, subtarget=subtarget_40_50,
# days=np.arange(sim.day('2021-04-10'), sim.day('2023-01-01')))]
def subtarget_45_50(sim):
inds = cv.true((sim.people.age >= 45) & (sim.people.age < 50))
if not hasattr(sim, 'subtarget_45_50'):
sim.subtarget_45_50 = cv.binomial_filter(
0.25, inds
) # 30% of 40-50 years olds will not agree to get vaccinated
inds = np.setdiff1d(inds, sim.subtarget_45_50)
return {'inds': inds, 'vals': 0.002 * np.ones(len(inds))}
interventions += [
cv.vaccinate(vaccine=az_vaccine,
prob=0.1,
subtarget=subtarget_45_50,
days=np.arange(sim.day('2021-03-20'),
sim.day('2021-10-30')))
]
def subtarget_40_45(sim):
inds = cv.true((sim.people.age >= 40) & (sim.people.age < 45))
if not hasattr(sim, 'subtarget_40_45'):
sim.subtarget_40_45 = cv.binomial_filter(
0.25, inds
) # 30% of 40-50 years olds will not agree to get vaccinated
inds = np.setdiff1d(inds, sim.subtarget_40_45)
return {'inds': inds, 'vals': 0.002 * np.ones(len(inds))}
interventions += [
cv.vaccinate(vaccine=az_vaccine,
prob=0.1,
subtarget=subtarget_40_45,
days=np.arange(sim.day('2021-04-10'),
sim.day('2021-10-30')))
]
def subtarget_30_40(sim):
inds = cv.true((sim.people.age >= 30) & (sim.people.age < 40))
if not hasattr(sim, 'subtarget_30_40'):
sim.subtarget_30_40 = cv.binomial_filter(
0.4, inds
) # 30% of 30-40+ years olds will not agree to get vaccinated
inds = np.setdiff1d(inds, sim.subtarget_30_40)
return {'inds': inds, 'vals': 0.002 * np.ones(len(inds))}
interventions += [
cv.vaccinate(vaccine=pfizer_vaccine,
prob=0.1,
subtarget=subtarget_30_40,
days=np.arange(sim.day('2021-05-10'),
sim.day('2021-10-30')))
]
def subtarget_25_30(sim):
inds = cv.true((sim.people.age >= 25) & (sim.people.age < 30))
if not hasattr(sim, 'subtarget_25_30'):
sim.subtarget_25_30 = cv.binomial_filter(
0.4,
inds) # 30% of 18+ years olds will not agree to get vaccinated
inds = np.setdiff1d(inds, sim.subtarget_25_30)
return {'inds': inds, 'vals': 0.002 * np.ones(len(inds))}
interventions += [
cv.vaccinate(vaccine=pfizer_vaccine,
prob=0.1,
subtarget=subtarget_25_30,
days=np.arange(sim.day('2021-06-10'),
sim.day('2021-10-30')))
]
def subtarget_18_25(sim):
inds = cv.true((sim.people.age >= 18) & (sim.people.age < 25))
if not hasattr(sim, 'subtarget_18_30'):
sim.subtarget_18_25 = cv.binomial_filter(
0.4,
inds) # 30% of 18+ years olds will not agree to get vaccinated
inds = np.setdiff1d(inds, sim.subtarget_18_25)
return {'inds': inds, 'vals': 0.002 * np.ones(len(inds))}
interventions += [
cv.vaccinate(vaccine=pfizer_vaccine,
prob=0.1,
subtarget=subtarget_18_25,
days=np.arange(sim.day('2021-06-20'),
sim.day('2021-10-30')))
]
def subtarget_12_17(sim):
inds = cv.true((sim.people.age >= 12) & (sim.people.age < 17))
if not hasattr(sim, 'subtarget_12_17'):
sim.subtarget_18_25 = cv.binomial_filter(
0.3,
inds) # 30% of 18+ years olds will not agree to get vaccinated
inds = np.setdiff1d(inds, sim.subtarget_12_17)
return {'inds': inds, 'vals': 0.002 * np.ones(len(inds))}
interventions += [
cv.vaccinate(vaccine=pfizer_vaccine,
prob=0.1,
subtarget=subtarget_18_25,
days=np.arange(sim.day('2021-07-20'),
sim.day('2021-10-30')))
]
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
'interventions': None,
}
},
'distance': {
'name': 'Social distancing',
'pars': {
'interventions': cv.change_beta(days=interv_day,
changes=interv_eff)
}
},
'distance2': { # With noise = 0.0, this should be identical to the above
'name': 'Social distancing, version 2',
'pars': {
'interventions':
cv.dynamic_pars({
'beta':
dict(days=interv_day, vals=interv_eff * default_beta)
})
}
},
}
if __name__ == "__main__": # Required for parallel processing on Windows
sc.tic()
# If we're rerunning...
if do_run:
scens = cv.Scenarios(basepars=basepars,
metapars=metapars,
scenarios=scenarios)
scens.run(verbose=verbose)
def make_sim(seed,
beta,
calibration=True,
scenario=None,
future_symp_test=None,
future_t_eff=None,
end_day=None,
verbose=0):
# Set the parameters
total_pop = 67.86e6 # UK population size
pop_size = 100e3 # Actual simulated population
pop_scale = int(total_pop / pop_size)
pop_type = 'hybrid'
pop_infected = 1500
beta = beta
asymp_factor = 2
contacts = {'h': 3.0, 's': 20, 'w': 20, 'c': 20}
if end_day is None: end_day = '2021-03-31'
pars = sc.objdict(
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,
)
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
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,
],
'2020-04-30': [
1.29,
0.02,
0.20,
0.20,
],
'2020-05-15': [
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,
0.425,
0.49,
],
})
if not calibration:
if scenario == 'masks15':
sbv1, sbv2, wbv1, wbv2, cbv1, cbv2 = 0.765, 1.00, 0.595, 0.425, 0.765, 0.595
elif scenario == 'masks30':
sbv1, sbv2, wbv1, wbv2, cbv1, cbv2 = 0.63, 0.70, 0.49, 0.35, 0.63, 0.49
elif scenario == 'masks15_notschools':
sbv1, sbv2, wbv1, wbv2, cbv1, cbv2 = 0.90, 0.90, 0.595, 0.425, 0.765, 0.595
elif scenario == 'masks30_notschools':
sbv1, sbv2, wbv1, wbv2, cbv1, cbv2 = 0.90, 0.70, 0.49, 0.35, 0.63, 0.49
beta_scens = sc.odict({
'2020-09-02': [1.00, sbv1, wbv1, cbv1],
'2020-10-28': [1.00, 0.00, wbv2, cbv2],
'2020-11-01': [1.00, sbv1, wbv1, cbv1],
'2020-12-23': [1.00, 0.00, wbv2, cbv2],
'2021-01-03': [1.00, sbv1, wbv1, cbv1],
'2021-02-17': [1.00, 0.00, wbv2, cbv2],
'2021-02-21': [1.00, sbv1, wbv1, cbv1],
'2021-04-06': [1.00, 0.00, wbv2, cbv2],
'2021-04-17': [1.00, sbv2, wbv1, cbv1]
})
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')
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'
) #intervention of tracing and enhanced testing (tti) at different levels starts on 1st August
s_prob_april = 0.008
s_prob_may = 0.03
s_prob_june = 0.02
s_prob_july = 0.02
s_prob_august = 0.02
if future_symp_test is None: future_symp_test = s_prob_august
t_delay = 1.0
iso_vals = [{k: 0.1 for k in 'hswc'}]
#tracing level at 42.35% in June; 47.22% in July
t_eff_june = 0.42
t_eff_july = 0.47
if future_t_eff is None: future_t_eff = t_eff_july
t_probs_june = {k: t_eff_june for k in 'hwsc'}
t_probs_july = {k: t_eff_july for k in 'hwsc'}
future_t_probs = {k: future_t_eff for k in 'hwsc'}
trace_d_1 = {'h': 0, 's': 1, 'w': 1, 'c': 2}
#testing and isolation intervention
interventions += [
cv.test_prob(symp_prob=0.0075,
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=0.00075,
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=0.00075,
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=0.00075,
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=0.00075,
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=future_symp_test,
asymp_prob=0.00075,
symp_quar_prob=0.0,
start_day=tti_day_sep,
test_delay=t_delay),
cv.dynamic_pars({'iso_factor': {
'days': te_day,
'vals': iso_vals
}}),
cv.contact_tracing(trace_probs=t_probs_june,
trace_time=trace_d_1,
start_day=tti_day,
end_day=tti_day_july - 1),
cv.contact_tracing(trace_probs=t_probs_july,
trace_time=trace_d_1,
start_day=tti_day_july,
end_day=tti_day_sep - 1),
cv.contact_tracing(trace_probs=future_t_probs,
trace_time=trace_d_1,
start_day=tti_day_sep),
]
# Finally, update the parameters
sim.update_pars(interventions=interventions)
for intervention in sim['interventions']:
intervention.do_plot = False
sim.initialize()
return sim
