def compliance_func(t, states, param, l, effectivity):
# Convert tau and l to dates
l_days = pd.Timedelta(l, unit='D')
# Measures
start_measures = pd.to_datetime('2020-03-15')
if t < start_measures:
return param
elif start_measures < t <= start_measures + l_days:
return ramp_fun(param, effectivity * param, t, start_measures, l)
else:
return param * effectivity
def report6_policy_function(t,
states,
param,
l,
tau,
prev_home,
prev_schools,
prev_work,
prev_rest,
scenario='1'):
# Convert tau and l to dates
tau_days = pd.Timedelta(tau, unit='D')
l_days = pd.Timedelta(l, unit='D')
# Define key policy dates
t1 = pd.Timestamp('2020-03-15') # start of lockdown
t2 = pd.Timestamp(
'2020-05-15'
) # gradual re-opening of schools (assume 50% of nominal scenario)
t3 = pd.Timestamp('2020-07-01') # start of summer: COVID-urgency very low
t4 = pd.Timestamp('2020-08-01')
t5 = pd.Timestamp(
'2020-09-01'
) # september: lockdown relaxation narrative in newspapers reduces sense of urgency
t6 = pd.Timestamp('2020-10-19') # lockdown
t7 = pd.Timestamp('2020-11-16') # schools re-open
t8 = pd.Timestamp('2020-12-18') # schools close
t9 = pd.Timestamp('2020-12-24')
t10 = pd.Timestamp('2020-12-26')
t11 = pd.Timestamp('2020-12-31')
t12 = pd.Timestamp('2021-01-01')
t13 = pd.Timestamp('2020-01-04') # Opening of schools
t14 = pd.Timestamp('2021-01-18') # start of alternative policies
t15 = pd.Timestamp('2021-02-15') # Start of spring break
t16 = pd.Timestamp('2021-02-21') # End of spring break
# Average out september mobility
if t5 < t <= t6 + tau_days:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t, school=1)
elif t6 + tau_days < t <= t6 + tau_days + l_days:
t = pd.Timestamp(t.date())
policy_old = contact_matrix_4prev(t, school=1)
policy_new = contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0)
return ramp_fun(policy_old, policy_new, t, tau_days, l, t6)
elif t6 + tau_days + l_days < t <= t7:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0)
elif t7 < t <= t8:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=1)
elif t8 < t <= t9:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0)
else:
# Scenario 1: Current contact behaviour + schools open on January 18th
if scenario == '1':
if t9 < t <= t13:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0)
elif t13 < t <= t15:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=1)
elif t15 < t <= t16:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0)
else:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=1)
# Scenario 2: increases in work or leisure mobility
elif scenario == '2a':
if t9 < t <= t13:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0)
elif t13 < t <= t14:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0.6)
elif t14 < t <= t15:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t, 1, 1, 1, 1, school=0.6, SB='2a')
elif t15 < t <= t16:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t, 1, 1, 1, 1, school=0, SB='2a')
else:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t, 1, 1, 1, 1, school=0.6, SB='2a')
elif scenario == '2b':
if t9 < t <= t13:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0)
elif t13 < t <= t14:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0.6)
elif t14 < t <= t15:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
1,
1,
prev_work,
1,
school=0.6,
SB='2b')
elif t15 < t <= t16:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
1,
1,
prev_work,
1,
school=0,
SB='2b')
else:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
1,
1,
prev_work,
1,
school=0.6,
SB='2b')
elif scenario == '2c':
if t9 < t <= t13:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0)
elif t13 < t <= t14:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0.6)
elif t14 < t <= t15:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
1,
1,
1,
prev_rest,
school=0.6,
SB='2c')
elif t15 < t <= t16:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
1,
1,
1,
prev_rest,
school=0,
SB='2c')
else:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
1,
1,
1,
prev_rest,
school=0.6,
SB='2c')
# Scenario 3: Christmas mentality change
elif scenario == '3':
if t9 < t <= t10:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
1,
prev_schools,
prev_work,
1,
school=0)
elif t10 < t <= t11:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
1,
prev_schools,
prev_work,
1,
school=0)
elif t11 < t <= t12:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
1,
prev_schools,
prev_work,
1,
school=0)
elif t12 < t <= t13:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0)
elif t13 < t <= t14:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=1)
elif t14 < t <= t15:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0.6)
elif t15 < t <= t16:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0)
else:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=1)
def report7_policy_function(t,
states,
param,
l,
tau,
prev_home,
prev_schools,
prev_work,
prev_rest,
scenario='1a'):
# Convert tau and l to dates
tau_days = pd.Timedelta(tau, unit='D')
l_days = pd.Timedelta(l, unit='D')
# Define key policy dates
t1 = pd.Timestamp('2020-03-15') # start of lockdown
t2 = pd.Timestamp(
'2020-05-15'
) # gradual re-opening of schools (assume 50% of nominal scenario)
t3 = pd.Timestamp('2020-07-01') # start of summer: COVID-urgency very low
t4 = pd.Timestamp('2020-08-01')
t5 = pd.Timestamp(
'2020-09-01'
) # september: lockdown relaxation narrative in newspapers reduces sense of urgency
t6 = pd.Timestamp('2020-10-19') # lockdown
t7 = pd.Timestamp('2020-11-16') # schools re-open
t8 = pd.Timestamp('2020-12-18') # start Christmas holidays (schools close)
t13 = pd.Timestamp('2020-01-04') # end Christmas holidays (schools open)
#t14 = pd.Timestamp('2021-01-18') # start of alternative policies
t15 = pd.Timestamp(
'2021-02-15') # Start of Krokus holidays (schools close)
t16 = pd.Timestamp('2021-02-21') # End of Krokus holidays (schools open)
t17 = pd.Timestamp('2021-03-01') # release to SB March 1
t18 = pd.Timestamp('2021-04-01') # release to SB April 1
t19 = pd.Timestamp('2021-04-05') # start Eastern Holidays (schools close)
t20 = pd.Timestamp('2021-04-19') # end Eastern Holidays (schools open)
t21 = pd.Timestamp('2021-05-01') # release to SB May 1
# Average out september mobility
if t5 < t <= t6 + tau_days:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t, school=1)
elif t6 + tau_days < t <= t6 + tau_days + l_days:
t = pd.Timestamp(t.date())
policy_old = contact_matrix_4prev(t, school=1)
policy_new = contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0)
return ramp_fun(policy_old, policy_new, t, tau_days, l, t6)
elif t6 + tau_days + l_days < t <= t7:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0)
elif t7 < t <= t8:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=1)
elif t8 < t <= t13:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0)
elif t13 < t <= t15:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0.6)
elif t15 < t <= t16:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0)
else:
# Scenario 1: Current contact behaviour
if scenario == '1':
if t16 < t <= t19:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0.6)
elif t19 < t <= t20:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0)
elif t20 < t:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0.6)
else:
raise Exception('scenario ' + scenario + ' t:' + str(t))
# Scenario 2: increases in work or leisure mobility on March 1
elif scenario == '2':
if t16 < t <= t17:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0.6)
elif t17 < t <= t19:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t, 1, 1, 1, 1, school=0.6, SB='2a')
elif t19 < t <= t20:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t, 1, 1, 1, 1, school=0, SB='2a')
elif t20 < t:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t, 1, 1, 1, 1, school=0.6, SB='2a')
else:
raise Exception('scenario ' + scenario + ' t:' + str(t))
# Scenario 3: increases in work or leisure mobility on April 1
elif scenario == '3':
if t16 < t <= t18:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0.6)
elif t18 < t <= t19:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t, 1, 1, 1, 1, school=0.6, SB='2a')
elif t19 < t <= t20:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t, 1, 1, 1, 1, school=0, SB='2a')
elif t20 < t:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t, 1, 1, 1, 1, school=0.6, SB='2a')
else:
raise Exception('scenario ' + scenario + ' t:' + str(t))
# Scenario 4: increases in work or leisure mobility on May 1
elif scenario == '4':
if t16 < t <= t19:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0.6)
elif t19 < t <= t20:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0)
elif t20 < t <= t21:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0.6)
elif t > t21:
t = pd.Timestamp(t.date())
return contact_matrix_4prev(t, 1, 1, 1, 1, school=0.6, SB='2a')
else:
raise Exception('scenario ' + scenario + ' t:' + str(t))
else:
raise Exception('scenario ' + scenario + ' non-existing')
def policies_RESTORE8(t,
states,
param,
l,
l_relax,
prev_schools,
prev_work,
prev_rest,
prev_home,
relaxdate,
scenario=0):
t = pd.Timestamp(t.date())
# Convert compliance tau and l to dates
l_days = pd.Timedelta(l, unit='D')
# Convert relaxation l to dates
l_relax_days = pd.Timedelta(l_relax, unit='D')
# Define key dates of first wave
t1 = pd.Timestamp('2020-03-15') # start of lockdown
t2 = pd.Timestamp(
'2020-05-15'
) # gradual re-opening of schools (assume 50% of nominal scenario)
t3 = pd.Timestamp('2020-07-01') # start of summer holidays
t4 = pd.Timestamp('2020-09-01') # end of summer holidays
# Define key dates of second wave
t5 = pd.Timestamp('2020-10-19') # lockdown (1)
t6 = pd.Timestamp('2020-11-02') # lockdown (2)
t7 = pd.Timestamp('2020-11-16') # schools re-open
t8 = pd.Timestamp('2020-12-18') # Christmas holiday starts
t9 = pd.Timestamp('2021-01-04') # Christmas holiday ends
t10 = pd.Timestamp('2021-02-15') # Spring break starts
t11 = pd.Timestamp('2021-02-21') # Spring break ends
t12 = pd.Timestamp('2021-02-28') # Contact increase in children
t13 = pd.Timestamp('2021-03-26') # Start of Easter holiday
t14 = pd.Timestamp('2021-04-18') # End of Easter holiday
t15 = pd.Timestamp(relaxdate) # Relaxation date
t16 = pd.Timestamp('2021-07-01') # Start of Summer holiday
t17 = pd.Timestamp('2021-09-01')
if t <= t1:
return all_contact(t)
elif t1 < t < t1:
return all_contact(t)
elif t1 < t <= t1 + l_days:
policy_old = all_contact(t)
policy_new = contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0)
return ramp_fun(policy_old, policy_new, t, t1, l)
elif t1 + l_days < t <= t2:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0)
elif t2 < t <= t3:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0)
elif t3 < t <= t4:
return contact_matrix_4prev(t, school=0)
# Second wave
elif t4 < t <= t5:
return contact_matrix_4prev(t, school=1)
elif t5 < t <= t5 + l_days:
policy_old = contact_matrix_4prev(t, school=1)
policy_new = contact_matrix_4prev(t,
prev_schools,
prev_work,
prev_rest,
school=1)
return ramp_fun(policy_old, policy_new, t, t5, l)
elif t5 + l_days < t <= t6:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=1)
elif t6 < t <= t7:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0)
elif t7 < t <= t8:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=1)
elif t8 < t <= t9:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0)
elif t9 < t <= t10:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=1)
elif t10 < t <= t11:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0)
elif t11 < t <= t12:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=1)
elif t12 < t <= t13:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=1)
elif t13 < t <= t14:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0)
if scenario == 0:
if t14 < t <= t15:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=1)
elif t15 < t <= t16:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=1)
elif t16 < t <= t17:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0)
else:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=1)
elif scenario == 1:
if t14 < t <= t15:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=1)
elif t15 < t <= t15 + l_relax_days:
policy_old = contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=1)
policy_new = contact_matrix_4prev(t,
prev_schools,
prev_work,
prev_rest,
work=1,
transport=1,
school=1)
return ramp_fun(policy_old, policy_new, t, t15, l_relax)
elif t15 + l_relax_days < t <= t16:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
work=1,
transport=1,
school=1)
elif t16 < t <= t17:
return contact_matrix_4prev(
t,
prev_home,
prev_schools,
prev_work,
prev_rest,
work=0.8,
transport=0.90,
school=0
) # 20% less work mobility during summer, 10% less public transport during summer
else:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
work=1,
transport=1,
school=1)
elif scenario == 2:
if t14 < t <= t15:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0)
elif t15 < t <= t15 + l_relax_days:
policy_old = contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0)
policy_new = contact_matrix_4prev(t,
prev_schools,
prev_work,
prev_rest,
work=1,
transport=1,
school=0)
return ramp_fun(
policy_old,
policy_new,
t,
t15,
l_relax,
)
elif t15 + l_relax_days < t <= t16:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
work=1,
transport=1,
school=0)
elif t16 < t <= t17:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
work=0.8,
transport=0.90,
school=0)
else:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
work=1,
transport=1,
school=1)
elif scenario == 3:
if t14 < t <= t15:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=1)
elif t15 < t <= t15 + l_relax_days:
policy_old = contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=1)
policy_new = contact_matrix_4prev(t,
prev_schools,
prev_work,
prev_rest,
leisure=1,
others=1,
transport=1,
school=1)
return ramp_fun(policy_old, policy_new, t, t15, l_relax)
elif t15 + l_relax_days < t <= t16:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
leisure=1,
others=1,
transport=1,
school=1)
elif t16 < t <= t17:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
leisure=1,
others=1,
transport=0.90,
school=0)
else:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
leisure=1,
others=1,
transport=1,
school=1)
elif scenario == 4:
if t14 < t <= t15:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0)
policy_new = contact_matrix_4prev(t,
prev_schools,
prev_work,
prev_rest,
leisure=1,
others=1,
transport=1,
school=0)
return ramp_fun(policy_old, policy_new, t, t15, l_relax)
elif t15 + l_relax_days < t <= t16:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
leisure=1,
others=1,
transport=1,
school=0)
elif t16 < t <= t17:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
leisure=1,
others=1,
transport=0.90,
school=0)
else:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
leisure=1,
others=1,
transport=1,
school=1)
elif scenario == 5:
if t14 < t <= t15:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=1)
elif t15 < t <= t15 + l_relax_days:
policy_old = contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=1)
policy_new = contact_matrix_4prev(t,
prev_schools,
prev_work,
prev_rest,
work=1,
leisure=1,
transport=1,
others=1,
school=1)
return ramp_fun(policy_old, policy_new, t, t15, l_relax)
elif t15 + l_relax_days < t <= t16:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
work=1,
leisure=1,
transport=1,
others=1,
school=1)
elif t16 < t <= t17:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
work=0.8,
leisure=1,
transport=0.90,
others=1,
school=0)
else:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
work=1,
leisure=1,
transport=1,
others=1,
school=1)
elif scenario == 6:
if t14 < t <= t15:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0)
if t15 < t <= t15 + l_relax_days:
policy_old = contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
school=0)
policy_new = contact_matrix_4prev(t,
prev_schools,
prev_work,
prev_rest,
work=1,
leisure=1,
transport=1,
others=1,
school=0)
return ramp_fun(policy_old, policy_new, t, t15, l_relax)
elif t15 + l_relax_days < t <= t16:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
work=1,
leisure=1,
transport=1,
others=1,
school=0)
elif t16 < t <= t17:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_rest,
work=0.8,
leisure=1,
transport=0.90,
others=1,
school=0)
else:
return contact_matrix_4prev(t,
prev_home,
prev_schools,
prev_work,
prev_work,
work=1,
leisure=1,
transport=1,
others=1,
school=1)