def speed(R0):
strdates = 'Days (Hub +Jan1)'
csse = CSSECovid()
deH, coH, reH, daH = csse.hubei()
pop = 6e7
nSteps = 500
gamma = 0.16
beta = gamma * R0
epsilon2 = 1
cfr2 = 0.03
delay_model2 = 0
system2 = sircd.SIRCD(susceptibles=pop,
infectious=1,
contact_rate=beta,
average_infection_period=1 / gamma,
epsilon=epsilon2,
delta=cfr2,
nSteps=nSteps,
t0=delay_model2)
system2.evolveSystem()
system2.plot()
t = 'R0=%g\nSymptomatic/asymptomatic = %g\n' % (system2.R0[0], epsilon2)
plt.title(t)
plt.ylim(10, )
plt.xlim(0, )
# plt.plot(daH, deH, '.-', color='C4', label='deaths Hubei')
# plt.plot(daH, coH, '.-', color='C3', label='confirmed Hubei')
plt.legend()
return system2
def plotEuropeDeath():
csse = CSSECovid()
deH, coH, daH = csse.hubei()
deI, coI, daI = csse.italy()
deF, coF, daF = csse.france()
deN, coN, daN = csse.netherlands()
deE, coE, daE = csse.spain()
deU, coU, daU = csse.uk()
deG, coG, daG = csse.germany()
deK, c*K, daK = csse.south_korea()
deS, coS, daS = csse.sweden()
deUs, coUs, daUs = csse.usa()
pop = 6e7
nSteps = 100
cfr = 0.03
delayModel = -1
beta0 = 0.481
gamma0 = 0.16
system3 = sircd.SIRCD(susceptibles=pop,
infectious=1,
contact_rate=beta0,
average_infection_period=1 / gamma0,
epsilon=0.5,
delta=cfr,
nSteps=nSteps,
t0=delayModel)
system3.evolveSystem()
system3.plot(susceptibles=False,
infectious=False,
recovered=False,
confirmed=False,
deaths=False)
plt.plot(daH, deH, '.-', color='C8', label='deaths Hubei')
plt.plot(daI,
deI,
'.-',
linewidth=2,
markersize=12,
color='C4',
label='deaths Italy')
plt.plot(daF, deF, '.-', color='C0', label='deaths France')
plt.plot(daE, deE, '.-', color='C3', label='deaths Spain')
plt.plot(daN, deN, '.-', color='C1', label='deaths Netherlands')
plt.plot(daU, deU, '.-', color='C2', label='deaths UK')
plt.plot(daG, deG, '.-', color='C7', label='deaths Germany')
plt.plot(daK, deK, '.-', color='C6', label='deaths South Korea')
plt.plot(daS, deS, '.-', color='C9', label='deaths Sweden')
plt.plot(daUs, deUs, '.-', color='C5', label='deaths USA')
plt.ylim(10, 1e6)
plt.xlim(10, 80)
plt.xlabel(csse.string_dates())
plt.title('')
plt.plot([], [], ' ', label=dataAtToday())
plt.legend()
def plotForecast():
csse = CSSECovid()
deH, coH, daH = csse.hubei()
deI, coI, daI = csse.italy()
pop = 6e7
nSteps = 100
epsilon = 0.75
cfr = 0.03
delayModel = -1
beta0 = 0.48
gamma0 = 0.16
beta2 = np.zeros(nSteps)
tInt2 = 31 - delayModel
beta2[:tInt2] = beta0
beta2[tInt2:] = 0.8 * gamma0
system3 = sircd.SIRCD(susceptibles=pop,
infectious=1,
contact_rate=beta2,
average_infection_period=1 / gamma0,
epsilon=epsilon,
delta=cfr,
nSteps=nSteps,
t0=delayModel)
system3.evolveSystem()
system3.plot(susceptibles=False,
infectious=False,
recovered=False,
confirmed=False,
deaths=False)
plt.plot(system3.timeSeries.timeVector,
system3.timeSeries.confirmed,
color='C7',
linestyle='dotted',
label='Confirmed eps=%g' % system3._epsilon[0])
plt.plot(system3.timeSeries.timeVector,
system3.timeSeries.deaths,
color='C7',
linestyle='dashed',
label='Deaths delta=%g' % system3._delta[0])
plt.plot(daH, deH, '.-', color='C8', label='deaths Hubei')
plt.plot(daI,
deI,
'.-',
linewidth=2,
markersize=12,
color='C4',
label='deaths Italy')
plt.plot(daH, coH, '.-', color='C8', label='confirmed Hubei')
plt.plot(daI, coI, '.-', color='C4', label='confirmed Italy')
plt.ylim(1, 1e5)
plt.xlim(0, 70)
plt.xlabel(csse.string_dates())
plt.legend()
def plotFB():
strdates = 'Days (Hub +Jan1, I +Feb6)'
csse = CSSECovid()
deH, coH, daH = csse.hubei()
deI, coI, daI = csse.italy()
pop = 6e7
nSteps = 100
epsilon = 0.75
cfr = 0.03
delayModel = -1
beta0 = 0.48
gamma0 = 0.16
beta = np.zeros(nSteps)
tInt2 = 31 - delayModel
beta[:tInt2] = beta0
beta[tInt2:] = 0.8 * gamma0
system3 = sircd.SIRCD(susceptibles=pop,
infectious=1,
contact_rate=beta,
average_infection_period=1 / gamma0,
epsilon=epsilon,
delta=cfr,
nSteps=nSteps,
t0=delayModel)
system3.evolveSystem()
system3.plot(susceptibles=False,
infectious=False,
recovered=False,
confirmed=False,
deaths=False)
plt.plot(system3.timeSeries.timeVector,
system3.timeSeries.deaths,
color='C2',
label='SIR model, R0=3')
plt.plot(daH, deH, '.-', color='C3', label='deaths Hubei')
plt.plot(daI,
deI,
'.-',
linewidth=2,
markersize=12,
color='C4',
label='deaths Italy')
plt.plot([], [], ' ', label="data at 17 Mar 2020")
plt.ylim(10, 1e5)
plt.xlim(20, 150)
plt.xlabel(strdates)
plt.title('')
plt.legend()
def plotModelloLombardia():
strdates = 'Days from Jan1'
csse = CSSECovid()
deH, coH, daH = csse.hubei()
dL = DpcCovid(DpcCovid.LOMBARDIA)
deL = dL.deceduti
coL = dL.totale_casi
daL = dL.days
nSteps = 100
delayModel = 0
periods = np.array([65])
E0 = 0
I0 = .00025
R0 = 0
A0 = I0 * 10
H0 = 0
S0 = 10000000 - E0 - I0 - R0 - A0 - H0
beta = piecewise(periods, (1.75, 0.65), nSteps)
r = 1
De = 5.2
Di = 2.3
Dq = piecewise(periods, (10, 10), nSteps)
Dh = 30
travellers = 0
system3 = seirah.SEIRAH(susceptibles=S0,
exposed=E0,
infectious=I0,
recovered=R0,
unascertained=A0,
hospitalized=H0,
transmission_rate=beta,
unscertained_transmission_rate_ratio=1,
ascertainment_fraction=r,
latent_period=De,
infectious_period=Di,
illness_to_hospitalization_period=Dq,
hospitalization_period=Dh,
inbound_outbound_travelers=travellers,
nSteps=nSteps,
t0=delayModel)
system3.evolveSystem()
system3.plot()
plt.plot(system3.timeSeries.timeVector,
system3.timeSeries.susceptibles[0] -
system3.timeSeries.susceptibles,
color='C7', label='total cases')
plt.plot(system3.timeSeries.timeVector,
system3.timeSeries.recovered_with_immunity * 0.005,
'--', color='C4', label='deaths')
plt.plot(dL.days, dL.deceduti, '+-', color='C4', label='deceduti')
# plt.plot(dL.days, dL.dimessi_guariti, 'x-', color='C4', label='dimessi')
# plt.plot(dL.days, dL.dimessi_guariti + dL.deceduti, '.-', color='C4', label='dimessi o deceduti')
plt.plot(dL.days, dL.totale_attualmente_positivi, '.-', color='C6',
label='att. positivi')
plt.plot(dL.days, dL.totale_casi, '.-', color='C7', label='totale casi')
plt.plot([], [], ' ', label=dataAtToday())
plt.ylim(10, 10e6)
plt.xlim(40, 100)
plt.xlabel(strdates)
plt.title('')
plt.legend()
def plotTalkXiongLin():
'''
https://harvard.zoom.us/rec/play/v8Ytceqqqzs3GNzB4gSDB_59W9TsK6Ks13RI_6cLxB62BSUAOlumZeRAZLC7e1vif7xIyy6HL_uXyNHw?startTime=1584118874000
'''
strdates = 'Days (Hub +Jan1, I +Feb6)'
csse = CSSECovid()
deH, coH, daH = csse.hubei()
deI, coI, daI = csse.italy()
nSteps = 100
delayModel = 0
periods = np.array([10, 22, 32])
S0 = 9999467
E0 = 346
I0 = 80
R0 = 0
A0 = 80
H0 = 27
beta = piecewise(periods, (1.75, 1.75, 0.58, 0.15), nSteps)
r = piecewise(periods, (0.19, 0.19, 0.22, 0.17), nSteps)
De = 5.2
Di = 2.3
Dh = 30
Dq = piecewise(periods, (10, 7, 5, 2), nSteps)
travellers = piecewise(periods, (500e3, 800e3, 0, 0), nSteps)
system3 = seirah.SEIRAH(susceptibles=S0,
exposed=E0,
infectious=I0,
recovered=R0,
unascertained=A0,
hospitalized=H0,
transmission_rate=beta,
unscertained_transmission_rate_ratio=1,
ascertainment_fraction=r,
latent_period=De,
infectious_period=Di,
illness_to_hospitalization_period=Dq,
hospitalization_period=Dh,
inbound_outbound_travelers=travellers,
nSteps=nSteps,
t0=delayModel)
system3.evolveSystem()
system3.plot()
# plt.plot(system3.timeSeries.timeVector,
# system3.timeSeries.deaths, color='C2', label='SIRCD model')
plt.plot(daH, deH, '.-', color='C3', label='deaths Hubei')
plt.plot(daH, coH, '+-', color='C3', label='confirmed Hubei')
# plt.plot(daI, deI, '.-',
# linewidth=2,
# markersize=12,
# color='C4',
# label='deaths Italy')
plt.plot([], [], ' ', label="data at 20 Mar 2020")
# plt.ylim(10, 1e4)
# plt.xlim(20, 60)
plt.xlabel(strdates)
plt.title('')
plt.legend()
return system3
def plotReport2():
strdates = 'Days (Hubei from Jan1, Italy from Feb6)'
csse = CSSECovid()
deH, coH, daH = csse.hubei()
pop = 6e7
nSteps = 200
beta0 = 0.48
gamma0 = 0.16
epsilon2 = 0.75
cfr2 = 0.03
delay_model2 = 0
beta2 = np.zeros(nSteps)
tInt2 = 32 - delay_model2
beta2[:tInt2] = beta0
beta2[tInt2:] = 0.8 * gamma0
system2 = sircd.SIRCD(susceptibles=pop,
infectious=1,
contact_rate=beta2,
average_infection_period=1 / gamma0,
epsilon=epsilon2,
delta=cfr2,
nSteps=nSteps,
t0=delay_model2)
system2.evolveSystem()
# system2.plot(susceptibles=False, infectious=False, recovered=False)
system2.plot()
t = 'Social rarefaction R0=0.8 from day %d\n $\epsilon$ = %g\n' % (
tInt2 + delay_model2, epsilon2)
plt.title(t)
plt.xlabel(strdates)
plt.ylim(10, )
plt.xlim(0, 100)
plt.plot(daH, deH, '.-', color='C4', label='deaths Hubei')
plt.plot(daH, coH, '.-', color='C3', label='confirmed Hubei')
plt.legend()
epsilon1 = 0.001
cfr1 = 0.00005
delay_model1 = -20
system1 = sircd.SIRCD(susceptibles=pop,
infectious=1,
contact_rate=beta0,
average_infection_period=1 / gamma0,
epsilon=epsilon1,
delta=cfr1,
nSteps=nSteps,
t0=delay_model1)
system1.evolveSystem()
system1.plot()
t = 'Constant contact_rate\n $\epsilon$ = %g\n' % (epsilon1)
plt.title(t)
plt.xlabel(strdates)
plt.ylim(10, )
plt.xlim(0, 100)
plt.plot(daH, deH, '.-', color='C4', label='deaths Hubei')
plt.plot(daH, coH, '.-', color='C3', label='confirmed Hubei')
plt.legend()
def plotFB():
delayI = 36
delayR = 34
delayH = 0
delayF = 45
delayS = 45
delayK = 35
strdates = 'Days (Hub +Jan1, I +Feb6)'
csse = CSSECovid()
deH, coH, reH, daH = csse.hubei()
deI, coI, reI, daI = csse.italy()
# deF, coF, reF, daF = csse.restoreFrance()
# deR, coR, reR, daR = _restoreCountry('Iran', delayR)
# deS, coS, reS, daS = csse.restoreSpain()
# deK, c*K, reK, daK = _restoreCountry('Korea, South', delayK)
pop = 6e7
nSteps = 100
cfr = 0.03
delayModel = -1
beta0 = 0.481
gamma0 = 0.16
system3 = sircd.SIRCD(susceptibles=pop,
infectious=1,
contact_rate=beta0,
average_infection_period=1 / gamma0,
epsilon=1,
delta=cfr,
nSteps=nSteps,
t0=delayModel)
system3.evolveSystem()
deaths3 = cfr * system3.timeSeries.recovered_with_immunity
system3.plot(susceptibles=False,
infectious=False,
recovered=False,
confirmed=False,
deaths=False)
plt.plot(system3.timeSeries.timeVector,
deaths3,
color='C2',
label='SIR model, R0=3')
plt.plot(daH, deH, '.-', color='C3', label='deaths Hubei')
plt.plot(daI,
deI,
'.-',
linewidth=2,
markersize=12,
color='C4',
label='deaths Italy')
# plt.plot(daF, deF, '.-', label='deaths France')
# plt.plot(daS, deS, '.-', label='deaths Spain')
# plt.plot(coHd + delayModel, coH, '.-', label='confirmed Hubei')
# plt.plot(coId + delayModel, coI, '.-', label='confirmed Italy')
# Create empty plot with blank marker containing the extra label
plt.plot([], [], ' ', label="data at 14 Mar 2020")
plt.ylim(10, 1e4)
plt.xlim(20, 60)
plt.xlabel(strdates)
plt.title('')
plt.legend()