def plotGuido():
delayH = 0
strdates = 'Days (Hubei from Jan1, Italy from Feb6)'
de = restore_data.restoreCSSEDeath()
deH = de['Hubei/China'].values
deHd = de['Hubei/China'].days - delayH
pop = 6e7
nSteps = 100
cfr = 0.00006
delayModel = -21
beta0 = 0.481
gamma0 = 0.16
system3 = simple_sir.SimpleSIR(pop,
1,
0,
beta0,
1 / gamma0,
nSteps,
t0=delayModel)
system3.evolveSystem()
system3.plot()
deaths3 = cfr * system3.timeSeries.recovered_with_immunity
plt.plot(system3.timeSeries.timeVector,
deaths3,
label='deaths no action cfr=%g' % cfr)
plt.plot(deHd, deH, '.-', label='deaths Hubei')
plt.ylim(1, )
plt.xlim(10, 75)
plt.xlabel(strdates)
plt.legend()
def plotFB():
delayI = 36
delayR = 34
delayH = 0
delayF = 45
delayS = 45
delayK = 35
strdates = 'Days (Hub +Jan1, I +Feb6, F +Feb15, S +Feb15)'
deH, coH, reH, daH = _restoreCountry('Hubei/China', delayH)
deI, coI, reI, daI = _restoreCountry('Italy', delayI)
deF, coF, reF, daF = _restoreCountry('France/France', delayF)
# deR, coR, reR, daR = _restoreCountry('Iran', delayR)
deS, coS, reS, daS = _restoreCountry('Spain', delayS)
# 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 = simple_sir.SimpleSIR(pop,
1,
0,
beta0,
1 / gamma0,
nSteps,
t0=delayModel)
system3.evolveSystem()
deaths3 = cfr * system3.timeSeries.recovered_with_immunity
system3.plot(susceptibles=False, infectious=False, recovered=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')
plt.ylim(10, 1e4)
plt.xlim(20, 70)
plt.xlabel(strdates)
plt.legend()
def main():
syncI2H = 27
deH, coH, reH, daH = restoreCovid('Hubei')
deI, coI, reI, daI = restoreCovid('Italy', fromItem=syncI2H)
plt.figure()
plt.plot(np.arange(len(deI)), deI, '.-', label='Deaths Italy')
plt.plot(np.arange(len(deH)), deH, '.-', label='Deaths Hubei')
t = "Evolution of deaths in Hubei and Italy (both 60M people)"
plt.title(t)
plt.legend()
plt.grid(True)
plt.ylabel('Total deaths')
plt.xlabel('Time [days]. Arbitrary origin')
plt.show()
plt.semilogy()
co2de = 0.04
delay_co2de = 2
ff = np.ones(int(delay_co2de)) / delay_co2de
expDeH = co2de * convolve(coH, ff, mode='full')[:len(coH)]
expDeI = co2de * convolve(coI, ff, mode='full')[:len(coI)]
plt.figure()
plt.plot(np.arange(len(deI)), deI, '.-', label='Deaths Italy')
plt.plot(np.arange(len(deH)), deH, '.-', label='Deaths Hubei')
plt.plot(np.arange(len(coI)), coI, '.-', label='Confirmed Italy')
plt.plot(np.arange(len(coH)), coH, '.-', label='Confirmed Hubei')
plt.plot(np.arange(len(expDeI)), expDeI, label='Expected Deaths Italy')
plt.plot(np.arange(len(expDeH)), expDeH, label='Expected Deaths Hubei')
t = "Confirmed 2 Dead\n co2de=%g, delay=%g d" % (co2de, delay_co2de)
plt.title(t)
plt.legend()
plt.grid(True)
plt.ylabel('Total deaths')
plt.xlabel('Time [days]. Arbitrary origin')
plt.show()
plt.semilogy()
pop = 6e7
system = simple_sir.SimpleSIR(pop, 1, 0, 0.4, 15, 100)
system.evolveSystem()
system.plot()
cfr = 0.03
deaths = cfr * system.timeSeries.recovered_with_immunity
plt.plot(deaths, label='deaths (CFR=%g)' % (cfr))
plt.plot(np.arange(len(deH)) + 27, deH, '.-', label='deaths Hubei')
plt.plot(np.arange(len(deI)) + 27, deI, '.-', label='deaths Italy')
plt.plot(np.arange(len(coH)) + 27, coH, '.-', label='confirmed Hubei')
plt.plot(np.arange(len(coI)) + 27, coI, '.-', label='confirmed Italy')
plt.ylim(1, )
plt.legend()
def plotTuttoAZero():
delayI = 36
delayH = 0
strdates = 'Days (Hubei from Jan1, Italy from Feb6)'
de = restore_data.restoreCSSEDeath()
deH = de['Hubei/Mainland China'].values
deHd = de['Hubei/Mainland China'].days - delayH
deI = de['Italy'].values
deId = de['Italy'].days - delayI
pop = 6e7
nSteps = 100
cfr = 0.03
delayModel = -1
beta0 = 0.481
gamma0 = 0.16
beta = np.zeros(nSteps)
tInt1 = 29 - delayModel
beta[:tInt1] = beta0
beta[tInt1:] = gamma0
system = simple_sir.SimpleSIR(pop,
1,
0,
beta,
1 / gamma0,
nSteps,
t0=delayModel)
system.evolveSystem()
system.plot(susceptibles=False, infectious=False, recovered=False)
deaths = cfr * system.timeSeries.recovered_with_immunity
plt.plot(system.timeSeries.timeVector,
deaths,
label='deaths if R0=1 at day=%d' % (tInt1 + delayModel))
beta2 = np.zeros(nSteps)
tInt2 = 32 - delayModel
beta2[:tInt2] = beta0
beta2[tInt2:] = gamma0 * 0.
system2 = simple_sir.SimpleSIR(pop,
1,
0,
beta2,
1 / gamma0,
nSteps,
t0=delayModel)
system2.evolveSystem()
deaths2 = cfr * system2.timeSeries.recovered_with_immunity
plt.plot(system2.timeSeries.timeVector,
deaths2,
label='deaths if R0=1 at day=%d' % (tInt2 + delayModel))
system3 = simple_sir.SimpleSIR(pop,
1,
0,
beta0,
1 / gamma0,
nSteps,
t0=delayModel)
system3.evolveSystem()
deaths3 = cfr * system3.timeSeries.recovered_with_immunity
plt.plot(system3.timeSeries.timeVector, deaths3, label='deaths no action')
plt.plot(deHd, deH, '.-', label='deaths Hubei')
plt.plot(deId, deI, '.-', linewidth=2, markersize=12, label='deaths Italy')
# plt.plot(coHd + delayModel, coH, '.-', label='confirmed Hubei')
# plt.plot(coId + delayModel, coI, '.-', label='confirmed Italy')
plt.ylim(10, )
plt.xlim(20, 75)
plt.xlabel(strdates)
plt.legend()
def delays():
delayI = 36
delayR = 34
delayH = 0
delayF = 45
delayS = 45
delayK = 35
strdates = 'Days (Hubei from Jan1, Italy from Feb6)'
deH, coH, reH, daH = _restoreCountry('Hubei/China', delayH)
deI, coI, reI, daI = _restoreCountry('Italy', delayI)
# deF, coF, reF, daF = _restoreCountry('France', delayF)
# deR, coR, reR, daR = _restoreCountry('Iran (Islamic Republic of)', delayR)
# deS, coS, reS, daS = _restoreCountry('Spain', delayS)
# deK, c*K, reK, daK = _restoreCountry('Republic of Korea', delayK)
pop = 6e7
nSteps = 100
beta0 = 0.48
gamma0 = 0.16
epsilon = 0.005
cfr = 0.0005
delayModel = -14
system = simple_sir.SimpleSIR(pop,
1,
0,
beta0,
1 / gamma0,
nSteps,
t0=delayModel)
system.evolveSystem()
system.plot()
dS = np.append(np.diff(system.timeSeries.susceptibles), 0)
dC = -epsilon * dS
C = np.cumsum(dC)
system.timeSeries.confirmed = C
plt.plot(system.timeSeries.timeVector,
system.timeSeries.confirmed,
label='confirmed eps=%g' % epsilon)
deaths = cfr * system.timeSeries.recovered_with_immunity
plt.plot(system.timeSeries.timeVector, deaths, label='deaths cfr=%g' % cfr)
plt.plot(daH, coH, '.-', label='confirmed Hubei')
plt.plot(daH, deH, '.-', label='deaths Hubei')
plt.plot(daI, coI, '.-', label='confirmed Italy')
plt.plot(daI, deI, '.-', label='deaths Italy')
# plt.plot(daK, c*K, '.-', label='confirmed Korea')
# plt.plot(daK, deK, '.-', label='deaths Korea')
plt.ylim(1, )
plt.xlim(10, 75)
plt.xlabel(strdates)
plt.legend()
alpha = cfr * system._gamma
ncdpi = (system._beta - system._gamma)
tauRI = -np.log(system.basicReproductionNumber - 1) / ncdpi
tauMI = np.log(alpha / (system._beta - system._gamma)) / ncdpi
tauMR = np.log(alpha / system._gamma) / ncdpi
tauCI = np.log(system._beta * epsilon / ncdpi) / ncdpi
tauCM = tauCI - tauMI
def main():
delayI2H = 36
# deH, coH, reH, daH = restoreCovid('Hubei')
# deI, coI, reI, daI = restoreCovid('Italy', fromItem=syncI2H)
de = restore_data.restoreCSSEDeath()
deH = de['Hubei/Mainland China'].values
deHd = de['Hubei/Mainland China'].days
deI = de['Italy'].values
deId = de['Italy'].days
co = restore_data.restoreCSSEConfirmed()
coH = co['Hubei/Mainland China'].values
coHd = co['Hubei/Mainland China'].days
coI = co['Italy'].values
coId = co['Italy'].days
plt.figure()
plt.plot(deId - delayI2H, deI, '.-', label='Deaths Italy')
plt.plot(deHd, deH, '.-', label='Deaths Hubei')
t = "Evolution of deaths in Hubei and Italy (both 60M people)"
plt.title(t)
plt.legend()
plt.grid(True)
plt.ylabel('Total deaths')
plt.xlabel('Days (H from Jan1, I from Feb27)')
plt.show()
plt.semilogy()
co2de = 0.04
delay_co2de = 2
ff = np.ones(int(delay_co2de)) / delay_co2de
expDeH = co2de * convolve(coH, ff, mode='full')[:len(coH)]
expDeI = co2de * convolve(coI, ff, mode='full')[:len(coI)]
plt.figure()
plt.plot(deId - delayI2H, deI, '.-', label='Deaths Italy')
plt.plot(deHd, deH, '.-', label='Deaths Hubei')
plt.plot(coId - delayI2H, coI, '.-', label='Confirmed Italy')
plt.plot(coHd, coH, '.-', label='Confirmed Hubei')
plt.plot(coId - delayI2H, expDeI, label='Expected Deaths Italy')
plt.plot(coHd, expDeH, label='Expected Deaths Hubei')
t = "Confirmed 2 Dead\n co2de=%g, delay=%g d" % (co2de, delay_co2de)
plt.title(t)
plt.legend()
plt.grid(True)
plt.ylabel('Total deaths')
plt.xlabel('Days (Hubei from Jan 1, I from Feb 27)')
plt.ylim(1, )
plt.show()
plt.semilogy()
pop = 6e7
nSteps = 100
beta = np.zeros(nSteps)
beta[:36] = 0.4
beta[36:] = 0.1
system = simple_sir.SimpleSIR(pop, 1, 0, beta, 15, nSteps)
system.evolveSystem()
system.plot()
cfr = 0.03
deaths = cfr * system.timeSeries.recovered_with_immunity
plt.plot(deaths, label='deaths (CFR=%g)' % (cfr))
plt.plot(deHd + 6, deH, '.-', label='deaths Hubei')
plt.plot(deId - delayI2H + 6, deI, '.-', label='deaths Italy')
plt.plot(coHd + 6, coH, '.-', label='confirmed Hubei')
plt.plot(coId - delayI2H + 6, coI, '.-', label='confirmed Italy')
plt.ylim(1, )
plt.legend()
def main():
delayI = 36
delayR = 34
delayH = 0
de = restore_data.restoreCSSEDeath()
deH = de['Hubei/Mainland China'].values
deHd = de['Hubei/Mainland China'].days - delayH
deI = de['Italy'].values
deId = de['Italy'].days - delayI
deR = de['Iran'].values
deRd = de['Iran'].days - delayR
co = restore_data.restoreCSSEConfirmed()
coH = co['Hubei/Mainland China'].values
coHd = co['Hubei/Mainland China'].days - delayH
coI = co['Italy'].values
coId = co['Italy'].days - delayI
coR = co['Iran'].values
coRd = co['Iran'].days - delayR
plt.figure()
plt.plot(deId, deI, '.-', label='Deaths Italy')
plt.plot(deRd, deR, '.-', label='Deaths Iran')
plt.plot(deHd, deH, '.-', label='Deaths Hubei')
t = "Evolution of deaths in Hubei, Italy, Iran"
plt.title(t)
plt.legend()
plt.grid(True)
plt.ylabel('Total deaths')
strdates = 'Days (H from Jan1, I from Feb6, Ir from Feb4)'
plt.xlabel(strdates)
plt.show()
plt.semilogy()
co2de = 0.04
delay_co2de = 2
ff = np.ones(int(delay_co2de)) / delay_co2de
expDeH = co2de * convolve(coH, ff, mode='full')[:len(coH)]
expDeI = co2de * convolve(coI, ff, mode='full')[:len(coI)]
expDeR = co2de * convolve(coR, ff, mode='full')[:len(coR)]
plt.figure()
plt.plot(deId, deI, '.-', label='Deaths Italy')
plt.plot(deRd, deR, '.-', label='Deaths Iran')
plt.plot(deHd, deH, '.-', label='Deaths Hubei')
plt.plot(coId, coI, '.-', label='Confirmed Italy')
plt.plot(coRd, coR, '.-', label='Confirmed Iran')
plt.plot(coHd, coH, '.-', label='Confirmed Hubei')
plt.plot(coId, expDeI, label='Expected Deaths Italy')
plt.plot(coRd, expDeR, label='Expected Deaths Iran')
plt.plot(coHd, expDeH, label='Expected Deaths Hubei')
t = "Confirmed 2 Dead\n co2de=%g, delay=%g d" % (co2de, delay_co2de)
plt.title(t)
plt.legend()
plt.grid(True)
plt.ylabel('Total deaths')
plt.xlabel(strdates)
plt.ylim(1, )
plt.show()
plt.semilogy()
pop = 6e7
nSteps = 100
beta = np.zeros(nSteps)
beta[:36] = 0.4
beta[36:] = 0.1
system = simple_sir.SimpleSIR(pop, 1, 0, beta, 15, nSteps)
system.evolveSystem()
system.plot()
cfr = 0.03
deaths = cfr * system.timeSeries.recovered_with_immunity
plt.plot(deaths, label='deaths (CFR=%g)' % (cfr))
plt.plot(deHd + 6, deH, '.-', label='deaths Hubei')
plt.plot(deId + 6, deI, '.-', label='deaths Italy')
plt.plot(deRd + 6, deR, '.-', label='deaths Iran')
plt.plot(coHd + 6, coH, '.-', label='confirmed Hubei')
plt.plot(coId + 6, coI, '.-', label='confirmed Italy')
plt.plot(coRd + 6, coR, '.-', label='confirmed Iran')
plt.ylim(1, )
plt.legend()
def main():
delayI = 36
delayR = 34
delayH = 0
strdates = 'Days (Hubei from Jan1, Italy from Feb6)'
de = restore_data.restoreCSSEDeath()
deH = de['Hubei/Mainland China'].values
deHd = de['Hubei/Mainland China'].days - delayH
deI = de['Italy'].values
deId = de['Italy'].days - delayI
deR = de['Iran'].values
deRd = de['Iran'].days - delayR
co = restore_data.restoreCSSEConfirmed()
coH = co['Hubei/Mainland China'].values
coHd = co['Hubei/Mainland China'].days - delayH
coI = co['Italy'].values
coId = co['Italy'].days - delayI
coR = co['Iran'].values
coRd = co['Iran'].days - delayR
plt.figure()
plt.plot(deId, deI, '.-', label='Deaths Italy')
plt.plot(deRd, deR, '.-', label='Deaths Iran')
plt.plot(deHd, deH, '.-', label='Deaths Hubei')
t = "Evolution of deaths in Hubei, Italy, Iran"
plt.title(t)
plt.legend()
plt.grid(True)
plt.ylabel('Total deaths')
plt.xlabel(strdates)
plt.show()
plt.semilogy()
pop = 6e7
nSteps = 100
cfr = 0.03
delayModel = -3
beta = np.zeros(nSteps)
tInt1 = 33
beta[:tInt1] = 0.481
beta[tInt1:] = 0.192
gamma = 0.192
system = simple_sir.SimpleSIR(pop,
1,
0,
beta,
1 / gamma,
nSteps,
t0=delayModel)
system.evolveSystem()
system.plot(susceptibles=False, infectious=False, recovered=False)
deaths = cfr * system.timeSeries.recovered_with_immunity
plt.plot(system.timeSeries.timeVector,
deaths,
label='deaths if R0=1 at day=%d' % (tInt1 + delayModel))
beta2 = np.zeros(nSteps)
tInt2 = 40
beta2[:tInt2] = 0.481
beta2[tInt2:] = 0.192
gamma2 = 0.192
system2 = simple_sir.SimpleSIR(pop,
1,
0,
beta2,
1 / gamma2,
nSteps,
t0=delayModel)
system2.evolveSystem()
deaths2 = cfr * system2.timeSeries.recovered_with_immunity
plt.plot(system2.timeSeries.timeVector,
deaths2,
label='deaths if R0=1 at day=%d' % (tInt2 + delayModel))
beta3 = 0.481
gamma3 = 0.192
system3 = simple_sir.SimpleSIR(pop,
1,
0,
beta3,
1 / gamma3,
nSteps,
t0=delayModel)
system3.evolveSystem()
deaths3 = cfr * system3.timeSeries.recovered_with_immunity
plt.plot(system3.timeSeries.timeVector, deaths3, label='deaths no action')
plt.plot(deHd, deH, '.-', label='deaths Hubei')
plt.plot(deId, deI, '.-', linewidth=2, markersize=12, label='deaths Italy')
# plt.plot(coHd + delayModel, coH, '.-', label='confirmed Hubei')
# plt.plot(coId + delayModel, coI, '.-', label='confirmed Italy')
plt.ylim(10, )
plt.xlim(20, 75)
plt.xlabel(strdates)
plt.legend()
def plotGio():
delayI = 36
delayR = 34
delayH = 0
delayF = 45
delayS = 45
delayK = 35
strdates = 'Days (Hub +Jan1, I +Feb6, F +Feb15, S +Feb15)'
deH, coH, reH, daH = _restoreCountry('Hubei/China', delayH)
deI, coI, reI, daI = _restoreCountry('Italy', delayI)
deF, coF, reF, daF = _restoreCountry('France/France', delayF)
# deR, coR, reR, daR = _restoreCountry('Iran', delayR)
deS, coS, reS, daS = _restoreCountry('Spain', delayS)
# deK, c*K, reK, daK = _restoreCountry('Korea, South', delayK)
pop = 6e7
nSteps = 100
cfr = 0.03
delayModel = -1
beta0 = 0.481
gamma0 = 0.16
beta = np.zeros(nSteps)
tInt1 = 29 - delayModel
beta[:tInt1] = beta0
beta[tInt1:] = gamma0
system = simple_sir.SimpleSIR(pop,
1,
0,
beta,
1 / gamma0,
nSteps,
t0=delayModel)
system.evolveSystem()
system.plot(susceptibles=False, infectious=False, recovered=False)
deaths = cfr * system.timeSeries.recovered_with_immunity
plt.plot(system.timeSeries.timeVector,
deaths,
label='deaths if R0=1 at day=%d' % (tInt1 + delayModel))
beta2 = np.zeros(nSteps)
tInt2 = 32 - delayModel
beta2[:tInt2] = beta0
beta2[tInt2:] = gamma0
system2 = simple_sir.SimpleSIR(pop,
1,
0,
beta2,
1 / gamma0,
nSteps,
t0=delayModel)
system2.evolveSystem()
deaths2 = cfr * system2.timeSeries.recovered_with_immunity
plt.plot(system2.timeSeries.timeVector,
deaths2,
label='deaths if R0=1 at day=%d' % (tInt2 + delayModel))
beta4 = np.zeros(nSteps)
beta4[:tInt2] = beta0
beta4[tInt2:] = 0 * gamma0
system4 = simple_sir.SimpleSIR(pop,
1,
0,
beta4,
1 / gamma0,
nSteps,
t0=delayModel)
system4.evolveSystem()
deaths4 = cfr * system4.timeSeries.recovered_with_immunity
plt.plot(system4.timeSeries.timeVector,
deaths4,
label='deaths if R0=0 at day=%d' % (tInt2 + delayModel))
system3 = simple_sir.SimpleSIR(pop,
1,
0,
beta0,
1 / gamma0,
nSteps,
t0=delayModel)
system3.evolveSystem()
deaths3 = cfr * system3.timeSeries.recovered_with_immunity
plt.plot(system3.timeSeries.timeVector, deaths3, label='deaths no action')
plt.plot(daH, deH, '.-', label='deaths Hubei')
plt.plot(daI, deI, '.-', linewidth=2, markersize=12, 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')
plt.ylim(10, 1e5)
plt.xlim(20, 70)
plt.xlabel(strdates)
plt.legend()
def plotLapo():
delayI = 36
delayR = 34
delayH = 0
delayF = 45
delayS = 45
delayK = 35
strdates = 'Days (Hub +Jan1, I +Feb6, F +Feb15, S +Feb15)'
deH, coH, reH, daH = _restoreCountry('Hubei/China', delayH)
deI, coI, reI, daI = _restoreCountry('Italy', delayI)
# deF, coF, reF, daF = _restoreCountry('France/France', delayF)
# deR, coR, reR, daR = _restoreCountry('Iran', delayR)
# deS, coS, reS, daS = _restoreCountry('Spain', delayS)
# deK, c*K, reK, daK = _restoreCountry('Korea, South', delayK)
pop = 6e7
nSteps = 200
cfr = 0.03
delayModel = -1
beta0 = 0.481
gamma0 = 0.16
lockDownTime = 60
beta2 = np.zeros(nSteps)
tInt2 = 32 - delayModel
beta2[:tInt2] = beta0
beta2[tInt2:tInt2 + lockDownTime] = 1.0 * gamma0
beta2[tInt2 + lockDownTime:] = beta0
system2 = simple_sir.SimpleSIR(pop,
1,
0,
beta2,
1 / gamma0,
nSteps,
t0=delayModel)
system2.evolveSystem()
# system2.plot(susceptibles=False, infectious=False, recovered=False)
system2.plot()
deaths2 = cfr * system2.timeSeries.recovered_with_immunity
plt.plot(system2.timeSeries.timeVector,
deaths2,
label='deaths if R0=1.0 from day %d to %d ' %
(tInt2 + delayModel, tInt2 + lockDownTime + delayModel))
# beta4 = np.zeros(nSteps)
# beta4[:tInt2] = beta0
# beta4[tInt2:tInt2 + lockDownTime] = 0
# beta4[tInt2 + lockDownTime:] = beta0
# system4 = simple_sir.SimpleSIR(pop, 1, 0, beta4, 1 / gamma0,
# nSteps, t0=delayModel)
# system4.evolveSystem()
# system4.plot(susceptibles=False, infectious=True, recovered=False, newFigure=False)
# deaths4 = cfr * system4.timeSeries.recovered_with_immunity
# plt.plot(system4.timeSeries.timeVector,
# deaths4,
# label='deaths if R0=0 from day %d to %d ' % (
# tInt2 + delayModel, tInt2 + lockDownTime + delayModel))
system3 = simple_sir.SimpleSIR(pop,
1,
0,
beta0,
1 / gamma0,
nSteps,
t0=delayModel)
system3.evolveSystem()
system3.plot(susceptibles=False,
infectious=True,
recovered=False,
newFigure=False)
deaths3 = cfr * system3.timeSeries.recovered_with_immunity
plt.plot(system3.timeSeries.timeVector, deaths3, label='deaths no action')
# plt.plot(daH, deH, '.-', label='deaths Hubei')
# plt.plot(daI, deI, '.-',
# linewidth=2,
# markersize=12,
# label='deaths Italy')
plt.ylim(10, )
plt.xlim(20, )
plt.xlabel(strdates)
plt.legend()