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 _restoreCountry(name, delay_date):
de = restore_data.restoreCSSEDeath()
co = restore_data.restoreCSSEConfirmed()
re = restore_data.restoreCSSERecovered()
assert np.array_equal(de[name].days, co[name].days)
assert np.array_equal(de[name].days, re[name].days)
deaths = de[name].values
confirmed = co[name].values
recovered = re[name].values
days = de[name].days - delay_date
return deaths, confirmed, recovered, days
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
re = restore_data.restoreCSSERecovered()
reH = re['Hubei/Mainland China'].values
reHd = re['Hubei/Mainland China'].days - delayH
reI = re['Italy'].values
reId = re['Italy'].days - delayI
reR = re['Iran'].values
reRd = re['Iran'].days - delayR
plt.figure()
plt.plot(deHd, deH, '.-', label='Deaths Hubei')
plt.plot(coHd, coH, '.-', label='Confirmed Hubei')
plt.plot(reHd, reH, '.-', label='Recovered Hubei')
plt.grid(True)
plt.legend()
plt.xlabel(strdates)
plt.show()
plt.semilogy()
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 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()
