def slider_app_bokeh(start_date=datetime(2020, 3, 1),
lock_date=datetime(2020, 3, 26),
post_lock_date=datetime(2020, 4, 26),
init_rho=3.7,
lock_rho=0.7,
post_lock_rho=1.6,
init_inf=61,
conf_case_delay=7,
init_exp=0,
init_rec=0,
mort_rate=0.013,
symp_2_death=14,
MaxDays=500,
tau=8,
k=0.5,
nu=1.7,
mu=5.1,
model='NBD_SEIR',
county='El Paso'):
models = {'SIR_nu': 0, 'SEIR_nu': 1, 'NBD_SEIR': 2}
model_text = {
'SIR_nu':
'Simple S-I-R Infection Model with \n' +
'Heterogeneous Mixing Simulated using \n' +
'a Power-Law Scaling Term ('
'nu'
')',
'SEIR_nu':
'S-E-I-R Infection Model with \n' +
'Heterogeneous Mixing Simulated using \n' +
'a Power-Law Scaling Term ('
'nu'
')',
'NBD_SEIR':
'S-E-I-R Infection Model with \n' +
'Heterogeneous Mixing Simulated using \n' +
'Negative Binomially-Distributed \n' + 'Infection Events (PDF shape '
'k'
')'
}
t = 7 / 24
today = datetime.now()
pct_tst = 0.15
global zm_toggle, legend_toggle
zm_toggle = -1
legend_toggle = 1
rho_sched = {lock_date: lock_rho, post_lock_date: post_lock_rho}
lock_time = (lock_date-start_date).days + \
(lock_date-start_date).seconds/86400
post_lock_time = (post_lock_date-start_date).days + \
(post_lock_date-start_date).seconds/86400
if model == 'SIR_nu':
I_ind = 2
R_ind = 3
D_ind = 4
out = SIR_nu(start_date,
P=720403,
I=init_inf,
R=0,
rho=init_rho,
tau=tau,
nu=1.7,
MaxDays=MaxDays,
suppress_output=1,
rho_sched=rho_sched,
mort_rate=mort_rate,
symp_2_death=symp_2_death,
t=t)
elif model == 'SEIR_nu':
I_ind = 3
R_ind = 4
D_ind = 5
out = SEIR_nu(start_date,
P=720403,
E=0,
I=init_inf,
R=0,
mu=mu,
rho=init_rho,
tau=tau,
nu=1.7,
MaxDays=MaxDays,
suppress_output=1,
rho_sched=rho_sched,
mort_rate=mort_rate,
symp_2_death=symp_2_death,
t=t)
elif model == 'NBD_SEIR':
I_ind = 3
R_ind = 4
D_ind = 6
out = NBD_SEIR(start_date,
720403,
0,
init_inf,
0,
init_rho,
tau,
k,
mu,
county,
MaxDays,
suppress_output=1,
rho_sched=rho_sched,
t=t,
mort_rate=mort_rate,
symp_2_death=symp_2_death)
C = int(tau / t)
max_ind = int((today - start_date).days / t)
T = out[0]
I = out[I_ind]
R = out[R_ind]
D = out[D_ind]
T1 = T
T2 = T[0:max_ind]
T3 = T[0:-C]
CC1 = array(R[C:max_ind + C])
CC1p = pct_tst * CC1
CC2 = array(R[C:])
CC2p = pct_tst * CC2
[death_times, case_times, deaths,
cases] = dataHandler(start_date, conf_case_delay)
# def days_2_ms(T):
# Dt = []
# for i in T:
# Dt.append(i*24*3600*1000)
# return Dt
# Dt1 = days_2_ms(T1)
# Dt2 = days_2_ms(T2)
# Dt3 = days_2_ms(T3)
# DTd = days_2_ms(death_times)
# DTc = days_2_ms(case_times)
# Set up data: ---
# source1 = ColumnDataSource(data=dict(T1=Dt1, I=I, D=D))
# source2 = ColumnDataSource(data=dict(T2=Dt2, CC1=CC1,CC1p=CC1p))
# source3 = ColumnDataSource(data=dict(T3=Dt3, CC2=CC2, CC2p=CC2p))
# scatter_source1 = ColumnDataSource(data=dict(Td=DTd, d=deaths))
# scatter_source2 = ColumnDataSource(data=dict(Tc=DTc, c=cases))
source1 = ColumnDataSource(data=dict(T1=T1, I=I, D=D))
source2 = ColumnDataSource(data=dict(T2=T2, CC1=CC1, CC1p=CC1p))
source3 = ColumnDataSource(data=dict(T3=T3, CC2=CC2, CC2p=CC2p))
scatter_source1 = ColumnDataSource(data=dict(Td=death_times, d=deaths))
scatter_source2 = ColumnDataSource(data=dict(Tc=case_times, c=cases))
# Set up plots: ---
# Top-left plot
plot1 = figure(
plot_height=400,
plot_width=400,
title="Predicted vs. Reported Deaths in " + county + " County",
tools="crosshair,pan,reset,save,wheel_zoom", # x_axis_type='datetime',
x_range=[0, T[max_ind]],
y_range=[0, max(deaths)])
plot1.line('T1'[0:max_ind],
'D'[0:max_ind],
source=source1,
line_color='red',
line_width=3,
line_alpha=0.6,
muted_alpha=0.2,
legend_label='Predicted Cumulative Deaths')
plot1.circle('Td',
'd',
source=scatter_source1,
line_color='red',
muted_alpha=0.2,
legend_label='Reported Deaths in ' + county + ' County')
plot1.legend.location = 'top_left'
plot1.legend.click_policy = 'mute'
plot1.xaxis.ticker = [0, 31, 61]
plot1.xaxis.major_label_overrides = {0: 'Mar', 31: 'Apr', 61: 'May'}
# Top-right plot
plot2 = figure(
plot_height=400,
plot_width=400,
title="Predicted Deaths over the Long Term",
tools="crosshair,pan,reset,save,wheel_zoom", #x_axis_type='datetime',
x_range=[0, MaxDays],
y_range=[0, max(D)])
plot2.line('T1',
'D',
source=source1,
line_color='red',
line_width=3,
line_alpha=0.6,
muted_alpha=0.2,
legend_label='Predicted Cumulative Deaths')
plot2.circle('Td',
'd',
source=scatter_source1,
line_color='red',
muted_alpha=0.2,
legend_label='Reported Deaths in' + county + ' County')
plot2.legend.location = 'top_left'
plot2.legend.click_policy = 'mute'
plot2.xaxis.ticker = [0, 31, 61, 92, 122, 153, 183, 214, 245, 275]
plot2.xaxis.major_label_overrides = {
0: 'Mar',
31: 'Apr',
61: 'May',
92: 'Jun',
122: 'Jul',
153: 'Aug',
183: 'Sep',
214: 'Oct',
245: 'Nov',
275: 'Dec'
}
# Bottom-left plot
plot3 = figure(
plot_height=400,
plot_width=400,
title="Predicted vs. Reported Infectious in El Paso County",
tools="crosshair,pan,reset,save,wheel_zoom", #x_axis_type='datetime',
x_range=[0, T[max_ind]],
y_range=[0, R[max_ind]])
plot3.line('T1'[0:max_ind],
'I'[0:max_ind],
source=source1,
line_color='blue',
line_width=3,
line_alpha=0.6,
muted_alpha=0.2,
legend_label='Predicted Infectious Population')
plot3.line('T2',
'CC1',
source=source2,
line_color='green',
line_width=3,
line_alpha=0.6,
muted_alpha=0.2,
legend_label='Predicted Cumulative Cases')
plot3.line(
'T2',
'CC1p',
source=source2,
line_color='green',
line_dash='dotted',
line_width=3,
line_alpha=0.6,
muted_alpha=0.2,
legend_label='Predicted Confirmed Cases based on Tested Percentage')
plot3.circle('Tc',
'c',
source=scatter_source2,
line_color='blue',
muted_alpha=0.2,
legend_label='Reported Confirmed Cases in ' + county +
' County')
plot3.legend.location = 'top_left'
plot3.legend.click_policy = 'mute'
plot3.xaxis.ticker = [0, 31, 61]
plot3.xaxis.major_label_overrides = {0: 'Mar', 31: 'Apr', 61: 'May'}
# Bottom-right plot
plot4 = figure(
plot_height=400,
plot_width=400,
title="Predicted Infectious over the Long Term",
tools="crosshair,pan,reset,save,wheel_zoom", #x_axis_type='datetime',
x_range=[0, MaxDays],
y_range=[0, max(R)])
plot4.line('T1',
'I',
source=source1,
line_color='blue',
line_width=3,
line_alpha=0.6,
muted_alpha=0.2,
legend_label='Predicted Infectious Population')
plot4.line('T3',
'CC2',
source=source3,
line_color='green',
line_width=3,
line_alpha=0.6,
muted_alpha=0.2,
legend_label='Predicted Cumulative Cases')
plot4.line(
'T3',
'CC2p',
source=source3,
line_color='green',
line_dash='dotted',
line_width=3,
line_alpha=0.6,
muted_alpha=0.2,
legend_label='Predicted Confirmed Cases based on Tested Percentage')
plot4.circle('Tc',
'c',
source=scatter_source2,
line_color='blue',
muted_alpha=0.2,
legend_label='Reported Confirmed Cases in ' + county +
' County')
plot4.legend.location = 'top_left'
plot4.legend.click_policy = 'mute'
plot4.xaxis.ticker = [0, 31, 61, 92, 122, 153, 183, 214, 245, 275]
plot4.xaxis.major_label_overrides = {
0: 'Mar',
31: 'Apr',
61: 'May',
92: 'Jun',
122: 'Jul',
153: 'Aug',
183: 'Sep',
214: 'Oct',
245: 'Nov',
275: 'Dec'
}
# Set up widgets
delta = 0.1
sliders = []
text = TextInput(title="title", value='my sine wave')
s_init_rho = Slider(title="Initial R_0",
value=init_rho,
start=0.1,
end=6.0,
step=delta / 10)
sliders.append(s_init_rho)
s_lock_rho = Slider(title="Stay-at-home R_0",
value=lock_rho,
start=0.1,
end=6.0,
step=delta / 10)
sliders.append(s_lock_rho)
s_post_lock_rho = Slider(title="R_0 after End of Stay-at-home order",
value=post_lock_rho,
start=0.1,
end=6.0,
step=delta / 10)
sliders.append(s_post_lock_rho)
s_post_lock_time = Slider(
title="End of Stay-at-home posture (Days after 1 Mar)",
value=post_lock_time,
start=50,
end=200,
step=1)
sliders.append(s_post_lock_time)
s_init_inf = Slider(title="Initial Infectious",
value=init_inf,
start=1,
end=100,
step=1)
sliders.append(s_init_inf)
s_init_exp = Slider(title="Initial Exposed",
value=init_exp,
start=0,
end=100,
step=1)
sliders.append(s_init_exp)
s_init_rec = Slider(title="Initial Recovered",
value=init_rec,
start=0,
end=100,
step=1)
sliders.append(s_init_rec)
s_pct_tst = Slider(
title="Percent of Infected Population Tested & Positive",
value=pct_tst * 100,
start=1,
end=100,
step=delta)
sliders.append(s_pct_tst)
s_tau = Slider(title="Infectious Period (days)",
value=tau,
start=2,
end=14,
step=delta)
sliders.append(s_tau)
s_mu = Slider(title="Incubation Period (days)",
value=mu,
start=0,
end=7,
step=delta)
sliders.append(s_mu)
if model == 'SIR_nu' or model == 'SEIR_nu':
s_het = Slider(title="Power-Law Heterogeneity",
value=nu,
start=1.0,
end=3.3,
step=delta)
elif model == 'NBD_SEIR':
s_het = Slider(title="NBD Homogeneity",
value=k,
start=0.001,
end=5,
step=0.01)
sliders.append(s_het)
s_mort_rate = Slider(title="Mortality Rate (%)",
value=mort_rate * 100,
start=0.1,
end=10,
step=delta)
sliders.append(s_mort_rate)
s_symp_2_death = Slider(title="Avg Delay from Symptoms until Death (days)",
value=symp_2_death,
start=3,
end=20,
step=delta)
sliders.append(s_symp_2_death)
s_conf_case_delay = Slider(title="Avg Delay from Test Processing (days)",
value=conf_case_delay,
start=0,
end=14,
step=delta)
sliders.append(s_conf_case_delay)
s_t = Slider(title="Timestep (minutes)",
value=t * (60 * 24),
start=15,
end=60 * 24,
step=5)
sliders.append(s_t)
# Set up callbacks
def update_title(attrname, old, new):
plot1.title.text = text.value
#text.on_change('value', update_title)
def update_data(attrname, old, new):
# Get the current slider values
init_rho = s_init_rho.value
lock_rho = s_lock_rho.value
post_lock_rho = s_post_lock_rho.value
post_lock_time = s_post_lock_time.value
post_lock_date = start_date + timedelta(days=post_lock_time)
init_inf = s_init_inf.value
init_exp = s_init_exp.value
init_red = s_init_rec.value
pct_tst = s_pct_tst.value / 100
tau = s_tau.value
mu = s_mu.value
if model_sel.active in [0, 1]: # Power law models (nu)
nu = s_het.value
elif model_sel.active == 2: # NBD model (k)
k = s_het.value
mort_rate = s_mort_rate.value / 100
symp_2_death = s_symp_2_death.value
conf_case_delay = s_conf_case_delay.value
t = s_t.value / (60 * 24)
max_ind = int((today - start_date).days / t)
[death_times, case_times, deaths,
cases] = dataHandler(start_date, conf_case_delay)
rho_sched = {lock_date: lock_rho, post_lock_date: post_lock_rho}
if model_sel.active == 0: # SIR_nu
I_ind = 2
R_ind = 3
D_ind = 4
out2 = SIR_nu(start_date,
P=720403,
I=init_inf,
R=init_rec,
rho=init_rho,
tau=tau,
nu=nu,
MaxDays=MaxDays,
suppress_output=1,
rho_sched=rho_sched,
mort_rate=mort_rate,
symp_2_death=symp_2_death,
t=t)
elif model_sel.active == 1: # SEIR_nu
I_ind = 3
R_ind = 4
D_ind = 5
out2 = SEIR_nu(start_date,
P=720403,
E=init_exp,
I=init_inf,
R=init_rec,
mu=mu,
rho=init_rho,
tau=tau,
nu=nu,
MaxDays=MaxDays,
suppress_output=1,
rho_sched=rho_sched,
mort_rate=mort_rate,
symp_2_death=symp_2_death,
t=t)
elif model_sel.active == 2: # NBD_SEIR
I_ind = 3
R_ind = 4
D_ind = 6
out2 = NBD_SEIR(start_date,
720403,
init_exp,
init_inf,
init_rec,
init_rho,
tau,
k,
mu,
county,
MaxDays,
suppress_output=1,
rho_sched=rho_sched,
mort_rate=mort_rate,
symp_2_death=symp_2_death,
t=t)
T = out2[0]
I = out2[I_ind]
R = out2[R_ind]
D = out2[D_ind]
T1 = T
T2 = T[0:max_ind]
T3 = T[0:-C]
CC1 = array(R[C:max_ind + C])
CC1p = pct_tst * CC1
CC2 = array(R[C:])
CC2p = pct_tst * CC2
[death_times, case_times, deaths,
cases] = dataHandler(start_date, conf_case_delay)
plot1.y_range.start = 0
plot1.y_range.end = max(deaths)
plot2.y_range.start = 0
plot2.y_range.end = max(D)
if zm_toggle == -1:
plot3.y_range.start = 0
plot3.y_range.end = R[max_ind]
plot4.y_range.start = 0
plot4.y_range.end = max(R)
# Generate the new curve
source1.data = dict(T1=T1, I=I, D=D)
source2.data = dict(T2=T2, CC1=CC1, CC1p=CC1p)
source3.data = dict(T3=T3, CC2=CC2, CC2p=CC2p)
scatter_source1.data = dict(Td=death_times, d=deaths)
scatter_source2.data = dict(Tc=case_times, c=cases)
def update_radio_buttons(attrname, old, new):
if model_sel.active in [0, 1]:
s_het.title = "Power-Law Heterogeneity"
s_het.value = nu
s_het.start = 1.0
s_het.end = 3.3
s_het.step = delta
elif model_sel.active == 2:
s_het.title = "NBD Heterogeneity"
s_het.value = k
s_het.start = 0.0001
s_het.end = 5
s_het.step = delta / 10
update_data(attrname, old, new)
def zm_button_event(event):
global zm_toggle
zm_toggle = -zm_toggle
if zm_toggle == 1:
plot3.y_range.start = 0
plot3.y_range.end = max(cases)
elif zm_toggle == -1:
plot3.y_range.start = 0
plot3.y_range.end = R[max_ind]
def legend_button_event(event):
global legend_toggle
legend_toggle = -legend_toggle
if legend_toggle == 1:
plot1.legend.visible = True
plot2.legend.visible = True
plot3.legend.visible = True
plot4.legend.visible = True
elif legend_toggle == -1:
plot1.legend.visible = False
plot2.legend.visible = False
plot3.legend.visible = False
plot4.legend.visible = False
for s in sliders:
s.on_change('value', update_data)
model_sel = RadioButtonGroup(labels=['SIR-nu', 'SEIR-nu', 'NBD_SEIR'],
active=2)
model_sel.on_change('active', update_radio_buttons)
zm_button = Button(label='Zoom to Confirmed Cases', button_type='success')
zm_button.on_event(ButtonClick, zm_button_event)
legend_button = Button(label='Hide Plot Legends', button_type='success')
legend_button.on_event(ButtonClick, legend_button_event)
# Set up layouts and add to document
inputs = column(
model_sel,
s_init_rho,
s_lock_rho,
s_post_lock_rho,
s_post_lock_time,
s_init_inf,
s_init_exp,
s_init_rec,
s_pct_tst,
s_tau,
s_mu,
s_het, #s_nu, s_k,
s_mort_rate,
s_symp_2_death,
s_conf_case_delay,
s_t)
grid = gridplot([[plot1, plot2], [plot3, plot4]])
middle = column(grid, zm_button)
curdoc().add_root(row(inputs, middle, legend_button, width=800))
curdoc().title = "El Paso County COVID-19 App"
