from IPython.display import HTML
HTML('''<script>
code_show=true;
function code_toggle() {
if (code_show){
$('div.input').hide();
} else {
$('div.input').show();
}
code_show = !code_show
}
$( document ).ready(code_toggle);
</script>
O código bruto do Notebook é, por padrão, escondido para facilitar a leitura das informações.
Para fazer aparecer/desaparecer o código, só clicar <a href="javascript:code_toggle()">aqui</a>.''')
#!pip install ipywidgets
import ipywidgets as widgets
from IPython.display import Javascript, display
html = widgets.HTMLMath(
value=r"Para rodar as simulações, basta mudar os valores das condições iniciais com os sliders abaixo. Por fim, é importante clicar no botão para que as simulações sejam compiladas",
placeholder='Some HTML',
description='',
)
slider = widgets.FloatSlider(
min=0,
max=10,
step=0.1,
description='\(r_0\)',
value=2.5,
readout=True,
readout_format='.1f',
)
slider1 = widgets.FloatSlider(
min=0,
max=10,
step=0.1,
description='\(T_{inc}\)',
value=5.2,
readout=True,
readout_format='.1f',
)
slider2 = widgets.FloatSlider(
min=0,
max=10,
step=0.1,
description='\(T_{inf}\)',
value=2.9,
readout=True,
readout_format='.1f',
)
checkbox = widgets.Checkbox(
value=False,
description='Deseja que \(r_0\) seja aleatório?',
disabled=False
)
def run_all(ev):
display(Javascript('IPython.notebook.execute_cell_range(IPython.notebook.get_selected_index()+1, IPython.notebook.ncells())'))
button = widgets.Button(description="Rodar as Simulações")
button.on_click(run_all)
display(html)
display(slider,checkbox,slider1, slider2)
display(button)
IPyWidgets are not supported
IPyWidgets are not supported
IPyWidgets are not supported
IPyWidgets are not supported
IPyWidgets are not supported
IPyWidgets are not supported
import numpy as np
#%matplotlib widget
#%matplotlib inline
#import matplotlib.pyplot as plt
#import seaborn; seaborn.set();
r_0=slider.value #número de reprodução basal
T_inc=slider1.value #tempo de incubação
T_inf=slider2.value #tempo de infecção
tf=498 #número final de dias da simulação
dt=1 # passo (1 dia)
t=np.arange(0,tf+0.5,dt) # vetor com todos os dias até o 498º dia
n=len(t) #tamanho do vetor t => número de dias da simulação
S = np.ones(n)*0.999999
E = np.ones(n)*(10**-6)
I = np.zeros(n)
R = np.zeros(n)
def f(y):
a = (r_0/T_inf)*y[0]*y[2]
b = (1/T_inc)*y[1]
c = (1/T_inf)*y[2]
S_ = -a
E_ = (a - b)
I_ = (b - c)
R_ = c
return [S_,E_,I_,R_]
rand=[]
for i in range(1,n):
if checkbox.value:
a=np.random.rand(1)[0]*5
r_0=abs(a*np.cos(2.5*np.pi*t[i]/498))+0.5;rand.append(r_0)
#r_0=np.random.rand(1)[0]*4;rand.append(r_0)
#if i>=50:
# r_0=(2.5+1)/i
y05 = f([S[i-1],E[i-1],I[i-1],R[i-2]])
S_05 = S[i-1] + y05[0]*(dt/2)
E_05 = E[i-1] + y05[1]*(dt/2)
I_05 = I[i-1] + y05[2]*(dt/2)
R_05 = R[i-1] + y05[3]*(dt/2)
y_ = [S_05,E_05,I_05,R_05]
S[i] = S[i-1] + dt*f(y_)[0]
E[i] = E[i-1] + dt*f(y_)[1]
I[i] = I[i-1] + dt*f(y_)[2]
R[i] = R[i-1] + dt*f(y_)[3]
#print(S)
#print(E)
#print(I)
#print(R)
#print(rand)
import plotly.graph_objects as go
from IPython.display import display
from IPython.display import HTML
# Layout
layout = go.Layout(
title="Proporção do modelo SEIR versus tempo",
title_x=0.5,
xaxis=dict(
title="Tempo (dias)"
),
yaxis=dict(
title="Porcentagem da População (%)"
)
)
fig = go.Figure(layout = layout)
# Add scatter trace with medium sized markers
fig.add_trace(
go.Scatter(
mode='lines+markers',
x=t,
y=S,
name='Suscetíveis',
marker=dict(
color='darkturquoise',
size=6,
opacity=0.4,
line=dict(
color='burlywood',
width=1
)
),
showlegend=True
)
)
fig.add_trace(
go.Scatter(
mode='lines+markers',
x=t,
y=E,
name='Expostos',
marker=dict(
color='darkmagenta',
size=6,
opacity=0.4,
line=dict(
color='burlywood',
width=1
)
),
showlegend=True
)
)
fig.add_trace(
go.Scatter(
mode='lines+markers',
x=t,
y=I,
name='Infectados',
marker=dict(
color='red',
size=6,
opacity=0.4,
line=dict(
color='burlywood',
width=1
)
),
showlegend=True
)
)
fig.add_trace(
go.Scatter(
mode='lines+markers',
x=t,
y=R,
name='Recuperados',
marker=dict(
color='yellow',
size=6,
opacity=0.4,
line=dict(
color='burlywood',
width=1
)
),
showlegend=True
)
)
S0 = np.ones(n)
E0 = np.ones(n)
I0 = np.zeros(n)
R0 = np.zeros(n)
L0 = np.zeros(n)
P0 = np.zeros(n)
W0 = np.zeros(n)
for j in range(n):
S0[j] = int(S[j]*44040000)
E0[j] = int(E[j]*44040000)
I0[j] = int(I[j]*44040000)
R0[j] = int(R[j]*44040000)
L0[j] = int((27*44040000)/100000)
if j>=1:
P0[j] = int(abs(R0[j-1]-R0[j])*0.03)
for p in range(n):
if p==0:
W0[p]=P0[p]
elif p==1:
W0[p]=P0[p]+P0[p-1]
elif p==2:
W0[p]=P0[p]+P0[p-1]+P0[p-2]
elif p==3:
W0[p]=P0[p]+P0[p-1]+P0[p-2]+P0[p-3]
elif p==4:
W0[p]=P0[p]+P0[p-1]+P0[p-2]+P0[p-3]+P0[p-4]
elif p==5:
W0[p]=P0[p]+P0[p-1]+P0[p-2]+P0[p-3]+P0[p-4]+P0[p-5]
else:
W0[p]=P0[p]+P0[p-1]+P0[p-2]+P0[p-3]+P0[p-4]+P0[p-5]+P0[p-6]
# Layout
layout = go.Layout(
title="Proporção do modelo SEIR versus tempo",
title_x=0.5,
xaxis=dict(
title="Tempo (dias)"
),
yaxis=dict(
title="Porcentagem da População (%)"
)
)
fig = go.Figure(layout = layout)
# Add scatter trace with medium sized markers
fig.add_trace(
go.Scatter(
mode='lines+markers',
x=t,
y=S0,
name='Suscetíveis',
marker=dict(
color='darkturquoise',
size=6,
opacity=0.4,
line=dict(
color='burlywood',
width=1
)
),
showlegend=True
)
)
fig.add_trace(
go.Scatter(
mode='lines+markers',
x=t,
y=E0,
name='Expostos',
marker=dict(
color='darkmagenta',
size=6,
opacity=0.4,
line=dict(
color='burlywood',
width=1
)
),
showlegend=True
)
)
fig.add_trace(
go.Scatter(
mode='lines+markers',
x=t,
y=I0,
name='Infectados',
marker=dict(
color='red',
size=6,
opacity=0.4,
line=dict(
color='burlywood',
width=1
)
),
showlegend=True
)
)
fig.add_trace(
go.Scatter(
mode='lines+markers',
x=t,
y=R0,
name='Recuperados',
marker=dict(
color='yellow',
size=6,
opacity=0.4,
line=dict(
color='burlywood',
width=1
)
),
showlegend=True
)
)
fig.add_trace(
go.Scatter(
mode='lines+markers',
x=t,
y=L0,
name='Leitos',
marker=dict(
color='orange',
size=6,
opacity=0.4,
line=dict(
color='burlywood',
width=1
)
),
showlegend=True
)
)
fig.add_trace(
go.Scatter(
mode='lines+markers',
x=t,
y=W0,
name='Necessidade de Leitos',
marker=dict(
color='green',
size=6,
opacity=0.4,
line=dict(
color='burlywood',
width=1
)
),
showlegend=True
)
)
fig.add_trace(
go.Scatter(
mode='lines+markers',
x=t,
y=rand,
name='Reprodução basal',
marker=dict(
color='black',
size=6,
opacity=0.4,
line=dict(
color='burlywood',
width=1
)
),
showlegend=True
)
)
pessoas_sem_uti = []
for b in range(len(L0)):
if W0[b]>=L0[b]:
pessoas_sem_uti.append(W0[b]-L0[b])
print("O número de pessoas que ficarão sem UTI é ",'{:,}'.format(sum(pessoas_sem_uti)))
O número de pessoas que ficarão sem UTI é 7,229,088.0