def update_SIR_figure(country_drop_down):
traces = []
df_plot = df_analyse[df_analyse['country'] == country_drop_down]
df_plot = df_plot[['state', 'country', 'confirmed',
'date']].groupby(['country',
'date']).agg(np.sum).reset_index()
df_plot.sort_values('date', ascending=True).head()
df_plot = df_plot.confirmed[35:]
t, fitted = SIR_modelling(df_plot)
traces.append(
dict(x=t, y=fitted, mode='markers+lines', opacity=8, name='SIR-curve'))
traces.append(
dict(x=t, y=df_plot, mode='lines', opacity=0.9, name='Original-curve'))
return {
'data':
traces,
'layout':
dict(width=2200,
height=800,
title='SIR virus spread model',
xaxis={
'title': 'Days',
'tickangle': -45,
'nticks': 20,
'tickfont': dict(size=14, color="#7f7f7f"),
},
yaxis={'title': "Infected population"})
}
def update_SIR_figure(country_drop_down):
traces = []
df_plot = df_input_large[df_input_large['country'] == country_drop_down]
df_plot = df_plot[[
'state', 'country', 'confirmed', 'confirmed_filtered', 'date'
]].groupby(['country', 'date']).agg(np.sum).reset_index()
df_plot.sort_values('date', ascending=True).head()
df_plot = df_plot.confirmed[40:]
t, fitted = SIR_modelling(df_plot)
traces.append(
dict(x=t,
y=fitted,
mode='markers',
opacity=1.1,
name='SIR-model-curve'))
traces.append(
dict(x=t,
y=df_plot,
mode='lines',
opacity=0.7,
name='Original Data curve'))
return {
'data':
traces,
'layout':
dict(width=1200,
height=700,
title='SIR model fitting',
xaxis={
'title': 'Days',
'tickangle': -48,
'nticks': 21,
'tickfont': dict(size=16, color="#7f7f7f"),
},
yaxis={'title': "Infected population"})
}
def SIR(countries):
SIR_modelling()