def filtra_obitos_covid(cases, estado):
obitos = cases[(cases.state == estado) & (cases.place_type == 'state')][['date', 'confirmed', 'deaths']]
obitos['data'] = pd.to_datetime(obitos.date)
obitos.set_index('data', inplace=True)
obitos.sort_index(inplace=True)
obitos['incidencia'] = obitos.deaths.diff()
obitos['ew'] = [int(episem.episem(x, out='W')) for x in obitos.index]
return obitos
def plot_excess_deaths(data, estado, only_viral=True):
obitos = data[(data.state == estado) & (data.place_type == 'state')][[
'date', 'Casos Confirmados', 'Mortes Acumuladas'
]]
obitos['data'] = pd.to_datetime(obitos.date)
obitos.set_index('data', inplace=True)
obitos.sort_index(inplace=True)
obitos['incidencia'] = obitos['Mortes Acumuladas'].diff()
obitos['ew'] = [int(episem.episem(x, out='W')) for x in obitos.index]
if only_viral:
ob_sim = pd.read_csv(f'dashboard/dados/baseline_{estado}.csv.gz')
else:
ob_sim = pd.read_csv(
f'dashboard/dados/baseline_{estado}_all_resp.csv.gz')
obitos_W = obitos.groupby('ew').sum().incidencia.iloc[:-1]
# obitos_W.reset_index()
fig = px.line(ob_sim, x=ob_sim.index, y='median', line_shape='spline')
fig.add_scatter(x=ob_sim.index,
y=ob_sim.perc_25,
name='1⁰ quartil',
fill='tonexty',
hovertemplate="1⁰ quartil: %{y:.0f} SE: %{x}")
fig.add_scatter(x=ob_sim.index,
y=ob_sim.perc_75,
name='3⁰ quartil',
fill='tonexty',
hovertemplate="3⁰ quartil: %{y:.0f} SE: %{x}")
fig.add_scatter(x=obitos_W.index.values,
y=obitos_W.values,
fillcolor='red',
marker_symbol=3,
hovertemplate="Mortes por semana: %{y:.0f} SE: %{x}",
name='Mortes por COVID-19')
fig.update_traces(mode='lines+markers')
fig.update_layout(
xaxis_title="Semanas epidemiológicas",
yaxis_title="Mortes",
plot_bgcolor='rgba(0,0,0,0)',
legend_orientation="h",
)
fig.update_xaxes(
showgrid=True,
gridwidth=1,
gridcolor='rgb(211,211,211)',
showline=True,
linewidth=1,
linecolor='black',
)
fig.update_yaxes(
showgrid=True,
gridwidth=1,
gridcolor='rgb(211,211,211)',
showline=True,
linewidth=1,
linecolor='black',
)
st.plotly_chart(fig)
def lastepiweek(year):
# Calcula o valor da última semana do ano
day = datetime.datetime(int(year), 12, 31) # Ultimo dia do ano
day_week = episem.extractweekday(day) # dia semana do ultimo dia
if day_week < 3:
day = day - datetime.timedelta(days=(day_week+1))
else:
day = day + datetime.timedelta(days=(6-day_week))
return(int(episem.episem(day, out='W')))
def main(fname, sep=','):
df = pd.read_csv(fname, low_memory=False, encoding='utf-8')
target_cols = [
'DT_NOTIFIC', 'DT_DIGITA', 'DT_SIN_PRI', 'DT_ANTIVIR', 'DT_COLETA',
'DT_IFI', 'DT_PCR_1', 'DT_ENCERRA'
]
yearweek_cols = ['%s_epiyearweek' % k for k in target_cols]
year_cols = ['%s_epiyear' % k for k in target_cols]
week_cols = ['%s_epiweek' % k for k in target_cols]
df[yearweek_cols] = df[target_cols].applymap(episem)
df[year_cols] = df[target_cols].applymap(lambda x: episem(x, out='Y'))
df[week_cols] = df[target_cols].applymap(lambda x: episem(x, out='W'))
# Calculate opportunities:
df = opportunity_calc_epiweeks(df,
colA='DT_SIN_PRI',
colB='DT_DIGITA',
colnew='SinPri2Digita_DelayWeeks')
df = opportunity_calc_epiweeks(df,
colA='DT_SIN_PRI',
colB='DT_ANTIVIR',
colnew='SinPri2Antivir_DelayWeeks')
df = opportunity_calc_epiweeks(df,
colA='DT_SIN_PRI',
colB='DT_NOTIFIC',
colnew='SinPri2Notific_DelayWeeks')
df = opportunity_calc_epiweeks(df,
colA='DT_SIN_PRI',
colB='DT_COLETA',
colnew='SinPri2Coleta_DelayWeeks')
df = opportunity_calc_epiweeks(df,
colA='DT_NOTIFIC',
colB='DT_ENCERRA',
colnew='Notific2Encerra_DelayWeeks')
df = opportunity_calc_epiweeks(df,
colA='DT_COLETA',
colB='DT_IFI',
colnew='Coleta2IFI_DelayWeeks')
df = opportunity_calc_epiweeks(df,
colA='DT_COLETA',
colB='DT_PCR_1',
colnew='Coleta2PCR_DelayWeeks')
df = opportunity_calc_epiweeks(df,
colA='DT_NOTIFIC',
colB='DT_DIGITA',
colnew='Notific2Digita_DelayWeeks')
df = opportunity_calc_days(df,
colA='DT_SIN_PRI',
colB='DT_DIGITA',
colnew='SinPri2Digita_DelayDays')
df = opportunity_calc_days(df,
colA='DT_SIN_PRI',
colB='DT_ANTIVIR',
colnew='SinPri2Antivir_DelayDays')
df = opportunity_calc_days(df,
colA='DT_SIN_PRI',
colB='DT_NOTIFIC',
colnew='SinPri2Notific_DelayDays')
df = opportunity_calc_days(df,
colA='DT_SIN_PRI',
colB='DT_COLETA',
colnew='SinPri2Coleta_DelayDays')
df = opportunity_calc_days(df,
colA='DT_NOTIFIC',
colB='DT_ENCERRA',
colnew='Notific2Encerra_DelayDays')
df = opportunity_calc_days(df,
colA='DT_COLETA',
colB='DT_IFI',
colnew='Coleta2IFI_DelayDays')
df = opportunity_calc_days(df,
colA='DT_COLETA',
colB='DT_PCR_1',
colnew='Coleta2PCR_DelayDays')
df = opportunity_calc_days(df,
colA='DT_NOTIFIC',
colB='DT_DIGITA',
colnew='Notific2Digita_DelayDays')
fout = '../clean_data/%s_epiweek.csv' % fname.split('/')[-1][:-4]
df.to_csv(fout, index=False, encoding='utf-8')