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app.py
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app.py
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from func_logging import *
import geopandas as gpd
import pandas as pd
import numpy as np
from datetime import datetime, date, timedelta
# from external modules
from func_read_spatial import *
from func_read_covid import *
from func_read_nsi import *
logger = create_logger('app')
logger.info('Starting the process')
####### GENERAL STATS #######
logger.info('Reading general stats')
covid_general = read_covid_general('./data/COVID_general.csv', 'Дата')
import plotly.graph_objects as go
import plotly.io as pio
pio.templates.default = "plotly_dark"
from plotly.offline import plot
from plotly.subplots import make_subplots
logger.info('Creating chart 1: Cumulative cases over time')
fig_gen_stats = make_subplots(specs=[[{"secondary_y": True}]])
fig_gen_stats.add_trace(go.Scatter(
x=covid_general.date,
y=covid_general.total_cases,
name='Confirmed',
line_shape='spline'), secondary_y=True)
fig_gen_stats.add_trace(go.Scatter(
x=covid_general.date,
y=covid_general.active_cases,
line=dict(color='yellow'),
name='Active',
line_shape='spline'), secondary_y=False)
fig_gen_stats.add_trace(go.Scatter(
x=covid_general.date,
y=covid_general.total_recoveries,
line=dict(color='green'),
name='Recovered',
line_shape='spline'),secondary_y=True)
fig_gen_stats.add_trace(go.Scatter(
x=covid_general.date,
y=covid_general.total_deaths,
line=dict(color='red'),
name='Deaths',
line_shape='spline'),secondary_y=False)
fig_gen_stats.update_layout(title = 'Number of cases over time (cumulative)')
fig_gen_stats.update_layout(paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)')
fig_gen_stats.update_yaxes(title_text="Confirmed / Recovered", secondary_y=True)
fig_gen_stats.update_yaxes(title_text="Deaths / Active infections", secondary_y=False)
#fig_gen_stats.update_xaxes(range=[date.today() - timedelta(days=5*30), date.today() + timedelta(days=1)])
logger.info('Creating chart 2: Cases per week')
#covid_general['week'] = covid_general['date'].dt.isocalendar().week
epoch = pd.Timestamp("2019-12-30") # continue the week number count when the year changes
covid_general['day_name'] = covid_general['date'].dt.day_name()
covid_general['week'] = (np.where(covid_general.date.astype("datetime64").le(epoch),
covid_general.date.dt.isocalendar().week,
covid_general.date.sub(epoch).dt.days//7+1)
)
covid_general['first_day'] = covid_general.groupby('week')['date'].transform('first')
covid_general_weekly = covid_general.groupby('first_day')[['new_cases', 'new_deaths', 'new_recoveries']].sum()
#covid_general_weekly['new_cases_pct_change'] = covid_general_weekly['new_cases'].pct_change()
if covid_general_weekly.iloc[-1,0] == 53:
pass
else:
# getting the current day to calculate projected values for the current week
last_day_num = covid_general['date'].dt.isocalendar().day.values[-1]
# projected current week confirmed cases
covid_general_weekly.iloc[-1,0] = round(covid_general_weekly.iloc[-1,0] * [24.9 if x==1 else 5.4 if x==2 else 2.6 if x==3 else 1.8 if x==4 else 1.4 if x==5 else 1.1 if x==6 else 1 for x in [last_day_num]][0],0)
# projected current week death cases
covid_general_weekly.iloc[-1,1] = round(covid_general_weekly.iloc[-1,1] * [14.7 if x==1 else 3.6 if x==2 else 2.2 if x==3 else 1.6 if x==4 else 1.3 if x==5 else 1.1 if x==6 else 1 for x in [last_day_num]][0],0)
# projected current week recovered cases
covid_general_weekly.iloc[-1,2] = round(covid_general_weekly.iloc[-1,2] * [12.7 if x==1 else 4.3 if x==2 else 2.5 if x==3 else 1.8 if x==4 else 1.3 if x==5 else 1.1 if x==6 else 1 for x in [last_day_num]][0],0)
covid_general_weekly['new_cases_pct_change'] = covid_general_weekly['new_cases'].pct_change()
#removing the first week as it starts on Saturday
covid_general_weekly = covid_general_weekly[1:]
from plotly.subplots import make_subplots
fig_gen_stats_weekly = make_subplots(specs=[[{"secondary_y": True}]])
fig_gen_stats_weekly.add_trace(go.Scatter(
x=covid_general_weekly.index,
y=covid_general_weekly.new_cases,
name='New confirmed cases',
line_shape='spline'), secondary_y=True)
fig_gen_stats_weekly.add_trace(go.Bar(
x=covid_general_weekly.index,
y=covid_general_weekly.new_deaths,
name='New death cases'), secondary_y=False)
fig_gen_stats_weekly.add_trace(go.Scatter(
x=covid_general_weekly.index,
y=covid_general_weekly.new_recoveries,
name='New recoveries',
line_shape='spline'),secondary_y=True)
#fig_gen_stats_weekly.add_annotation(
# x=53, y=530, text="New Year",
# showarrow=True, arrowhead=2, arrowsize=1, arrowwidth=2,
# arrowcolor='orange', font=dict(color='orange', size=15),
# ax=0, ay=-100)
fig_gen_stats_weekly.update_layout(title = 'New cases per week (projected estimations for the current week)')
fig_gen_stats_weekly.update_xaxes(title_text="week number")
fig_gen_stats_weekly.update_yaxes(title_text="Confirmed/recovered", secondary_y=True)
fig_gen_stats_weekly.update_yaxes(title_text="Deaths", secondary_y=False)
fig_gen_stats_weekly.update_layout(paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)')
logger.info('Creating chart 3: New cases weekly % change')
covid_general_weekly = covid_general_weekly.merge(
covid_general[['first_day', 'week']].drop_duplicates().set_index('first_day'), left_index=True, right_index=True
)
fig_gen_stats_weekly_new_pct = go.Figure()
fig_gen_stats_weekly_new_pct.add_trace(go.Scatter(
x=covid_general_weekly['week'][1:],
y=covid_general_weekly.new_cases_pct_change[1:],
line=dict(color='orange'),
line_shape='spline',
name='Confirmed % change'))
fig_gen_stats_weekly_new_pct.add_annotation(
x=25, y=0.08, text="Borders reopening",
showarrow=True, arrowhead=1,
arrowcolor='ivory', font=dict(color='ivory'),
ax=20, ay=50)
fig_gen_stats_weekly_new_pct.add_annotation(
x=27, y=0.41, text="Football cup final",
showarrow=True, arrowhead=1,
arrowcolor='cornflowerblue', font=dict(color='cornflowerblue'),
ax=-30)
fig_gen_stats_weekly_new_pct.add_annotation(
x=28, y=0.43, text="Start of protests",
showarrow=True, arrowhead=1,
arrowcolor='cyan', font=dict(color='cyan'),
ax=20, ay=-50)
fig_gen_stats_weekly_new_pct.add_annotation(
x=36, y=-0.04, text="First mass protest",
showarrow=True, arrowhead=1,
arrowcolor='lime', font=dict(color='lime'),
ay=-50)
fig_gen_stats_weekly_new_pct.add_annotation(
x=38, y=0.12, text="Schools opening",
showarrow=True, arrowhead=1,
arrowcolor='red', font=dict(color='red'),
ay=-70)
fig_gen_stats_weekly_new_pct.add_annotation(
x=48, y=-0.08, text="Second lockdown",
showarrow=True, arrowhead=1,
arrowcolor='yellow', font=dict(color='yellow'),
ax=30, ay=-50)
fig_gen_stats_weekly_new_pct.add_annotation(
x=52, y=-0.49 , text="Antigen tests",
showarrow=True, arrowhead=1,
arrowcolor='orange', font=dict(color='orange'),
ay=20)
fig_gen_stats_weekly_new_pct.add_annotation(
x=57, y=0.33, text="Vaccines",
showarrow=True, arrowhead=1,
arrowcolor='green', font=dict(color='green'))
fig_gen_stats_weekly_new_pct.add_annotation(
x=58, y=0.26, text="Schools partial reopening",
showarrow=True, arrowhead=1,
arrowcolor='red', font=dict(color='red'),
ax=-20, ay=30)
fig_gen_stats_weekly_new_pct.update_layout(
title='New cases over time - weekly % change',
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)',
yaxis=dict(tickformat=',.0%', hoverformat=',.2%'))
logger.info('Creating chart 4: New cases per week + summer events')
fig_gen_stats_weekly_events = go.Figure()
fig_gen_stats_weekly_events.add_trace(go.Scatter(
x=covid_general_weekly.index[1:],
y=covid_general_weekly.new_cases[1:],
name='New confirmed cases'))
fig_gen_stats_weekly_events.add_annotation(
x=25, y=626, text="Borders reopening",
showarrow=True, arrowhead=1,
arrowcolor='ivory', font=dict(color='ivory'),
ax=10)
fig_gen_stats_weekly_events.add_annotation(
x=27, y=1072, text="Football cup final",
showarrow=True, arrowhead=1,
arrowcolor='cornflowerblue', font=dict(color='cornflowerblue'))
fig_gen_stats_weekly_events.add_annotation(
x=28, y=1518, text="Start of protests",
showarrow=True, arrowhead=1,
arrowcolor='cyan', font=dict(color='cyan'),
ax=10)
fig_gen_stats_weekly_events.add_annotation(
x=36, y=906, text="First mass protest",
showarrow=True, arrowhead=1,
arrowcolor='lime', font=dict(color='lime'))
fig_gen_stats_weekly_events.add_annotation(
x=38, y=948, text="Schools opening",
showarrow=True, arrowhead=1,
arrowcolor='red', font=dict(color='red'),
ax=10, ay=-30)
fig_gen_stats_weekly_events.update_layout(
title='New confirmed cases per week + summer events',
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)')
fig_gen_stats_weekly_events.update_xaxes(range=[24, 43])
fig_gen_stats_weekly_events.update_yaxes(range=[0, 6000])
logger.info('Creating chart 5: New cases per week, events and second lockdown')
fig_gen_stats_weekly_events_2 = go.Figure()
fig_gen_stats_weekly_events_2.add_trace(go.Scatter(
x=covid_general_weekly.index[1:],
y=covid_general_weekly.new_cases[1:],
name='New confirmed cases'))
fig_gen_stats_weekly_events_2.add_annotation(
x=25, y=706, text="Borders reopening",
showarrow=True, arrowhead=1,
arrowcolor='ivory', font=dict(color='ivory'),
ax=10)
fig_gen_stats_weekly_events_2.add_annotation(
x=27, y=1152, text="Football cup final",
showarrow=True, arrowhead=1,
arrowcolor='cornflowerblue', font=dict(color='cornflowerblue'),
ax=5, ay=-50)
fig_gen_stats_weekly_events_2.add_annotation(
x=28, y=1598, text="Start of protests",
showarrow=True, arrowhead=1,
arrowcolor='cyan', font=dict(color='cyan'),
ax=50, ay=-30)
fig_gen_stats_weekly_events_2.add_annotation(
x=36, y=986, text="First mass protest",
showarrow=True, arrowhead=1,
arrowcolor='lime', font=dict(color='lime'))
fig_gen_stats_weekly_events_2.add_annotation(
x=38, y=1028, text="Schools opening",
showarrow=True, arrowhead=1,
arrowcolor='red', font=dict(color='red'),
ax=10, ay=-50)
fig_gen_stats_weekly_events_2.add_annotation(
x=48, y=21150, text="Second lockdown",
showarrow=True, arrowhead=1,
arrowcolor='yellow', font=dict(color='yellow'),
ax=30, ay=-30)
fig_gen_stats_weekly_events_2.add_annotation(
x=52, y=6255, text="Antigen tests",
showarrow=True, arrowhead=1,
arrowcolor='orange', font=dict(color='orange'),
ay=50)
fig_gen_stats_weekly_events_2.add_annotation(
x=57, y=3922, text="Vaccines",
showarrow=True, arrowhead=1,
arrowcolor='green', font=dict(color='green'),
ax=-20)
fig_gen_stats_weekly_events_2.add_annotation(
x=58, y=4934, text="Schools partial reopening",
showarrow=True, arrowhead=1,
arrowcolor='red', font=dict(color='red'),
ax=-20, ay=30)
fig_gen_stats_weekly_events_2.update_layout(
title='New confirmed cases per week + summer events + second lockdown',
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)')
fig_gen_stats_weekly_events_2.update_xaxes(range=[24, covid_general_weekly.index[-1]])
### Rates
logger.info('Calculating rates')
covid_general['total_cases_7days_ago'] = covid_general['total_cases'].shift(periods=7)
covid_general['total_cases_14days_ago'] = covid_general['total_cases'].shift(periods=14)
covid_general['death_rate'] = (covid_general['total_deaths'] / covid_general['total_cases_14days_ago']).round(4)
covid_general['recovery_rate'] = (covid_general['total_recoveries'] / covid_general['total_cases_14days_ago']).round(4)
covid_general['hospitalized_rate'] = (covid_general['hospitalized'] / covid_general['active_cases']).round(4)
covid_general['intensive_care_rate'] = (covid_general['intensive_care'] / covid_general['hospitalized']).round(4)
covid_general['tests_positive_rate'] = (covid_general['new_cases'] / covid_general['daily_tests']).round(4)
covid_general['death_rate_v2'] = (covid_general['total_deaths'] / (covid_general['total_deaths'] + covid_general['total_recoveries'])).round(4)
covid_general['recovery_rate_v2'] = (covid_general['total_recoveries'] / (covid_general['total_deaths'] + covid_general['total_recoveries'])).round(4)
logger.info('Creating chart 6: Rates')
fig_rates_mort_rec = go.Figure()
fig_rates_mort_rec.add_trace(go.Scatter(
x=covid_general.date,
y=covid_general.death_rate,
line_shape='spline',
line=dict(color='red'),
name='Mortality rate'))
fig_rates_mort_rec.add_trace(go.Scatter(
x=covid_general.date,
y=covid_general.recovery_rate,
line_shape='spline',
line=dict(color='green'),
name='Recovery rate',
visible='legendonly'))
fig_rates_mort_rec.update_layout(
title='COVID-19 mortality and recovery rates (based on confirmed cases by 14 days ago)',
yaxis=dict(tickformat=',.0%', hoverformat=',.2%'))
fig_rates_mort_rec_v2 = go.Figure()
fig_rates_mort_rec_v2.add_trace(go.Scatter(
x=covid_general.date,
y=covid_general.death_rate_v2,
line_shape='spline',
line=dict(color='red'),
name='Mortality rate'))
fig_rates_mort_rec_v2.add_trace(go.Scatter(
x=covid_general.date,
y=covid_general.recovery_rate_v2,
line_shape='spline',
line=dict(color='green'),
name='Recovery rate',
visible='legendonly'))
fig_rates_mort_rec_v2.update_layout(
title='COVID-19 mortality and recovery rates (based on closed cases)',
yaxis=dict(tickformat=',.0%', hoverformat=',.2%'))
fig_rates_hospitalized = go.Figure()
fig_rates_hospitalized.add_trace(go.Scatter(
x=covid_general.date,
y=covid_general.hospitalized_rate,
line_shape='spline',
line=dict(color='yellow'),
name='Hospitalized rate'))
fig_rates_hospitalized.add_trace(go.Scatter(
x=covid_general.date,
y=covid_general.intensive_care_rate,
line_shape='spline',
line=dict(color='orange'),
name='Intensive care rate'))
fig_rates_hospitalized.update_layout(
title="COVID-19 hospitalized and intensive care rates over time",
yaxis=dict(tickformat=',.0%', hoverformat=',.2%'))
fig_rates_positive_tests = go.Figure()
fig_rates_positive_tests.add_trace(go.Scatter(
x=covid_general.date,
y=covid_general.tests_positive_rate,
line_shape='spline',
line=dict(color='cyan'),
name='Tests positive rate'))
fig_rates_positive_tests.update_layout(
title="COVID-19 positive tests rate",
yaxis=dict(tickformat=',.0%', hoverformat=',.2%'))
# for the dashboard
for f in [fig_rates_mort_rec, fig_rates_mort_rec_v2, fig_rates_hospitalized, fig_rates_positive_tests]:
f.update_layout(paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)')
### Map provinces
logger.info('Reading spatial data')
geodf = read_spatial_data('./shape/BGR_adm1.shp', codes_spatial)
covid_by_province = read_covid_by_province('./data/COVID_provinces.csv', date_col='Дата')
pop_by_province = read_population_data('./data/Pop_6.1.1_Pop_DR.xls', worksheet_name='2019',
col_num=2, col_names=['municipality','pop'],
skip=5, codes=codes_pop)
covid_pop = (covid_by_province.set_index('code')
.join(pop_by_province.set_index('code'))
.join(geodf[['code','province']].set_index('code')))
covid_pop['new_per_100k'] = (100000*covid_pop['new_cases']/covid_pop['pop']).round(0)
covid_pop['total_per_100k'] = (100000*covid_pop['ALL']/covid_pop['pop']).round(0)
covid_pop['active_per_100k'] = (100000*covid_pop['ACT']/covid_pop['pop']).round(0)
#covid_pop = gpd.GeoDataFrame(covid_pop)
covid_pop_sorted = covid_pop.sort_values(by=['date', 'ALL'])
# animation frame parameter should be string or int
#covid_pop_sorted['day'] = covid_pop_sorted.date.apply(lambda x: (x - min(covid_pop_sorted.date)).days + 1)
geodf['geometry'] = geodf['geometry'].simplify(tolerance=0.005, preserve_topology=True)
covid_yesterday = gpd.GeoDataFrame(
covid_pop.loc[covid_pop.date == max(covid_pop.date)]
.rename(columns={'ALL':'total cases', 'ACT':'active cases', 'new_cases':'new cases'})
.join(geodf[['code','geometry']].set_index('code')))
#logger.info('Creating chart 7: Provinces map - total cases per 100k pop')
import plotly.express as px
#fig_yesterday_map_total = px.choropleth_mapbox(
# covid_yesterday,
# geojson=covid_yesterday.geometry,
# locations=covid_yesterday.index,
# color='total_per_100k',
# color_continuous_scale='Burgyl',
# hover_name='province',
# hover_data=['total cases'],
# labels={'total_per_100k':'total infections<br>per 100k pop'},
# title='Total confirmed cases per 100,000 population by province',
# center={'lat': 42.734189, 'lon': 25.1635087},
# mapbox_style='carto-darkmatter',
# opacity=0.85,
# zoom=6)
#fig_yesterday_map_total.update_layout(
# margin={"r":0,"t":40,"l":0,"b":0},
# template='plotly_dark',
# paper_bgcolor='rgba(0,0,0,0)',
# plot_bgcolor='rgba(0,0,0,0)')
logger.info('Creating chart 7: Provinces map - new cases per 100k pop for the past 14 days')
covid_past2weeks = gpd.GeoDataFrame(
(
covid_pop.loc[
covid_pop.date >= covid_pop.date.max() - timedelta(14),
['date','province','new_per_100k']
].reset_index()
).groupby(['code','province'])[['new_per_100k']].sum().reset_index(level='province')
.join(geodf[['code','geometry']].set_index('code'))
).reset_index(drop=True).set_index('province').sort_values(by='new_per_100k')
covid_past2weeks_conditions = [(covid_past2weeks.new_per_100k < 100),
(covid_past2weeks.new_per_100k < 250),
(covid_past2weeks.new_per_100k < 500),
(covid_past2weeks.new_per_100k < 1000),
(covid_past2weeks.new_per_100k >= 1000)]
covid_past2weeks_labels = ['< 100 per 100k pop',
'100-250 per 100k pop',
'250-500 per 100k pop',
'500-1000 per 100k pop',
'1000+ per 100k pop']
covid_past2weeks_colors = ['green', 'gold', 'darkorange', 'red', 'purple']
covid_past2weeks['label'] = np.select(
covid_past2weeks_conditions, covid_past2weeks_labels
)
covid_past2weeks['color'] = np.select(
covid_past2weeks_conditions, covid_past2weeks_colors
)
fig_newper100k_14days_map = px.choropleth_mapbox(
covid_past2weeks,
geojson = covid_past2weeks.geometry,
locations = covid_past2weeks.index,
color = 'label',
color_discrete_sequence = covid_past2weeks['color'].drop_duplicates(),
hover_name = covid_past2weeks.reset_index()['province'],
hover_data = ['new_per_100k'],
labels = {'new_per_100k':'New infections per 100k pop<br> for the past 14 days',
'label':'Category'},
title = f"New infections per 100,000 population for the past 14 days",
center = {'lat': 42.734189, 'lon': 25.1635087},
mapbox_style = 'carto-darkmatter',
opacity = 1,
zoom = 6)
fig_newper100k_14days_map.update_layout(
margin={"r":0,"t":40,"l":0,"b":0},
template='plotly_dark',
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)')
fig_newper100k_14days_map_continuous = px.choropleth_mapbox(
covid_past2weeks,
geojson = covid_past2weeks.geometry,
locations = covid_past2weeks.index,
color = 'new_per_100k',
color_continuous_scale='Sunsetdark',
hover_name = covid_past2weeks.reset_index()['province'],
hover_data = ['new_per_100k'],
labels = {'new_per_100k':'New infections per 100k pop<br> for the past 14 days',
'label':'Category'},
title = f"New infections per 100,000 population for the past 14 days",
center = {'lat': 42.734189, 'lon': 25.1635087},
mapbox_style = 'carto-darkmatter',
opacity = 1,
zoom = 6)
fig_newper100k_14days_map_continuous.update_layout(
margin={"r":0,"t":40,"l":0,"b":0},
template='plotly_dark',
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)')
logger.info('Creating chart 8: Provinces map - new cases per 100k pop')
fig_yesterday_map_new = px.choropleth_mapbox(
covid_yesterday,
geojson=covid_yesterday.geometry,
locations=covid_yesterday.index,
color='new_per_100k',
color_continuous_scale='Burgyl',
hover_name='province',
hover_data=['new cases'],
labels={'new_per_100k':'new infections<br>per 100k pop'},
title=f"New daily cases per 100,000 population by province",
center={'lat': 42.734189, 'lon': 25.1635087},
mapbox_style='carto-darkmatter',
opacity=1,
zoom=6)
fig_yesterday_map_new.update_layout(
margin={"r":0,"t":40,"l":0,"b":0},
template='plotly_dark',
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)')
logger.info('Creating chart 9: Provinces map - active cases per 100k pop')
fig_yesterday_map_active = px.choropleth_mapbox(
covid_yesterday,
geojson=covid_yesterday.geometry,
locations=covid_yesterday.index,
color='active_per_100k',
color_continuous_scale='Burgyl',
hover_name='province',
hover_data=['active cases'],
labels={'active_per_100k':'active infections<br>per 100k pop'},
title=f"Currently active cases per 100,000 population by province",
center={'lat': 42.734189, 'lon': 25.1635087},
mapbox_style='carto-darkmatter',
opacity=1,
zoom=6)
fig_yesterday_map_active.update_layout(
margin={"r":0,"t":40,"l":0,"b":0},
template='plotly_dark',
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)')
### Age bands
logger.info('Reading age bands data')
covid_by_age_band = (
pd.read_csv(
'./data/COVID_age_bands.csv',
parse_dates=['Дата']
).rename(columns={'Дата':'date'})
).replace('-',0).drop(['0 - 1', '1 - 5', '6 - 9', '10 - 14', '15 - 19'], axis=1)
logger.info('Creating chart 10: Cumulative cases by age band')
age_band_colors = ['green', 'cyan', 'magenta', 'ghostwhite', 'coral', 'royalblue', 'darkred', 'orange', 'brown']
fig_age = go.Figure()
i=0
for col in covid_by_age_band.columns[1:]:
covid_by_age_band[col] = covid_by_age_band[col].astype('int')
fig_age.add_trace(go.Scatter(
x=covid_by_age_band['date'],
y=covid_by_age_band[col],
mode='lines',
line=dict(width=3, color=age_band_colors[i]),
name=col))
i+=1
fig_age.update_layout(
title='Total cases over time by age band',
template='plotly_dark',
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)')
fig_age.add_annotation(
x=pd.Timestamp(2020,6,15), y=0, text="Borders reopening",
showarrow=True, arrowhead=1,
arrowcolor='ivory', font=dict(color='ivory'),
ay=20)
fig_age.add_annotation(
x=pd.Timestamp(2020,7,1), y=0, text="Football cup final",
showarrow=True, arrowhead=1,
arrowcolor='cornflowerblue', font=dict(color='cornflowerblue'),
ay=40)
fig_age.add_annotation(
x=pd.Timestamp(2020,7,11), y=0, text="Start of protests",
showarrow=True, arrowhead=1,
arrowcolor='cyan', font=dict(color='cyan'),
ax=20, ay=20)
fig_age.add_annotation(
x=pd.Timestamp(2020,9,2), y=0, text="First mass protest",
showarrow=True, arrowhead=1,
arrowcolor='lime', font=dict(color='lime'),
ay=20)
fig_age.add_annotation(
x=pd.Timestamp(2020,9,15), y=0, text="Schools opening",
showarrow=True, arrowhead=1,
arrowcolor='red', font=dict(color='red'),
ax=10, ay=40)
fig_age.add_annotation(
x=pd.Timestamp(2020,11,28), y=0, text="Second lockdown",
showarrow=True, arrowhead=1,
arrowcolor='yellow', font=dict(color='yellow'),
ay=20)
fig_age.add_annotation(
x=pd.Timestamp(2020,12,24), y=0, text="Antigen tests",
showarrow=True, arrowhead=1,
arrowcolor='orange', font=dict(color='orange'),
ay=20)
fig_age.add_annotation(
x=pd.Timestamp(2021,1,25), y=0, text="Vaccines",
showarrow=True, arrowhead=1,
arrowcolor='green', font=dict(color='green'),
ay=20)
fig_age.add_annotation(
x=pd.Timestamp(2021,2,4), y=0, text="Schools partial reopening",
showarrow=True, arrowhead=1,
arrowcolor='red', font=dict(color='red'),
ax=-10, ay=40)
# Prop of total
covid_by_age_band_perc = covid_by_age_band.copy()
for c in covid_by_age_band_perc.columns[1:]:
covid_by_age_band_perc[c] = 100*covid_by_age_band[c] / covid_by_age_band.iloc[:,1:].sum(axis=1)
fig_age_prop = go.Figure()
for age_band in covid_by_age_band_perc.columns[1:]:
fig_age_prop.add_trace(go.Scatter(
x=covid_by_age_band_perc['date'],
y=covid_by_age_band_perc[age_band],
mode='lines',
name=age_band,
))
fig_age_prop.update_layout(
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)',
title='Proportion of total infections by age band')
# Area chart - past 14 days
covid_by_age_band_daily = covid_by_age_band.copy()
for c in covid_by_age_band_daily.columns[1:]:
covid_by_age_band_daily[c] = covid_by_age_band[c].diff()
covid_by_age_band_14days = covid_by_age_band_daily.copy()
for i in covid_by_age_band_14days.index:
for c in covid_by_age_band_14days.columns[1:]:
covid_by_age_band_14days.loc[covid_by_age_band_14days.index==i, c] = covid_by_age_band_daily.loc[covid_by_age_band_daily.index.isin(np.arange(i-13,i+1)), c].sum()
covid_by_age_band_14days_perc = covid_by_age_band_14days.copy()
for c in covid_by_age_band_14days_perc.columns[1:]:
covid_by_age_band_14days_perc[c] = 100*covid_by_age_band_14days[c] / covid_by_age_band_14days.iloc[:,1:].sum(axis=1)
fig_age_area = go.Figure()
for age_band in covid_by_age_band_14days_perc.columns[1:]:
fig_age_area.add_trace(go.Scatter(
x=covid_by_age_band_14days_perc['date'],
y=covid_by_age_band_14days_perc[age_band],
mode='lines',
name=age_band,
stackgroup='one' # define stack group
))
fig_age_area.update_layout(
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)',
title='Proportion of infections for the past 14 days by age band'
)
# Deaths breakdown
## gender
deaths_breakdown = ReadDeathsBreakdown(
'./data/COVID_deaths_breakdown.csv', 'Дата').to_eng()
deaths_breakdown_gender = deaths_breakdown.groupby('gender', as_index=False)['num'].sum()
fig_deaths_gender = px.pie(
deaths_breakdown_gender, values='num', color='gender',
color_discrete_map={'Male':'#636EFA', 'Female':'#EF553B'})
fig_deaths_gender.update_layout(
title='Deaths breakdown by gender',
yaxis=dict(tickformat=',.0%', hoverformat=',.2%'),
paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)')
## mortality rate by age band
deaths_breakdown_age = deaths_breakdown.groupby('ageband', as_index=False)['num'].sum()
deaths_breakdown_age['perc'] = deaths_breakdown_age['num'] / deaths_breakdown_age['num'].sum()
deaths_breakdown_age['perc_cum'] = deaths_breakdown_age['perc'].cumsum()
deaths_breakdown_age['ageband_coarse'] = np.where(
deaths_breakdown_age['ageband']
.apply(lambda x: int(x[:2])<20)
,
'0 - 19', deaths_breakdown_age['ageband']
)
deaths_breakdown_age = deaths_breakdown_age.groupby('ageband_coarse', as_index=False)['num', 'perc'].sum()
for band in deaths_breakdown_age['ageband_coarse']:
deaths_breakdown_age.loc[deaths_breakdown_age['ageband_coarse']==band, 'infections'] = covid_by_age_band[band].values[-1]
deaths_breakdown_age['mortality_rate'] = deaths_breakdown_age['num'] / deaths_breakdown_age['infections']
fig_age_mortality = px.bar(
deaths_breakdown_age, x='ageband_coarse', y='mortality_rate',
color='infections', color_continuous_scale='jet')
fig_age_mortality.update_xaxes(title='Age band')
fig_age_mortality.update_yaxes(title='Mortality rate')
fig_age_mortality.update_layout(
title='Mortality rate by age band',
yaxis=dict(tickformat=',.0%', hoverformat=',.2%'),
paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)')
## deaths by age band
fig_deaths_age = px.bar(
deaths_breakdown_age, x='ageband_coarse', y='num')
fig_deaths_age.update_xaxes(title='Age band')
fig_deaths_age.update_yaxes(title='Deaths')
fig_deaths_age.update_layout(
title='Total Deaths by Age Band',
paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)')
## deaths by age and gender
deaths_breakdown_age_gender = (
deaths_breakdown
.groupby(['gender','ageband'], as_index=False)
['num'].sum()
)
deaths_breakdown_age_gender
deaths_breakdown_age_gender['perc'] = deaths_breakdown_age_gender['num'] / deaths_breakdown_age_gender.groupby('ageband')['num'].transform('sum')
fig_deaths_age_gender = px.bar(
deaths_breakdown_age_gender,
x='ageband', y='perc', color='gender',
color_discrete_map={'Male':'#636EFA', 'Female':'#EF553B'}
)
fig_deaths_age_gender.update_xaxes(title='Age band')
fig_deaths_age_gender.update_yaxes(title='Deaths proportion by gender')
fig_deaths_age_gender.update_layout(
title='Total deaths by age band',
yaxis=dict(tickformat=',.0%', hoverformat=',.2%'),
paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)')
###### TESTS ######
logger.info('Test types')
tests = read_covid_tests('./data/COVID_test_type.csv', 'Дата')
logger.info('Creating chart 11: Daily tests by test type')
fig_tests_daily = go.Figure()
fig_tests_daily.add_trace(go.Scatter(
x=tests.date,
y=tests.new_pcr,
name='Daily PCR tests',
mode='lines',
line_shape='spline',
marker_color='coral'))
fig_tests_daily.add_trace(go.Scatter(
x=tests.date,
y=tests.new_antigen,
name='Daily Antigen tests',
mode='lines',
line_shape='spline',
marker_color='darkcyan'))
fig_tests_daily.update_layout(
title='New COVID-19 tests per day by test type',
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)')
#fig_tests_daily.update_xaxes(
# range=[date.today() - timedelta(days=5*30),
# date.today() + timedelta(days=1)]
#)
logger.info('Creating chart 12: Positive tests % by test type')
fig_tests_daily_positive = go.Figure()
fig_tests_daily_positive.add_trace(go.Scatter(
x=tests.date,
y=tests.pos_rate_pcr,
name='Positive PCR %',
mode='lines',
line_shape='spline',
marker_color='coral'))
fig_tests_daily_positive.add_trace(go.Scatter(
x=tests.date,
y=tests.pos_rate_antigen,
name='Positive Antigen %',
mode='lines',
line_shape='spline',
marker_color='darkcyan'))
fig_tests_daily_positive.update_layout(
title='Positive tests % by test type',
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)',
yaxis=dict(tickformat=',.0%', hoverformat=',.2%'))
logger.info('Creating chart 13: Tests Sankey')
tests_sankey_data_since_antigen = pd.DataFrame()
tests_sankey_data_since_antigen['total_tests'] = [tests['new_tests'].sum()]
tests_sankey_data_since_antigen['total_tests_pcr'] = [tests['new_pcr'].sum()]
tests_sankey_data_since_antigen['total_tests_antigen'] = [tests['new_antigen'].sum()]
tests_sankey_data_since_antigen['total_positive'] = [tests['new_positive'].sum()]
tests_sankey_data_since_antigen['total_positive_pcr'] = [tests['new_positive_pcr'].sum()]
tests_sankey_data_since_antigen['total_positive_antigen'] = [tests['new_positive_antigen'].sum()]
tests_sankey_data_since_antigen['new_positive_pcr'] = [tests['new_positive_pcr'].values[-1]]
tests_sankey_data_since_antigen['new_positive_antigen'] = [tests['new_positive_antigen'].values[-1]]
colors_list = [
u'rgba(5, 116, 187, 0.8)',
u'rgba(225, 127, 14, 0.8)',
u'rgba(44, 160, 44, 0.8)',
u'rgba(214, 39, 40, 0.8)',
u'rgba(140, 86, 75, 0.8)',
u'rgba(238, 43, 148, 0.8)',
u'rgba(83, 128, 94, 0.8)',
u'rgba(188, 189, 34, 0.8)',
u'rgba(23, 190, 207, 0.8)',
#u'rgba(74, 64, 48, 0.8)',
u'rgba(170, 139, 180, 0.8)'
]
fig_tests_sankey = go.Figure(
data = [
go.Sankey(
valueformat = ",", # integer
node = dict(
pad = 15,
thickness = 15,
line = dict(color = 'black', width = 0.5),
#label = sankey_dummy.groupby('col1').count().index.tolist(),
label = ['Total tests', 'PCR tests', 'Antigen tests',
'Positive PCR', 'Positive Antigen',
'Positive for yesterday', 'Positive for yesterday'],
x = [0.1, 0.4, 0.4, 0.55, 0.55, 0.9, 0.9],
y = [0.5, 0.1, 0.6, 0.05, 0.3, 0.05, 0.3],
color = colors_list[:7]
),
link = dict(
#source = sankey_dummy['src_ID'].tolist(),
#target = sankey_dummy['tgt_ID'].tolist(),
#value = sankey_dummy['count'].tolist(),
#color = colors_list[:len(sankey_dummy)]
source = [0, 0, 1, 2, 3, 4],
target = [1, 2, 3, 4, 5, 6],
value = [
tests_sankey_data_since_antigen['total_tests_pcr'].values[-1],
tests_sankey_data_since_antigen['total_tests_antigen'].values[-1],
tests_sankey_data_since_antigen['total_positive_pcr'].values[-1],
tests_sankey_data_since_antigen['total_positive_antigen'].values[-1],
tests_sankey_data_since_antigen['new_positive_pcr'].values[-1],
tests_sankey_data_since_antigen['new_positive_antigen'].values[-1]
],
color = colors_list[-6:],
hovertemplate = "from %{source.label} <br />" + "were %{target.label}"
)
)
]
).update_layout(
title_text='Tests by type since 24th Dec 2020 - Sankey diagram',
font_size=12,
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)')
logger.info('Creating chart 14: Hospitalized patients')
fig_hospitalized = make_subplots(specs=[[{"secondary_y": True}]])
fig_hospitalized.add_trace(go.Scatter(
x=covid_general.date,
y=covid_general.hospitalized,
name='Hospitalized'), secondary_y=False)
fig_hospitalized.add_trace(go.Scatter(
x=covid_general.date,
y=covid_general.intensive_care,
name='Intensive care units'), secondary_y=True)
fig_hospitalized.update_yaxes(title_text="Hospitalized patients", secondary_y=False)
fig_hospitalized.update_yaxes(title_text="Patients in intensive care units", secondary_y=True)
fig_hospitalized.update_layout(
title="Currently hospitalized patients by date",
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)')
#fig_hospitalized.update_xaxes(range=[date.today() - timedelta(days=5*30), date.today() + timedelta(days=1)])
####### REPRODUCTION NUMBER #######
logger.info('Starting Rt processing')
provinces = covid_pop_sorted[['province', 'date', 'ALL']].groupby(['province','date']).ALL.sum()
orig_bg = pd.read_csv('./dash_data/r0_bg_original.csv')
smoothed_bg = pd.read_csv('./dash_data/r0_bg_smoothed.csv')
logger.info('Creating chart 15: Smoothed new cases - BG')
fig_new_bg = go.Figure()
fig_new_bg.add_trace(go.Scatter(
x=orig_bg.reset_index()['date'],
y=orig_bg.reset_index()['ALL'],
mode='lines',
line=dict(dash='dot'),
name='Actual'))
fig_new_bg.add_trace(go.Scatter(
x=smoothed_bg.reset_index()['date'],
y=smoothed_bg.reset_index()['ALL'],
mode='lines',
line=dict(width=3),
name='Smoothed',
marker_color='royalblue'))
fig_new_bg.update_layout(
title='Daily new cases in Bulgaria',
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)')
#fig_new_bg.update_xaxes(range=[date.today() - timedelta(days=5*30), date.today() + timedelta(days=1)])
logger.info('Creating chart 16: Smoothed recoveries - BG')
orig_recovered_bg = pd.read_csv('./dash_data/r0_bg_original_recovered.csv')
smoothed_recovered_bg = pd.read_csv('./dash_data/r0_bg_smoothed_recovered.csv')
fig_recovered_bg = go.Figure()
fig_recovered_bg.add_trace(go.Scatter(
x=orig_recovered_bg.reset_index()['date'],
y=orig_recovered_bg.reset_index()['total_recoveries'],
mode='lines',
line=dict(dash='dot'),
name='Actual'))
fig_recovered_bg.add_trace(go.Scatter(
x=smoothed_recovered_bg.reset_index()['date'],
y=smoothed_recovered_bg.reset_index()['total_recoveries'],
mode='lines',
line=dict(width=3),
name='Smoothed',
marker_color='green'))
fig_recovered_bg.update_layout(
title='Daily new recoveries in Bulgaria',
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)')
#fig_recovered_bg.update_xaxes(range=[date.today() - timedelta(days=5*30), date.today() + timedelta(days=1)])
logger.info('Creating chart 17: Smoothed deaths - BG')
orig_deaths_bg = pd.read_csv('./dash_data/r0_bg_original_deaths.csv')
smoothed_deaths_bg = pd.read_csv('./dash_data/r0_bg_smoothed_deaths.csv')
fig_deaths_bg = go.Figure()
fig_deaths_bg.add_trace(go.Scatter(
x=orig_deaths_bg.reset_index()['date'],
y=orig_deaths_bg.reset_index()['total_deaths'],
mode='lines', line=dict(dash='dot'), name='Actual'))
fig_deaths_bg.add_trace(go.Scatter(
x=smoothed_deaths_bg.reset_index()['date'],
y=smoothed_deaths_bg.reset_index()['total_deaths'],
mode='lines', line=dict(width=3), name='Smoothed'))
fig_deaths_bg.update_layout(title='Daily new deaths in Bulgaria', paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)')
#fig_deaths_bg.update_xaxes(range=[date.today() - timedelta(days=5*30), date.today()])
logger.info('Creating chart 18: Smoothed new cases - BG - age bands')
covid_by_age_band_diff_smoothed = covid_by_age_band.set_index('date').diff().rolling(9,
win_type='gaussian',
min_periods=1,
center=True).mean(std=3).round()
fig_age_diff = go.Figure()
i=0
for col in covid_by_age_band_diff_smoothed.columns:
fig_age_diff.add_trace(go.Scatter(
x=covid_by_age_band_diff_smoothed.index,
y=covid_by_age_band_diff_smoothed[col],
mode='lines', line_shape='spline',
line=dict(color=age_band_colors[i]),
name=col))
i+=1
fig_age_diff.update_layout(
title='New confirmed cases per day by age band (smoothed figures)',
paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)')
fig_age_diff.update_xaxes(
range=[date.today() - timedelta(days=5*30),
date.today() + timedelta(days=1)]
)
logger.info("Creating chart 19: Smoothed new cases - BG - age bands per 100,000 pop")
pop_by_age_band = read_nsi_age_bands(
'./data/Pop_6.1.2_Pop_DR.xls',
worksheet_name='2019',
col_num=2,
col_names=['age_band', 'pop'],
skip=5, rows_needed=22)
covid_by_age_band_diff_smoothed_per100k = covid_by_age_band_diff_smoothed.copy()
for col in covid_by_age_band_diff_smoothed_per100k.columns:
covid_by_age_band_diff_smoothed_per100k[col] = (
100000 * covid_by_age_band_diff_smoothed_per100k[col]
/ pop_by_age_band.loc[
pop_by_age_band['covid_age_band'] == col, 'pop'
].values
).round(0)
fig_age_per100k = go.Figure()
i=0
for col in covid_by_age_band_diff_smoothed_per100k.columns:
fig_age_per100k.add_trace(go.Scatter(
x=covid_by_age_band_diff_smoothed_per100k.index,
y=covid_by_age_band_diff_smoothed_per100k[col],
mode='lines', line_shape='spline',
line=dict(color=age_band_colors[i]), name=col))
i+=1
fig_age_per100k.update_layout(