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Regional covid-19 mortality in Belgium per gender and age

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- Apr 22, 2020
- 10 min read

Regional covid-19 mortality in Belgium per gender and age¶

Combines the mortality number of the last 10 year with those of covid-19 this year.

# Import pandas for data wrangling and Altair for plotting
import pandas as pd
import altair as alt

df_tot_sc = pd.read_excel('https://epistat.sciensano.be/Data/COVID19BE.xlsx')

df_inhab = pd.read_excel('https://statbel.fgov.be/sites/default/files/files/opendata/bevolking%20naar%20woonplaats%2C%20nationaliteit%20burgelijke%20staat%20%2C%20leeftijd%20en%20geslacht/TF_SOC_POP_STRUCT_2019.xlsx')

df_inhab

	CD_REFNIS	TX_DESCR_NL	TX_DESCR_FR	CD_DSTR_REFNIS	TX_ADM_DSTR_DESCR_NL	TX_ADM_DSTR_DESCR_FR	CD_PROV_REFNIS	TX_PROV_DESCR_NL	TX_PROV_DESCR_FR	CD_RGN_REFNIS	TX_RGN_DESCR_NL	TX_RGN_DESCR_FR	CD_SEX	CD_NATLTY	TX_NATLTY_NL	TX_NATLTY_FR	CD_CIV_STS	TX_CIV_STS_NL	TX_CIV_STS_FR	CD_AGE	MS_POPULATION
0	11001	Aartselaar	Aartselaar	11000	Arrondissement Antwerpen	Arrondissement d’Anvers	10000.0	Provincie Antwerpen	Province d’Anvers	2000	Vlaams Gewest	Région flamande	F	BEL	Belgen	Belges	4	Gescheiden	Divorcé	69	11
1	11001	Aartselaar	Aartselaar	11000	Arrondissement Antwerpen	Arrondissement d’Anvers	10000.0	Provincie Antwerpen	Province d’Anvers	2000	Vlaams Gewest	Région flamande	F	BEL	Belgen	Belges	4	Gescheiden	Divorcé	80	3
2	11001	Aartselaar	Aartselaar	11000	Arrondissement Antwerpen	Arrondissement d’Anvers	10000.0	Provincie Antwerpen	Province d’Anvers	2000	Vlaams Gewest	Région flamande	M	BEL	Belgen	Belges	4	Gescheiden	Divorcé	30	2
3	11001	Aartselaar	Aartselaar	11000	Arrondissement Antwerpen	Arrondissement d’Anvers	10000.0	Provincie Antwerpen	Province d’Anvers	2000	Vlaams Gewest	Région flamande	F	BEL	Belgen	Belges	4	Gescheiden	Divorcé	48	26
4	11001	Aartselaar	Aartselaar	11000	Arrondissement Antwerpen	Arrondissement d’Anvers	10000.0	Provincie Antwerpen	Province d’Anvers	2000	Vlaams Gewest	Région flamande	F	BEL	Belgen	Belges	4	Gescheiden	Divorcé	76	2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
463376	93090	Viroinval	Viroinval	93000	Arrondissement Philippeville	Arrondissement de Philippeville	90000.0	Provincie Namen	Province de Namur	3000	Waals Gewest	Région wallonne	F	BEL	Belgen	Belges	3	Weduwstaat	Veuf	73	10
463377	93090	Viroinval	Viroinval	93000	Arrondissement Philippeville	Arrondissement de Philippeville	90000.0	Provincie Namen	Province de Namur	3000	Waals Gewest	Région wallonne	M	BEL	Belgen	Belges	3	Weduwstaat	Veuf	64	1
463378	93090	Viroinval	Viroinval	93000	Arrondissement Philippeville	Arrondissement de Philippeville	90000.0	Provincie Namen	Province de Namur	3000	Waals Gewest	Région wallonne	M	BEL	Belgen	Belges	3	Weduwstaat	Veuf	86	3
463379	93090	Viroinval	Viroinval	93000	Arrondissement Philippeville	Arrondissement de Philippeville	90000.0	Provincie Namen	Province de Namur	3000	Waals Gewest	Région wallonne	M	ETR	niet-Belgen	non-Belges	3	Weduwstaat	Veuf	74	1
463380	93090	Viroinval	Viroinval	93000	Arrondissement Philippeville	Arrondissement de Philippeville	90000.0	Provincie Namen	Province de Namur	3000	Waals Gewest	Région wallonne	M	BEL	Belgen	Belges	3	Weduwstaat	Veuf	52	1

463381 rows × 21 columns

inhab_provence = df_inhab['TX_PROV_DESCR_NL'].dropna().unique()
inhab_provence

array(['Provincie Antwerpen', 'Provincie Vlaams-Brabant',
       'Provincie Waals-Brabant', 'Provincie West-Vlaanderen',
       'Provincie Oost-Vlaanderen', 'Provincie Henegouwen',
       'Provincie Luik', 'Provincie Limburg', 'Provincie Luxemburg',
       'Provincie Namen'], dtype=object)

sc_provence = df_tot_sc['PROVINCE'].unique()
sc_provence

array(['Brussels', 'Liège', 'Limburg', 'OostVlaanderen', 'VlaamsBrabant',
       'Antwerpen', 'WestVlaanderen', 'BrabantWallon', 'Hainaut', 'Namur',
       nan, 'Luxembourg'], dtype=object)

[p.split()[1] for p in inhab_provence]

['Antwerpen',
 'Vlaams-Brabant',
 'Waals-Brabant',
 'West-Vlaanderen',
 'Oost-Vlaanderen',
 'Henegouwen',
 'Luik',
 'Limburg',
 'Luxemburg',
 'Namen']

map_statbel_provence_to_sc_provence = {'Provincie Antwerpen':'Antwerpen', 'Provincie Vlaams-Brabant':'VlaamsBrabant',
       'Provincie Waals-Brabant':'BrabantWallon', 'Provincie West-Vlaanderen':'WestVlaanderen',
       'Provincie Oost-Vlaanderen':'OostVlaanderen', 'Provincie Henegouwen':'Hainaut',
       'Provincie Luik':'Liège', 'Provincie Limburg':'Limburg', 'Provincie Luxemburg':'Luxembourg',
       'Provincie Namen':'Namur'}

df_inhab['sc_provence'] = df_inhab['TX_PROV_DESCR_NL'].map(map_statbel_provence_to_sc_provence)

df_tot_sc['AGEGROUP'].unique()

array(['10-19', '20-29', '30-39', '40-49', '50-59', '70-79', '60-69',
       '0-9', '90+', '80-89', nan], dtype=object)

df_inhab['AGEGROUP'] =pd.cut(df_inhab['CD_AGE'], bins=[0,10,20,30,40,50,60,70,80,90,200], labels=['0-9','10-19','20-29','30-39','40-49','50-59','60-69','70-79','80-89','90+'], include_lowest=True)

df_inhab_gender_prov = df_inhab.groupby(['sc_provence', 'CD_SEX', 'AGEGROUP'])['MS_POPULATION'].sum().reset_index()

df_inhab_gender_prov_cases = pd.merge(df_inhab_gender_prov, df_tot_sc.dropna(), left_on=['sc_provence', 'AGEGROUP', 'CD_SEX'], right_on=['PROVINCE', 'AGEGROUP', 'SEX'])

df_inhab_gender_prov_cases.head()

	sc_provence	CD_SEX	AGEGROUP	MS_POPULATION	DATE	PROVINCE	REGION	SEX	CASES
0	Antwerpen	F	0-9	113851	2020-03-05	Antwerpen	Flanders	F	1
1	Antwerpen	F	0-9	113851	2020-03-18	Antwerpen	Flanders	F	1
2	Antwerpen	F	0-9	113851	2020-03-26	Antwerpen	Flanders	F	1
3	Antwerpen	F	0-9	113851	2020-03-30	Antwerpen	Flanders	F	1
4	Antwerpen	F	0-9	113851	2020-04-03	Antwerpen	Flanders	F	1

df_plot = df_inhab_gender_prov_cases.groupby(['SEX', 'AGEGROUP', 'PROVINCE']).agg(CASES = ('CASES', 'sum'), MS_POPULATION=('MS_POPULATION', 'first')).reset_index()
df_plot

	SEX	AGEGROUP	PROVINCE	CASES	MS_POPULATION
0	F	0-9	Antwerpen	9	113851
1	F	0-9	BrabantWallon	3	23744
2	F	0-9	Hainaut	11	81075
3	F	0-9	Limburg	11	48102
4	F	0-9	Liège	19	67479
...	...	...	...	...	...
195	M	90+	Luxembourg	17	469
196	M	90+	Namur	27	827
197	M	90+	OostVlaanderen	102	3105
198	M	90+	VlaamsBrabant	129	2611
199	M	90+	WestVlaanderen	121	3292

200 rows × 5 columns

df_plot['PROVINCE'].unique()

array(['Antwerpen', 'BrabantWallon', 'Hainaut', 'Limburg', 'Liège',
       'Luxembourg', 'Namur', 'OostVlaanderen', 'VlaamsBrabant',
       'WestVlaanderen'], dtype=object)

alt.Chart(df_plot).mark_bar().encode(x='AGEGROUP:N', y='CASES', color='SEX:N', column='PROVINCE:N')

df_plot['percentage'] = df_plot['CASES'] / df_plot['MS_POPULATION']

alt.Chart(df_plot).mark_bar().encode(x='AGEGROUP:N', y='percentage', color='SEX:N', column='PROVINCE:N')

Let's add a colorscale the makes the male blue and female number pink.

color_scale = alt.Scale(domain=['M', 'F'],
                        range=['#1f77b4', '#e377c2'])

alt.Chart(df_plot).mark_bar().encode(
    x='AGEGROUP:N', 
    y='percentage', 
    color=alt.Color('SEX:N', scale=color_scale, legend=None),
    column='PROVINCE:N')

The graph's get to wide. Let's use faceting to make two rows.

Inspired and based on https://altair-viz.github.io/gallery/us_population_pyramid_over_time.html

#slider = alt.binding_range(min=1850, max=2000, step=10)
# select_province = alt.selection_single(name='PROVINCE', fields=['PROVINCE'],
#                                    bind=slider, init={'PROVINCE': 'Antwerpen'})
color_scale = alt.Scale(domain=['Male', 'Female'],
                        range=['#1f77b4', '#e377c2'])

select_province = alt.selection_multi(fields=['PROVINCE'], bind='legend')

base = alt.Chart(df_plot).add_selection(
    select_province
).transform_filter(
    select_province
).transform_calculate(
    gender=alt.expr.if_(alt.datum.SEX == 'M', 'Male', 'Female')
).properties(
    width=250
)

left = base.transform_filter(
    alt.datum.gender == 'Female'
).encode(
    y=alt.Y('AGEGROUP:O', axis=None),
    x=alt.X('percentage:Q', axis=alt.Axis(format='.0%'),
            title='Percentage',
            sort=alt.SortOrder('descending'),
            ),
    color=alt.Color('gender:N', scale=color_scale, legend=None),
).mark_bar().properties(title='Female')

middle = base.encode(
    y=alt.Y('AGEGROUP:O', axis=None),
    text=alt.Text('AGEGROUP:O'),
).mark_text().properties(width=20)

right = base.transform_filter(
    alt.datum.gender == 'Male'
).encode(
    y=alt.Y('AGEGROUP:O', axis=None),
    x=alt.X('percentage:Q', title='Percentage', axis=alt.Axis(format='.0%'),),
    color=alt.Color('gender:N', scale=color_scale, legend=None)
).mark_bar().properties(title='Male')

# legend = alt.Chart(df_plot).mark_text().encode(
#     y=alt.Y('PROVINCE:N', axis=None),
#     text=alt.Text('PROVINCE:N'),
#     color=alt.Color('PROVINCE:N', legend=alt.Legend(title="Provincie"))
# )

alt.concat(left, middle, right, spacing=5)

#legend=alt.Legend(title="Species by color")

provinces = df_plot['PROVINCE'].unique()
select_province = alt.selection_single(
    name='Select', # name the selection 'Select'
    fields=['PROVINCE'], # limit selection to the Major_Genre field
    init={'PROVINCE': 'Antwerpen'}, # use first genre entry as initial value
    bind=alt.binding_select(options=provinces) # bind to a menu of unique provence values
)


base = alt.Chart(df_plot).add_selection(
    select_province
).transform_filter(
    select_province
).transform_calculate(
    gender=alt.expr.if_(alt.datum.SEX == 'M', 'Male', 'Female')
).properties(
    width=250
)

left = base.transform_filter(
    alt.datum.gender == 'Female'
).encode(
    y=alt.Y('AGEGROUP:O', axis=None),
    x=alt.X('percentage:Q', axis=alt.Axis(format='.0%'),
            title='Percentage',
            sort=alt.SortOrder('descending'),
            scale=alt.Scale(domain=(0.0, 0.1), clamp=True)
            ),
    color=alt.Color('gender:N', scale=color_scale, legend=None),
    tooltip=[alt.Tooltip('percentage', format='.1%')]
).mark_bar().properties(title='Female')

middle = base.encode(
    y=alt.Y('AGEGROUP:O', axis=None),
    text=alt.Text('AGEGROUP:O'),
).mark_text().properties(width=20)

right = base.transform_filter(
    alt.datum.gender == 'Male'
).encode(
    y=alt.Y('AGEGROUP:O', axis=None),
    x=alt.X('percentage:Q', title='Percentage', axis=alt.Axis(format='.1%'), scale=alt.Scale(domain=(0.0, 0.1), clamp=True)),
    color=alt.Color('gender:N', scale=color_scale, legend=None),
    tooltip=[alt.Tooltip('percentage', format='.1%')]
).mark_bar().properties(title='Male')

alt.concat(left, middle, right, spacing=5).properties(title='Percentage of covid-19 cases per province, gender and age grup in Belgium')

Mortality¶

# https://epistat.wiv-isp.be/covid/
# Dataset of mortality by date, age, sex, and region
df_dead_sc = pd.read_csv('https://epistat.sciensano.be/Data/COVID19BE_MORT.csv')

df_dead_sc.head()

	DATE	REGION	AGEGROUP	SEX	DEATHS
0	2020-03-10	Brussels	85+	F	1
1	2020-03-11	Flanders	85+	F	1
2	2020-03-11	Brussels	75-84	M	1
3	2020-03-11	Brussels	85+	F	1
4	2020-03-12	Brussels	75-84	M	1

df_dead_sc['REGION'].value_counts()

Wallonia    291
Flanders    275
Brussels    271
Name: REGION, dtype: int64

df_dead_sc['AGEGROUP'].value_counts()

85+      223
75-84    205
65-74    179
45-64    132
25-44     19
0-24       1
Name: AGEGROUP, dtype: int64

df_inhab['AGEGROUP_sc'] =pd.cut(df_inhab['CD_AGE'], bins=[0,24,44,64,74,84,200], labels=['0-24','25-44','45-64','65-74','75-84','85+'], include_lowest=True)

df_inhab.groupby('AGEGROUP_sc').agg(lowest_age=('CD_AGE', 'min'), highest_age=('CD_AGE', max))

	lowest_age	highest_age
AGEGROUP_sc
0-24	0	24
25-44	25	44
45-64	45	64
65-74	65	74
75-84	75	84
85+	85	110

df_inhab.head()

	CD_REFNIS	TX_DESCR_NL	TX_DESCR_FR	CD_DSTR_REFNIS	TX_ADM_DSTR_DESCR_NL	TX_ADM_DSTR_DESCR_FR	CD_PROV_REFNIS	TX_PROV_DESCR_NL	TX_PROV_DESCR_FR	CD_RGN_REFNIS	TX_RGN_DESCR_NL	TX_RGN_DESCR_FR	CD_SEX	CD_NATLTY	TX_NATLTY_NL	TX_NATLTY_FR	CD_CIV_STS	TX_CIV_STS_NL	TX_CIV_STS_FR	CD_AGE	MS_POPULATION	sc_provence	AGEGROUP	AGEGROUP_sc
0	11001	Aartselaar	Aartselaar	11000	Arrondissement Antwerpen	Arrondissement d’Anvers	10000.0	Provincie Antwerpen	Province d’Anvers	2000	Vlaams Gewest	Région flamande	F	BEL	Belgen	Belges	4	Gescheiden	Divorcé	69	11	Antwerpen	60-69	65-74
1	11001	Aartselaar	Aartselaar	11000	Arrondissement Antwerpen	Arrondissement d’Anvers	10000.0	Provincie Antwerpen	Province d’Anvers	2000	Vlaams Gewest	Région flamande	F	BEL	Belgen	Belges	4	Gescheiden	Divorcé	80	3	Antwerpen	70-79	75-84
2	11001	Aartselaar	Aartselaar	11000	Arrondissement Antwerpen	Arrondissement d’Anvers	10000.0	Provincie Antwerpen	Province d’Anvers	2000	Vlaams Gewest	Région flamande	M	BEL	Belgen	Belges	4	Gescheiden	Divorcé	30	2	Antwerpen	20-29	25-44
3	11001	Aartselaar	Aartselaar	11000	Arrondissement Antwerpen	Arrondissement d’Anvers	10000.0	Provincie Antwerpen	Province d’Anvers	2000	Vlaams Gewest	Région flamande	F	BEL	Belgen	Belges	4	Gescheiden	Divorcé	48	26	Antwerpen	40-49	45-64
4	11001	Aartselaar	Aartselaar	11000	Arrondissement Antwerpen	Arrondissement d’Anvers	10000.0	Provincie Antwerpen	Province d’Anvers	2000	Vlaams Gewest	Région flamande	F	BEL	Belgen	Belges	4	Gescheiden	Divorcé	76	2	Antwerpen	70-79	75-84

df_dead_sc['REGION'].unique()

array(['Brussels', 'Flanders', 'Wallonia'], dtype=object)

df_inhab['TX_RGN_DESCR_NL'].value_counts()

Vlaams Gewest                     242865
Waals Gewest                      199003
Brussels Hoofdstedelijk Gewest     21513
Name: TX_RGN_DESCR_NL, dtype: int64

df_inhab_gender_prov = df_inhab.groupby(['TX_RGN_DESCR_NL', 'CD_SEX', 'AGEGROUP_sc'])['MS_POPULATION'].sum().reset_index()

region_sc_to_region_inhad = {'Flanders':'Vlaams Gewest', 'Wallonia':'Waals Gewest', 'Brussels':'Brussels Hoofdstedelijk Gewest'}

df_dead_sc['TX_RGN_DESCR_NL'] = df_dead_sc['REGION'].map(region_sc_to_region_inhad)

df_dead_sc.groupby(['TX_RGN_DESCR_NL', 'AGEGROUP', 'SEX'])['DEATHS'].sum()

TX_RGN_DESCR_NL                 AGEGROUP  SEX
Brussels Hoofdstedelijk Gewest  25-44     F        1
                                          M        4
                                45-64     F       21
                                          M       43
                                65-74     F       42
                                          M       71
                                75-84     F      128
                                          M      170
                                85+       F      270
                                          M      186
Vlaams Gewest                   0-24      F        1
                                25-44     F        2
                                          M        3
                                45-64     F       27
                                          M       63
                                65-74     F       67
                                          M      130
                                75-84     F      199
                                          M      335
                                85+       F      232
                                          M      309
Waals Gewest                    25-44     F        5
                                          M        4
                                45-64     F       41
                                          M       89
                                65-74     F       98
                                          M      186
                                75-84     F      290
                                          M      300
                                85+       F      704
                                          M      421
Name: DEATHS, dtype: int64

df_dead_sc_region_agegroup_gender = df_dead_sc.groupby(['TX_RGN_DESCR_NL', 'AGEGROUP', 'SEX'])['DEATHS'].sum().reset_index()

df_inhab_gender_prov_deaths = pd.merge(df_inhab_gender_prov, df_dead_sc_region_agegroup_gender, left_on=['TX_RGN_DESCR_NL', 'AGEGROUP_sc', 'CD_SEX'], right_on=['TX_RGN_DESCR_NL', 'AGEGROUP', 'SEX'])

df_inhab_gender_prov_deaths['MS_POPULATION'].sum()

df_inhab_gender_prov_deaths['DEATHS'].sum()

df_inhab_gender_prov_deaths

	TX_RGN_DESCR_NL	CD_SEX	AGEGROUP_sc	MS_POPULATION	AGEGROUP	SEX	DEATHS
0	Brussels Hoofdstedelijk Gewest	F	25-44	197579	25-44	F	1
1	Brussels Hoofdstedelijk Gewest	F	45-64	137628	45-64	F	21
2	Brussels Hoofdstedelijk Gewest	F	65-74	45214	65-74	F	42
3	Brussels Hoofdstedelijk Gewest	F	75-84	30059	75-84	F	128
4	Brussels Hoofdstedelijk Gewest	F	85+	18811	85+	F	270
5	Brussels Hoofdstedelijk Gewest	M	25-44	194988	25-44	M	4
6	Brussels Hoofdstedelijk Gewest	M	45-64	140348	45-64	M	43
7	Brussels Hoofdstedelijk Gewest	M	65-74	36698	65-74	M	71
8	Brussels Hoofdstedelijk Gewest	M	75-84	19969	75-84	M	170
9	Brussels Hoofdstedelijk Gewest	M	85+	7918	85+	M	186
10	Vlaams Gewest	F	0-24	874891	0-24	F	1
11	Vlaams Gewest	F	25-44	820036	25-44	F	2
12	Vlaams Gewest	F	45-64	901554	45-64	F	27
13	Vlaams Gewest	F	65-74	353925	65-74	F	67
14	Vlaams Gewest	F	75-84	245981	75-84	F	199
15	Vlaams Gewest	F	85+	132649	85+	F	232
16	Vlaams Gewest	M	25-44	827281	25-44	M	3
17	Vlaams Gewest	M	45-64	917008	45-64	M	63
18	Vlaams Gewest	M	65-74	336242	65-74	M	130
19	Vlaams Gewest	M	75-84	193576	75-84	M	335
20	Vlaams Gewest	M	85+	69678	85+	M	309
21	Waals Gewest	F	25-44	457356	25-44	F	5
22	Waals Gewest	F	45-64	496668	45-64	F	41
23	Waals Gewest	F	65-74	199422	65-74	F	98
24	Waals Gewest	F	75-84	118224	75-84	F	290
25	Waals Gewest	F	85+	68502	85+	F	704
26	Waals Gewest	M	25-44	459444	25-44	M	4
27	Waals Gewest	M	45-64	487322	45-64	M	89
28	Waals Gewest	M	65-74	175508	65-74	M	186
29	Waals Gewest	M	75-84	82876	75-84	M	300
30	Waals Gewest	M	85+	30048	85+	M	421

df_inhab_gender_prov_deaths['percentage'] = df_inhab_gender_prov_deaths['DEATHS']/df_inhab_gender_prov_deaths['MS_POPULATION']

df_plot = df_inhab_gender_prov_deaths

regions = df_plot['TX_RGN_DESCR_NL'].unique()
select_province = alt.selection_single(
    name='Select', # name the selection 'Select'
    fields=['TX_RGN_DESCR_NL'], # limit selection to the Major_Genre field
    init={'TX_RGN_DESCR_NL': 'Vlaams Gewest'}, # use first genre entry as initial value
    bind=alt.binding_select(options=regions) # bind to a menu of unique provence values
)

base = alt.Chart(df_plot).add_selection(
    select_province
).transform_filter(
    select_province
).transform_calculate(
    gender=alt.expr.if_(alt.datum.SEX == 'M', 'Male', 'Female')
).properties(
    width=250
)

left = base.transform_filter(
    alt.datum.gender == 'Female'
).encode(
    y=alt.Y('AGEGROUP:O', axis=None),
    x=alt.X('percentage:Q', axis=alt.Axis(format='.2%'),
            title='Percentage',
            sort=alt.SortOrder('descending'),
            # scale=alt.Scale(domain=(0.0, 0.02), clamp=True)
            ),
    color=alt.Color('gender:N', scale=color_scale, legend=None),
    tooltip=[alt.Tooltip('percentage', format='.2%')]
).mark_bar().properties(title='Female')

middle = base.encode(
    y=alt.Y('AGEGROUP:O', axis=None),
    text=alt.Text('AGEGROUP:O'),
).mark_text().properties(width=20)

right = base.transform_filter(
    alt.datum.gender == 'Male'
).encode(
    y=alt.Y('AGEGROUP:O', axis=None),
    # x=alt.X('percentage:Q', title='Percentage', axis=alt.Axis(format='.2%'), scale=alt.Scale(domain=(0.0, 0.02), clamp=True)),
    x=alt.X('percentage:Q', title='Percentage', axis=alt.Axis(format='.2%')),
    color=alt.Color('gender:N', scale=color_scale, legend=None),
    tooltip=[alt.Tooltip('percentage', format='.2%')]
).mark_bar().properties(title='Male')

alt.concat(left, middle, right, spacing=5).properties(title='Percentage of covid-19 deaths per province, gender and age group relative to number of inhabitants in Belgium')

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