Importing and preparing the data

We will be looking at data from the following countries:

Italy
Austria
Germany
Belgium
France
United Kingdom
Portugal

We begin by importing the data, and adding some new features so that we can compare the data from different countries. For example we calculate 'confirmed cases per 100k population', 'deaths per 100k' and 'new cases' since these are not initially in the dataset. This data is collected and preprocessed in this file.

import altair as alt
import pandas as pd

x_small_url = "https://raw.githubusercontent.com/idjotherwise/nlp-otherwise/master/data_sets/european_covid.csv"

Here is a random sample of 5 rows from the dataset.

x_small = pd.read_csv(x_small_url)
x_small.sample(5)

Plotting the data

We will first look at the total numbers of cases and deaths in each country, before moving on to cases and deaths per 100k population.

In each of the charts below, you can click on the legend to filter the lines shown

Total cases per 100,000

leg_selection = alt.selection_multi(fields=['id'], bind='legend')

alt.Chart(x_small_url).mark_line().encode(
    x=alt.X("yearmonthdate(date):T", axis=alt.Axis(title='Date')),
    y=alt.Y("confirmed_per:Q", axis=alt.Axis(title='Confirmed per 100k')),
    tooltip=['id:N', 'confirmed_per:Q'],
    color=alt.Color('id:N', legend=alt.Legend(title="Countries")),
    opacity=alt.condition(leg_selection, alt.value(1), alt.value(0.2))
).transform_filter(alt.datum.confirmed_per>0).add_selection(leg_selection).properties(title='Total number of cases per 100,000 population for selected European Countries', width=600).interactive()

Total deaths per 100,000

alt.Chart(x_small_url).mark_line().encode(
    x=alt.X("yearmonthdate(date):T", axis=alt.Axis(title='Date')),
    y=alt.Y("deaths_per:Q", axis=alt.Axis(title='Deaths per 100k'), impute=alt.ImputeParams(value=50)),
    tooltip=["id:N", "deaths_per:Q", "yearmonthdate(date):T"],
    color=alt.Color('id:N', legend=alt.Legend(title="Countries")),
    opacity=alt.condition(leg_selection, alt.value(1), alt.value(0.2))
).transform_filter(alt.datum.deaths_per>0).add_selection(leg_selection).properties(title='Number of deaths per 100,000 population for selected European Countries', width=600).interactive()

Two week incidence rate

brush = alt.selection(type='interval', encodings=['x'])

base = alt.Chart(x_small_url).mark_line().transform_filter(alt.datum.new_cases_per>0).transform_window(
    rolling_mean='sum(new_cases_per)',
    frame=[-7, 0],
    groupby=['id:N']
).encode(
    x=alt.X("yearmonthdate(date):T",
            axis=alt.Axis(title='Date')
           ),
    y=alt.Y("rolling_mean:Q",
            axis=alt.Axis(title='Incidence rate')
           ),
    tooltip=['id:N', 'rolling_mean:Q'],
    color=alt.Color('id:N', legend=alt.Legend(title="Countries")),
    opacity=alt.condition(leg_selection, alt.value(1), alt.value(0.2))
).add_selection(leg_selection).properties(
    width=600,
    height=400,
    title='Number of new cases per 100,000 over two weeks for selected countries'
)

upper = base.encode(
    alt.X('yearmonthdate(date):T',axis=alt.Axis(title='Date'),
          scale=alt.Scale(domain=brush))
)

lower = base.properties(
    height=60
).add_selection(brush)

upper & lower

The ratio of confirmed cases and deaths gives an indication of what the case fatality rate is - it seems to be between 2 and 3%, assuming that the countries listed here are catching all positive cases (which they probably aren't, so it's likely lower than this).

Case fatality rate

base = alt.Chart(x_small_url).mark_line().transform_filter(alt.datum.ratio>0).encode(
    x=alt.X("yearmonthdate(date):T", axis=alt.Axis(title='Date')),
    y=alt.Y("ratio:Q", axis=alt.Axis(title='Ratio of deaths per case')),
    tooltip='id:N',
    color=alt.Color('id:N', legend=alt.Legend(title="Countries")),
opacity=alt.condition(leg_selection, alt.value(1), alt.value(0.2))
).add_selection(leg_selection).properties(title='The ratio of deaths to confirmed cases (case fatality rate)', width=600)

upper = base.encode(
    alt.X('yearmonthdate(date):T',axis=alt.Axis(title='Date'),
          scale=alt.Scale(domain=brush))
)

lower = base.properties(
    height=60
).add_selection(brush)

upper & lower

Vaccines

In the chart below we plot the number vaccines given per population - this means that if the number is 1, then the country has given the equivalent of 1 shot for each person in the country. Since not everyone in the countries are eligible to get the vaccine, a ratio of 1 means that many people have recieved two jabs. Note also that some kinds of vaccines (the J&J's Janssen vaccine, for example) only require 1 shot so the goal is not neccesarily to reach exactly 2 shots per person in the whole country.

alt.Chart(x_small_url).mark_line().transform_filter(alt.datum.vaccines_per>0).encode(
    x=alt.X("yearmonthdate(date):T", axis=alt.Axis(title='Date')),
    y=alt.Y("vaccines_per:Q", axis=alt.Axis(title='Number of vaccines given')),
    tooltip=['id:N', 'vaccines_per:Q'],
    color=alt.Color('id:N', legend=alt.Legend(title="Countries")),
    opacity=alt.condition(leg_selection, alt.value(1), alt.value(0.2))
).add_selection(leg_selection).properties(title='Number of vaccines given', width=600).interactive()

	id	date	vaccines	confirmed	tests	recovered	deaths	population	confirmed_per	deaths_per	ratio	tests_per	vaccines_per	new_cases	new_cases_per
1891	Portugal	2021-09-02	14891181.0	1042322.0	17153567.0	980599.0	17766.0	10283822.0	10135.550771	172.756782	1.704464	166801.477116	1.448020	2830.0	27.518952
4454	United Kingdom	2021-05-07	52433184.0	4429046.0	160342371.0	NaN	127740.0	66460344.0	6664.193613	192.204843	2.884143	241260.218274	0.788939	1885.0	2.836278
4708	Switzerland	2020-03-06	NaN	315.0	NaN	NaN	2.0	8513227.0	3.700125	0.023493	0.634921	NaN	NaN	73.0	0.857489
3641	Austria	2020-12-19	NaN	336093.0	NaN	297323.0	5628.0	8840521.0	3801.732952	63.661406	1.674537	NaN	NaN	1505.0	17.023884
326	Italy	2021-01-15	1125736.0	2352423.0	28734210.0	1713030.0	81325.0	60421760.0	3893.337433	134.595550	3.457074	47556.062584	0.018631	16144.0	26.718851