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=[-14, 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
2725	Germany	2020-09-29	NaN	291204.0	NaN	281353.0	9851.0	82905782.0	351.246913	11.882163	3.382852	NaN	NaN	2165.0	2.611398
4226	Austria	2020-04-14	NaN	14389.0	NaN	8590.0	450.0	8840521.0	162.761901	5.090198	3.127389	NaN	NaN	150.0	1.696733
2484	Germany	2020-02-01	NaN	12.0	NaN	12.0	0.0	82905782.0	0.014474	0.000000	0.000000	NaN	NaN	1.0	0.001206
5425	United Kingdom	2021-04-04	37013749.0	4439471.0	127143632.0	NaN	127655.0	66460344.0	6679.879659	192.076947	2.875455	191307.514147	0.556930	2412.0	3.629232
1215	Belgium	2021-02-21	750465.0	752879.0	9212254.0	NaN	22218.0	11433256.0	6584.992062	194.327845	2.951072	80574.195137	0.065639	805.0	7.040864