ClickHouse/docs/en/getting-started/example-datasets/covid19.md

slug	sidebar_label
/en/getting-started/example-datasets/covid19	COVID-19 Open-Data

COVID-19 Open-Data

COVID-19 Open-Data attempts to assemble the largest Covid-19 epidemiological database, in addition to a powerful set of expansive covariates. It includes open, publicly sourced, licensed data relating to demographics, economy, epidemiology, geography, health, hospitalizations, mobility, government response, weather, and more.

The details are in GitHub here.

It's easy to insert this data into ClickHouse...

:::note The following commands were executed on a Production instance of ClickHouse Cloud. You can easily run them on a local install as well. :::

1. Let's see what the data looks like:

DESCRIBE url(
    'https://storage.googleapis.com/covid19-open-data/v3/epidemiology.csv',
    'CSVWithNames'
);

The CSV file has 10 columns:

┌─name─────────────────┬─type─────────────┐
│ date                 │ Nullable(String) │
│ location_key         │ Nullable(String) │
│ new_confirmed        │ Nullable(Int64)  │
│ new_deceased         │ Nullable(Int64)  │
│ new_recovered        │ Nullable(Int64)  │
│ new_tested           │ Nullable(Int64)  │
│ cumulative_confirmed │ Nullable(Int64)  │
│ cumulative_deceased  │ Nullable(Int64)  │
│ cumulative_recovered │ Nullable(Int64)  │
│ cumulative_tested    │ Nullable(Int64)  │
└──────────────────────┴──────────────────┘

10 rows in set. Elapsed: 0.745 sec.

2. Now let's view some of the rows:

SELECT *
FROM url('https://storage.googleapis.com/covid19-open-data/v3/epidemiology.csv')
LIMIT 100;

Notice the url function easily reads data from a CSV file:

┌─c1─────────┬─c2───────────┬─c3────────────┬─c4───────────┬─c5────────────┬─c6─────────┬─c7───────────────────┬─c8──────────────────┬─c9───────────────────┬─c10───────────────┐
│ date       │ location_key │ new_confirmed │ new_deceased │ new_recovered │ new_tested │ cumulative_confirmed │ cumulative_deceased │ cumulative_recovered │ cumulative_tested │
│ 2020-04-03 │ AD           │ 24            │ 1            │ ᴺᵁᴸᴸ          │ ᴺᵁᴸᴸ       │ 466                  │ 17                  │ ᴺᵁᴸᴸ                 │ ᴺᵁᴸᴸ              │
│ 2020-04-04 │ AD           │ 57            │ 0            │ ᴺᵁᴸᴸ          │ ᴺᵁᴸᴸ       │ 523                  │ 17                  │ ᴺᵁᴸᴸ                 │ ᴺᵁᴸᴸ              │
│ 2020-04-05 │ AD           │ 17            │ 4            │ ᴺᵁᴸᴸ          │ ᴺᵁᴸᴸ       │ 540                  │ 21                  │ ᴺᵁᴸᴸ                 │ ᴺᵁᴸᴸ              │
│ 2020-04-06 │ AD           │ 11            │ 1            │ ᴺᵁᴸᴸ          │ ᴺᵁᴸᴸ       │ 551                  │ 22                  │ ᴺᵁᴸᴸ                 │ ᴺᵁᴸᴸ              │
│ 2020-04-07 │ AD           │ 15            │ 2            │ ᴺᵁᴸᴸ          │ ᴺᵁᴸᴸ       │ 566                  │ 24                  │ ᴺᵁᴸᴸ                 │ ᴺᵁᴸᴸ              │
│ 2020-04-08 │ AD           │ 23            │ 2            │ ᴺᵁᴸᴸ          │ ᴺᵁᴸᴸ       │ 589                  │ 26                  │ ᴺᵁᴸᴸ                 │ ᴺᵁᴸᴸ              │
└────────────┴──────────────┴───────────────┴──────────────┴───────────────┴────────────┴──────────────────────┴─────────────────────┴──────────────────────┴───────────────────┘

3. We will create a table now that we know what the data looks like:

CREATE TABLE covid19 (
    date Date,
    location_key LowCardinality(String),
    new_confirmed Int32,
    new_deceased Int32,
    new_recovered Int32,
    new_tested Int32,
    cumulative_confirmed Int32,
    cumulative_deceased Int32,
    cumulative_recovered Int32,
    cumulative_tested Int32
)
ENGINE = MergeTree
ORDER BY (location_key, date);

4. The following command inserts the entire dataset into the covid19 table:

INSERT INTO covid19
   SELECT *
   FROM
      url(
        'https://storage.googleapis.com/covid19-open-data/v3/epidemiology.csv',
        CSVWithNames,
        'date Date,
        location_key LowCardinality(String),
        new_confirmed Int32,
        new_deceased Int32,
        new_recovered Int32,
        new_tested Int32,
        cumulative_confirmed Int32,
        cumulative_deceased Int32,
        cumulative_recovered Int32,
        cumulative_tested Int32'
    );

5. It goes pretty quick - let's see how many rows were inserted:

SELECT formatReadableQuantity(count())
FROM covid19;

┌─formatReadableQuantity(count())─┐
│ 12.53 million                   │
└─────────────────────────────────┘

6. Let's see how many total cases of Covid-19 were recorded:

SELECT formatReadableQuantity(sum(new_confirmed))
FROM covid19;

┌─formatReadableQuantity(sum(new_confirmed))─┐
│ 1.39 billion                               │
└────────────────────────────────────────────┘

7. You will notice the data has a lot of 0's for dates - either weekends or days when numbers were not reported each day. We can use a window function to smooth out the daily averages of new cases:

SELECT
   AVG(new_confirmed) OVER (PARTITION BY location_key ORDER BY date ROWS BETWEEN 2 PRECEDING AND 2 FOLLOWING) AS cases_smoothed,
   new_confirmed,
   location_key,
   date
FROM covid19;

8. This query determines the latest values for each location. We can't use max(date) because not all countries reported every day, so we grab the last row using ROW_NUMBER:

WITH latest_deaths_data AS
   ( SELECT location_key,
            date,
            new_deceased,
            new_confirmed,
            ROW_NUMBER() OVER (PARTITION BY location_key ORDER BY date DESC) as rn
     FROM covid19)
SELECT location_key,
       date,
       new_deceased,
       new_confirmed,
       rn
FROM latest_deaths_data
WHERE rn=1;

9. We can use lagInFrame to determine the LAG of new cases each day. In this query we filter by the US_DC location:

SELECT
   new_confirmed - lagInFrame(new_confirmed,1) OVER (PARTITION BY location_key ORDER BY date) AS confirmed_cases_delta,
   new_confirmed,
   location_key,
   date
FROM covid19
WHERE location_key = 'US_DC';

The response look like:

┌─confirmed_cases_delta─┬─new_confirmed─┬─location_key─┬───────date─┐
│                     0 │             0 │ US_DC        │ 2020-03-08 │
│                     2 │             2 │ US_DC        │ 2020-03-09 │
│                    -2 │             0 │ US_DC        │ 2020-03-10 │
│                     6 │             6 │ US_DC        │ 2020-03-11 │
│                    -6 │             0 │ US_DC        │ 2020-03-12 │
│                     0 │             0 │ US_DC        │ 2020-03-13 │
│                     6 │             6 │ US_DC        │ 2020-03-14 │
│                    -5 │             1 │ US_DC        │ 2020-03-15 │
│                     4 │             5 │ US_DC        │ 2020-03-16 │
│                     4 │             9 │ US_DC        │ 2020-03-17 │
│                    -1 │             8 │ US_DC        │ 2020-03-18 │
│                    24 │            32 │ US_DC        │ 2020-03-19 │
│                   -26 │             6 │ US_DC        │ 2020-03-20 │
│                    15 │            21 │ US_DC        │ 2020-03-21 │
│                    -3 │            18 │ US_DC        │ 2020-03-22 │
│                     3 │            21 │ US_DC        │ 2020-03-23 │

10. This query calculates the percentage of change in new cases each day, and includes a simple increase or decrease column in the result set:

WITH confirmed_lag AS (
  SELECT
    *,
    lagInFrame(new_confirmed) OVER(
      PARTITION BY location_key
      ORDER BY date
    ) AS confirmed_previous_day
  FROM covid19
),
confirmed_percent_change AS (
  SELECT
    *,
    COALESCE(ROUND((new_confirmed - confirmed_previous_day) / confirmed_previous_day * 100), 0) AS percent_change
  FROM confirmed_lag
)
SELECT
  date,
  new_confirmed,
  percent_change,
  CASE
    WHEN percent_change > 0 THEN 'increase'
    WHEN percent_change = 0 THEN 'no change'
    ELSE 'decrease'
  END AS trend
FROM confirmed_percent_change
WHERE location_key = 'US_DC';

The results look like

┌───────date─┬─new_confirmed─┬─percent_change─┬─trend─────┐
│ 2020-03-08 │             0 │            nan │ decrease  │
│ 2020-03-09 │             2 │            inf │ increase  │
│ 2020-03-10 │             0 │           -100 │ decrease  │
│ 2020-03-11 │             6 │            inf │ increase  │
│ 2020-03-12 │             0 │           -100 │ decrease  │
│ 2020-03-13 │             0 │            nan │ decrease  │
│ 2020-03-14 │             6 │            inf │ increase  │
│ 2020-03-15 │             1 │            -83 │ decrease  │
│ 2020-03-16 │             5 │            400 │ increase  │
│ 2020-03-17 │             9 │             80 │ increase  │
│ 2020-03-18 │             8 │            -11 │ decrease  │
│ 2020-03-19 │            32 │            300 │ increase  │
│ 2020-03-20 │             6 │            -81 │ decrease  │
│ 2020-03-21 │            21 │            250 │ increase  │
│ 2020-03-22 │            18 │            -14 │ decrease  │
│ 2020-03-23 │            21 │             17 │ increase  │
│ 2020-03-24 │            46 │            119 │ increase  │
│ 2020-03-25 │            48 │              4 │ increase  │
│ 2020-03-26 │            36 │            -25 │ decrease  │
│ 2020-03-27 │            37 │              3 │ increase  │
│ 2020-03-28 │            38 │              3 │ increase  │
│ 2020-03-29 │            59 │             55 │ increase  │
│ 2020-03-30 │            94 │             59 │ increase  │
│ 2020-03-31 │            91 │             -3 │ decrease  │
│ 2020-04-01 │            67 │            -26 │ decrease  │
│ 2020-04-02 │           104 │             55 │ increase  │
│ 2020-04-03 │           145 │             39 │ increase  │

:::note As mentioned in the GitHub repo, the dataset is no longer updated as of September 15, 2022. :::