14-day cumulative number of COVID-19 cases per 100 000

At the end of the page, we provide a detailed description of how the numbers are calculated.

Compute data

In [1]:
import pandas as pd
pd.set_option("display.max_rows", None)
from oscovida import get_incidence_rates_countries

Table for all countries

In [2]:
cases_incidence, deaths_incidence = get_incidence_rates_countries()

Downloaded data: last data point 3/6/23 from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv
Downloaded data: last data point 3/6/23 from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv

/tank/oscovida/work/oscovida.github.io/oscovida.github.io/.venv/lib/python3.9/site-packages/oscovida/oscovida.py:597: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  cases = cases.groupby(cases.index).sum().astype(int)
/tank/oscovida/work/oscovida.github.io/oscovida.github.io/.venv/lib/python3.9/site-packages/oscovida/oscovida.py:598: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  deaths = deaths.groupby(deaths.index).sum().astype(int)
/tank/oscovida/work/oscovida.github.io/oscovida.github.io/.venv/lib/python3.9/site-packages/oscovida/oscovida.py:520: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  population
In [3]:
cases_incidence
Out[3]:
14-day-sum population 14-day-incidence-rate
Country
Afghanistan 253 38928341 0.6
Albania 112 2877800 3.9
Algeria 51 43851043 0.1
Andorra 9 77265 11.6
Angola 93 32866268 0.3
Antarctica 0 0 NaN
Antigua and Barbuda 0 97928 0.0
Argentina 1649 45195777 3.6
Armenia 487 2963234 16.4
Australia 37836 25459700 148.6
Austria 76997 9006400 854.9
Azerbaijan 372 10139175 3.7
Bahamas 0 393248 0.0
Bahrain 4492 1701583 264.0
Bangladesh 91 164689383 0.1
Barbados 211 287371 73.4
Belarus 0 9449321 0.0
Belgium 19522 11492641 169.9
Belize 17 397621 4.3
Benin 0 12123198 0.0
Bhutan 9 771612 1.2
Bolivia 1766 11673029 15.1
Bosnia and Herzegovina 109 3280815 3.3
Botswana 65 2351625 2.8
Brazil 88371 212559409 41.6
Brunei 1257 437483 287.3
Bulgaria 944 6948445 13.6
Burkina Faso 0 20903278 0.0
Burma 46 54409794 0.1
Burundi 22 11890781 0.2
Cabo Verde 5 555988 0.9
Cambodia 11 16718971 0.1
Cameroon 64 26545864 0.2
Canada 18299 38246108 47.8
Central African Republic 0 4829764 0.0
Chad 3 16425859 0.0
Chile 31734 19116209 166.0
China 3 1411778724 0.0
Colombia 1764 50882884 3.5
Comoros 4 869595 0.5
Congo (Brazzaville) 4 5518092 0.1
Congo (Kinshasa) 103 89561404 0.1
Costa Rica 9580 5094114 188.1
Cote d'Ivoire 132 26378275 0.5
Croatia 722 4105268 17.6
Cuba 78 11326616 0.7
Cyprus 3654 1207361 302.6
Czechia 12966 10708982 121.1
Denmark 1532 5837213 26.2
Diamond Princess 0 0 NaN
Djibouti 0 988002 0.0
Dominica 0 71991 0.0
Dominican Republic 213 10847904 2.0
Ecuador 543 17643060 3.1
Egypt 0 102334403 0.0
El Salvador 0 6486201 0.0
Equatorial Guinea 7 1402985 0.5
Eritrea 0 3546427 0.0
Estonia 580 1326539 43.7
Eswatini 68 1160164 5.9
Ethiopia 188 114963583 0.2
Fiji 21 896444 2.3
Finland 1393 5540718 25.1
France 52120 65249843 79.9
Gabon 0 2225728 0.0
Gambia 0 2416664 0.0
Georgia 3553 3989175 89.1
Germany 192740 83155031 231.8
Ghana 49 31072945 0.2
Greece 0 10423056 0.0
Grenada 0 112519 0.0
Guatemala 3506 17915567 19.6
Guinea 0 13132792 0.0
Guinea-Bissau 7 1967998 0.4
Guyana 23 786559 2.9
Haiti 59 11402533 0.5
Holy See 0 809 0.0
Honduras 693 9904608 7.0
Hungary 1824 9660350 18.9
Iceland 172 341250 50.4
India 3235 1380004385 0.2
Indonesia 2969 273523621 1.1
Iran 3940 83992953 4.7
Iraq 0 40222503 0.0
Ireland 1327 4937796 26.9
Israel 7215 8655541 83.4
Italy 56096 60461828 92.8
Jamaica 131 2961161 4.4
Japan 166539 126476458 131.7
Jordan 0 10203140 0.0
Kazakhstan 1219 18776707 6.5
Kenya 45 53771300 0.1
Kiribati 1 117606 0.9
Korea, North 0 25778815 0.0
Korea, South 135724 51269183 264.7
Kosovo 373 1810366 20.6
Kuwait 598 4270563 14.0
Kyrgyzstan 62 6524191 1.0
Laos 8 7275556 0.1
Latvia 340 1886202 18.0
Lebanon 1477 6825442 21.6
Lesotho 0 2142252 0.0
Liberia 25 5057677 0.5
Libya 16 6871287 0.2
Liechtenstein 39 38137 102.3
Lithuania 4465 2722291 164.0
Luxembourg 823 625976 131.5
MS Zaandam 0 0 NaN
Madagascar 37 27691019 0.1
Malawi 62 19129955 0.3
Malaysia 2804 32365998 8.7
Maldives 9 540542 1.7
Mali 138 20250834 0.7
Malta 229 441539 51.9
Marshall Islands 20 58413 34.2
Mauritania 1 4649660 0.0
Mauritius 279 1271767 21.9
Mexico 40875 127792286 32.0
Micronesia 277 113815 243.4
Moldova 6534 4027690 162.2
Monaco 13 39244 33.1
Mongolia 2 3278292 0.1
Montenegro 1071 628062 170.5
Morocco 104 36910558 0.3
Mozambique 0 31255435 0.0
Namibia 97 2540916 3.8
Nauru 0 10834 0.0
Nepal 14 29136808 0.0
Netherlands 8486 17134873 49.5
New Zealand 20545 4822233 426.0
Nicaragua 11 6624554 0.2
Niger 0 24206636 0.0
Nigeria 135 206139587 0.1
North Macedonia 119 2083380 5.7
Norway 662 5421242 12.2
Oman 0 5106622 0.0
Pakistan 507 220892331 0.2
Palau 1 18008 5.6
Panama 1073 4314768 24.9
Papua New Guinea 17 8947027 0.2
Paraguay 948 7132530 13.3
Peru 2124 32971846 6.4
Philippines 1564 109581085 1.4
Poland 32154 37846605 85.0
Portugal 1376 10196707 13.5
Qatar 1247 2881060 43.3
Romania 10915 19237682 56.7
Russia 173179 145934460 118.7
Rwanda 10 12952209 0.1
Saint Kitts and Nevis 1 53192 1.9
Saint Lucia 122 183629 66.4
Saint Vincent and the Grenadines 0 110947 0.0
Samoa 13 196130 6.6
San Marino 62 33938 182.7
Sao Tome and Principe 1 219161 0.5
Saudi Arabia 899 34813867 2.6
Senegal 11 16743930 0.1
Serbia 11423 8737370 130.7
Seychelles 0 98340 0.0
Sierra Leone 0 7976985 0.0
Singapore 8862 5850343 151.5
Slovakia 2807 5434712 51.6
Slovenia 4385 2078932 210.9
Solomon Islands 0 652858 0.0
Somalia 0 15893219 0.0
South Africa 4772 59308690 8.0
South Sudan 0 11193729 0.0
Spain 14473 46754783 31.0
Sri Lanka 12 21413250 0.1
Sudan 34 43849269 0.1
Summer Olympics 2020 0 0 NaN
Suriname 240 586634 40.9
Sweden 1376 10099270 13.6
Switzerland 2611 8654618 30.2
Syria 4 17500657 0.0
Taiwan* 33721 23816775 141.6
Tajikistan 0 9537642 0.0
Tanzania 71 59734213 0.1
Thailand 351 69799978 0.5
Timor-Leste 0 1318442 0.0
Togo 28 8278737 0.3
Tonga 6 105697 5.7
Trinidad and Tobago 785 1399491 56.1
Tunisia 356 11818618 3.0
Turkey 0 84339067 0.0
Tuvalu 0 11792 0.0
US 519271 329466283 157.6
Uganda 26 45741000 0.1
Ukraine 8745 43733759 20.0
United Arab Emirates 1502 9890400 15.2
United Kingdom 55023 67886004 81.1
Uruguay 257 3473727 7.4
Uzbekistan 198 33469199 0.6
Vanuatu 0 292680 0.0
Venezuela 214 28435943 0.8
Vietnam 141 97338583 0.1
West Bank and Gaza 0 5101416 0.0
Winter Olympics 2022 0 0 NaN
Yemen 0 29825968 0.0
Zambia 411 18383956 2.2
Zimbabwe 485 14862927 3.3

Table sorted by 14-day-incidence

In [4]:
cases_incidence.sort_values(by=['14-day-incidence-rate'], ascending=False)
Out[4]:
14-day-sum population 14-day-incidence-rate
Country
Austria 76997 9006400 854.9
New Zealand 20545 4822233 426.0
Cyprus 3654 1207361 302.6
Brunei 1257 437483 287.3
Korea, South 135724 51269183 264.7
Bahrain 4492 1701583 264.0
Micronesia 277 113815 243.4
Germany 192740 83155031 231.8
Slovenia 4385 2078932 210.9
Costa Rica 9580 5094114 188.1
San Marino 62 33938 182.7
Montenegro 1071 628062 170.5
Belgium 19522 11492641 169.9
Chile 31734 19116209 166.0
Lithuania 4465 2722291 164.0
Moldova 6534 4027690 162.2
US 519271 329466283 157.6
Singapore 8862 5850343 151.5
Australia 37836 25459700 148.6
Taiwan* 33721 23816775 141.6
Japan 166539 126476458 131.7
Luxembourg 823 625976 131.5
Serbia 11423 8737370 130.7
Czechia 12966 10708982 121.1
Russia 173179 145934460 118.7
Liechtenstein 39 38137 102.3
Italy 56096 60461828 92.8
Georgia 3553 3989175 89.1
Poland 32154 37846605 85.0
Israel 7215 8655541 83.4
United Kingdom 55023 67886004 81.1
France 52120 65249843 79.9
Barbados 211 287371 73.4
Saint Lucia 122 183629 66.4
Romania 10915 19237682 56.7
Trinidad and Tobago 785 1399491 56.1
Malta 229 441539 51.9
Slovakia 2807 5434712 51.6
Iceland 172 341250 50.4
Netherlands 8486 17134873 49.5
Canada 18299 38246108 47.8
Estonia 580 1326539 43.7
Qatar 1247 2881060 43.3
Brazil 88371 212559409 41.6
Suriname 240 586634 40.9
Marshall Islands 20 58413 34.2
Monaco 13 39244 33.1
Mexico 40875 127792286 32.0
Spain 14473 46754783 31.0
Switzerland 2611 8654618 30.2
Ireland 1327 4937796 26.9
Denmark 1532 5837213 26.2
Finland 1393 5540718 25.1
Panama 1073 4314768 24.9
Mauritius 279 1271767 21.9
Lebanon 1477 6825442 21.6
Kosovo 373 1810366 20.6
Ukraine 8745 43733759 20.0
Guatemala 3506 17915567 19.6
Hungary 1824 9660350 18.9
Latvia 340 1886202 18.0
Croatia 722 4105268 17.6
Armenia 487 2963234 16.4
United Arab Emirates 1502 9890400 15.2
Bolivia 1766 11673029 15.1
Kuwait 598 4270563 14.0
Bulgaria 944 6948445 13.6
Sweden 1376 10099270 13.6
Portugal 1376 10196707 13.5
Paraguay 948 7132530 13.3
Norway 662 5421242 12.2
Andorra 9 77265 11.6
Malaysia 2804 32365998 8.7
South Africa 4772 59308690 8.0
Uruguay 257 3473727 7.4
Honduras 693 9904608 7.0
Samoa 13 196130 6.6
Kazakhstan 1219 18776707 6.5
Peru 2124 32971846 6.4
Eswatini 68 1160164 5.9
North Macedonia 119 2083380 5.7
Tonga 6 105697 5.7
Palau 1 18008 5.6
Iran 3940 83992953 4.7
Jamaica 131 2961161 4.4
Belize 17 397621 4.3
Albania 112 2877800 3.9
Namibia 97 2540916 3.8
Azerbaijan 372 10139175 3.7
Argentina 1649 45195777 3.6
Colombia 1764 50882884 3.5
Zimbabwe 485 14862927 3.3
Bosnia and Herzegovina 109 3280815 3.3
Ecuador 543 17643060 3.1
Tunisia 356 11818618 3.0
Guyana 23 786559 2.9
Botswana 65 2351625 2.8
Saudi Arabia 899 34813867 2.6
Fiji 21 896444 2.3
Zambia 411 18383956 2.2
Dominican Republic 213 10847904 2.0
Saint Kitts and Nevis 1 53192 1.9
Maldives 9 540542 1.7
Philippines 1564 109581085 1.4
Bhutan 9 771612 1.2
Indonesia 2969 273523621 1.1
Kyrgyzstan 62 6524191 1.0
Cabo Verde 5 555988 0.9
Kiribati 1 117606 0.9
Venezuela 214 28435943 0.8
Mali 138 20250834 0.7
Cuba 78 11326616 0.7
Uzbekistan 198 33469199 0.6
Afghanistan 253 38928341 0.6
Liberia 25 5057677 0.5
Haiti 59 11402533 0.5
Comoros 4 869595 0.5
Cote d'Ivoire 132 26378275 0.5
Sao Tome and Principe 1 219161 0.5
Equatorial Guinea 7 1402985 0.5
Thailand 351 69799978 0.5
Guinea-Bissau 7 1967998 0.4
Malawi 62 19129955 0.3
Togo 28 8278737 0.3
Angola 93 32866268 0.3
Morocco 104 36910558 0.3
Nicaragua 11 6624554 0.2
Ghana 49 31072945 0.2
Burundi 22 11890781 0.2
Libya 16 6871287 0.2
India 3235 1380004385 0.2
Cameroon 64 26545864 0.2
Pakistan 507 220892331 0.2
Ethiopia 188 114963583 0.2
Papua New Guinea 17 8947027 0.2
Vietnam 141 97338583 0.1
Tanzania 71 59734213 0.1
Uganda 26 45741000 0.1
Sri Lanka 12 21413250 0.1
Bangladesh 91 164689383 0.1
Algeria 51 43851043 0.1
Cambodia 11 16718971 0.1
Sudan 34 43849269 0.1
Burma 46 54409794 0.1
Senegal 11 16743930 0.1
Congo (Brazzaville) 4 5518092 0.1
Rwanda 10 12952209 0.1
Nigeria 135 206139587 0.1
Kenya 45 53771300 0.1
Laos 8 7275556 0.1
Madagascar 37 27691019 0.1
Congo (Kinshasa) 103 89561404 0.1
Mongolia 2 3278292 0.1
Egypt 0 102334403 0.0
Antigua and Barbuda 0 97928 0.0
Benin 0 12123198 0.0
Belarus 0 9449321 0.0
Guinea 0 13132792 0.0
Turkey 0 84339067 0.0
Tuvalu 0 11792 0.0
Mauritania 1 4649660 0.0
Chad 3 16425859 0.0
Bahamas 0 393248 0.0
Holy See 0 809 0.0
Timor-Leste 0 1318442 0.0
Lesotho 0 2142252 0.0
Iraq 0 40222503 0.0
Vanuatu 0 292680 0.0
Jordan 0 10203140 0.0
China 3 1411778724 0.0
West Bank and Gaza 0 5101416 0.0
Yemen 0 29825968 0.0
Saint Vincent and the Grenadines 0 110947 0.0
Grenada 0 112519 0.0
Greece 0 10423056 0.0
Central African Republic 0 4829764 0.0
South Sudan 0 11193729 0.0
Seychelles 0 98340 0.0
Sierra Leone 0 7976985 0.0
Oman 0 5106622 0.0
El Salvador 0 6486201 0.0
Dominica 0 71991 0.0
Solomon Islands 0 652858 0.0
Somalia 0 15893219 0.0
Niger 0 24206636 0.0
Eritrea 0 3546427 0.0
Gambia 0 2416664 0.0
Nepal 14 29136808 0.0
Nauru 0 10834 0.0
Burkina Faso 0 20903278 0.0
Mozambique 0 31255435 0.0
Gabon 0 2225728 0.0
Syria 4 17500657 0.0
Djibouti 0 988002 0.0
Tajikistan 0 9537642 0.0
Korea, North 0 25778815 0.0
Antarctica 0 0 NaN
Diamond Princess 0 0 NaN
MS Zaandam 0 0 NaN
Summer Olympics 2020 0 0 NaN
Winter Olympics 2022 0 0 NaN

Tutorial: Detailed calculation for one country

In [5]:
from oscovida import fetch_cases, get_population
import datetime
In [6]:
period = 14 # Days we compute the incidence rate over
In [7]:
cases = fetch_cases() # Get a DataFrame where each row is the country, and columns cumulative case numbers
cases = cases.groupby(cases.index).sum() # Merge the rows for different regions as we want the numbers for an entire country

/tmp/ipykernel_991088/1570404713.py:2: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  cases = cases.groupby(cases.index).sum() # Merge the rows for different regions as we want the numbers for an entire country
In [8]:
cases_germany = cases.loc['Germany'][2:] # First 2 entries are lat/lon so we only take the subsequent ones
cases_germany.tail()
Out[8]:
3/2/23    38202571.0
3/3/23    38210850.0
3/4/23    38210850.0
3/5/23    38210851.0
3/6/23    38210851.0
Name: Germany, dtype: float64
In [9]:
yesterday = datetime.datetime.now() - datetime.timedelta(days=1)
x_days_ago = yesterday - datetime.timedelta(days=period)
In [10]:
period_mask = (
    (yesterday > pd.to_datetime(cases_germany.index)) &
    (pd.to_datetime(cases_germany.index) > x_days_ago)
) # Mask for dates between today and x days ago
In [11]:
cases_in_period_per_day_germany = cases_germany[period_mask].diff() # Apply the period mask and get the diff to get the daily new cases
cases_in_period_per_day_germany
Out[11]:
2/21/23        NaN
2/22/23    25038.0
2/23/23    21177.0
2/24/23    20974.0
2/25/23        0.0
2/26/23        0.0
2/27/23    32970.0
2/28/23    24875.0
3/1/23     21046.0
3/2/23     12617.0
3/3/23      8279.0
3/4/23         0.0
3/5/23         1.0
3/6/23         0.0
Name: Germany, dtype: float64
In [12]:
cases_in_period_per_day_germany.plot() # We can easily look at new cases per day
Out[12]:
<Axes: >
In [13]:
population = get_population().population
In [14]:
population_germany = population.loc['Germany'] # Get the population of Germany
population_germany
Out[14]:
83155031.0
In [15]:
incidence_rate_germany = cases_in_period_per_day_germany.sum() / population_germany * 100_000
incidence_rate_germany # By convention this is total cases over period / population * 100_000
Out[15]:
200.80204167081604
In [ ]: