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(2023-02-02 06:10)

Rolling Immunity

Rolling immunity (in terms of this page) is a collective immunity metric based on factual uses cases (counted using deaths) and vaccination shots timeline and the fact that individual immunity decreases over time. This page is about finding relations with pandemic outbreaks and the rolling immunity concept.

Examples

To showcase the idea of rolling immunity, we use 5 countries having different vaccination timeframes. The Israel use case might be showing that having too fast of a vaccination campaign leads to outbreaks due to low levels of rolling immunity. This USA case might be showing that the vaccination level wasn't enough.

Israel

USA

UK

Germany

Brasil

Locations

Explore charts for other countries and share what you think. Take into account that this methodology is very approximate as the numbers depend on the healthcare and vaccines efficacy in the exact country. This table uses 7 day moving averages for deaths and cases:

Location Immunity Deaths/1M Cases/1M
Afghanistan 0.0 0.035 0.792
Africa 0.04 0.004 0.648
Albania 0.0 0.0 2.664
Algeria 0.0 0.0 0.08
Angola 0.0 0.0 0.0
Argentina 0.08
Armenia 0.0 0.0 0.0
Asia 0.04 0.279 20.884
Australia 0.01
Austria 0.07 0.799 362.623
Azerbaijan 0.0 0.138 2.8
Bahrain 0.0 0.291 64.431
Bangladesh 0.07 0.001 0.075
Belarus 0.0 0.0 0.0
Belgium 0.12
Benin 0.0 0.0 0.0
Bolivia 0.01 0.07 30.759
Bosnia and Herzegovina 0.0 0.309 3.313
Botswana 0.0 0.109
Brazil 0.03 0.396 45.957
Bulgaria 0.01 0.253 7.394
Burkina Faso 0.0 0.0 0.0
Burundi 0.0 0.0 11.781
Cambodia 0.0 0.0 0.119
Cameroon 0.0 0.0 0.0
Canada 0.11 0.914 34.401
Central African Republic 0.0 0.0 0.179
Chad 0.0 0.0 0.0
Chile 0.03 0.904 87.782
China 0.03 0.0
Colombia 0.0 0.438 18.669
Congo 0.0 0.0 0.0
Costa Rica 0.0
Cote d'Ivoire 0.0 0.0 0.117
Croatia 0.03 1.418 14.214
Cuba 0.07 0.0 0.79
Czechia 0.04 0.259 31.229
Democratic Republic of Congo 0.0 0.0 0.059
Denmark 0.02 1.02 27.176
Djibouti 0.0 0.0 0.0
Dominican Republic 0.01 0.0
Ecuador 0.0 0.0 0.0
Egypt 0.0 0.0 0.0
El Salvador 0.0 0.0 0.0
England 0.15
Equatorial Guinea 0.0 0.0 0.341
Eritrea 0.0 0.0 0.0
Estonia 0.04 0.862
Eswatini 0.0 0.0 2.259
Ethiopia 0.0 0.0 0.191
Europe 0.04 0.562 57.128
European Union 0.05 0.771 69.015
Finland 0.04
France 0.05 0.632 64.528
Gabon 0.0 0.0 0.06
Gambia 0.0 0.0 0.0
Georgia 0.0 0.0 0.0
Germany 0.06 1.244 140.15
Ghana 0.0 0.0 0.0
Greece 0.05 0.0 0.0
Guatemala 0.0 0.208 26.948
Guinea 0.0 0.0 0.0
Guinea-Bissau 0.0 0.0 0.0
Haiti 0.0 0.0 0.839
High income 0.09 1.046 141.366
Honduras 0.0 0.027
Hong Kong 0.17
Hungary 0.01 0.272 8.886
India 0.02 0.0 0.069
Indonesia 0.0 0.011 0.836
Iran 0.0 0.011 1.192
Iraq 0.0 0.0 0.0
Ireland 0.01 1.507 16.04
Israel 0.03 51.011
Italy 0.04
Jamaica 0.0 0.202 11.217
Japan 0.32 2.423 388.847
Jordan 0.0 0.0 0.0
Kazakhstan 0.0 0.0
Kenya 0.0 0.0 0.032
Kosovo 0.0 0.16 4.409
Kuwait 0.0 0.0 0.0
Kyrgyzstan 0.02 0.0 0.022
Laos 0.0 0.0 0.493
Latvia 0.01 2.084 13.046
Lebanon 0.0 0.286 32.32
Lesotho 0.0 0.0 0.0
Liberia 0.0 0.0 0.242
Libya 0.0 0.0 0.063
Lithuania 0.01 0.727 92.569
Low income 0.03 0.005 0.559
Lower middle income 0.04 0.01 0.701
Madagascar 0.0 0.0 0.048
Malawi 0.0 0.0 0.378
Malaysia 0.01 0.038 7.211
Mali 0.0 0.0 0.044
Mauritania 0.0 0.0 0.03
Mauritius 0.0 0.0
Mexico 0.0 0.413 32.538
Moldova 0.0 0.131
Mongolia 0.0 0.0 0.42
Morocco 0.0 0.0 0.21
Mozambique 0.0 0.0 0.0
Myanmar 0.0 0.0 0.05
Namibia 0.0 0.278
Nepal 0.0 0.0 0.098
Netherlands 0.0 0.016 15.82
New Zealand 0.04 0.248 291.291
Nicaragua 0.0 0.0 1.049
Niger 0.0 0.0 0.0
Nigeria 0.0 0.0 0.0
North America 0.07 0.892 74.702
North Korea 0.0 0.0 0.0
North Macedonia 0.0 0.205 7.165
Northern Ireland 0.13
Norway 0.04 8.806
Oceania 0.02 1.691 101.687
Oman 0.0 0.0
Pakistan 0.01 0.0 0.075
Palestine 0.0 0.0 0.0
Panama 0.0 0.065
Papua New Guinea 0.0 0.014 1.225
Paraguay 0.0
Peru 0.06 15.687
Philippines 0.0 0.089 1.491
Poland 0.02 0.122 12.606
Portugal 0.01
Puerto Rico 0.0
Qatar 0.0 0.0 19.347
Romania 0.0
Russia 0.01 0.277 47.718
Rwanda 0.0 0.0 0.0
Saudi Arabia 0.0 0.047 1.079
Scotland 0.0
Senegal 0.0 0.025 0.016
Serbia 0.01 0.79 149.062
Sierra Leone 0.0 0.0 0.0
Singapore 0.01 0.051 75.369
Slovakia 0.01 0.709 16.935
Slovenia 0.01 0.472 103.646
Somalia 0.0 0.0 0.0
South Africa 0.0 0.0 3.334
South America 0.04 0.397 33.062
South Korea 0.01 0.623 381.229
South Sudan 0.0 0.0 0.0
Spain 0.01
Sri Lanka 0.0 0.013 0.046
Sudan 0.0 0.021 0.076
Sweden 0.02
Switzerland 0.06 0.0
Syria 0.0 0.0 0.019
Taiwan 0.07 1.124 1103.802
Tajikistan 0.0 0.0 0.0
Tanzania 0.0 0.0 0.14
Thailand 0.0
Timor 0.0 0.0 0.32
Togo 0.0 0.0 0.016
Trinidad and Tobago 0.01 0.653
Tunisia 0.0 0.069 0.902
Turkey 0.0 0.0 0.0
Turkmenistan 0.0
Uganda 0.0 0.0 0.218
Ukraine 0.0 0.327 23.065
United Arab Emirates 0.0 0.0 8.247
United Kingdom 0.01 0.0
United States 0.09 1.299 112.505
Upper middle income 0.03 0.467 10.426
Uruguay 0.04 0.334
Uzbekistan 0.0 0.0 0.507
Venezuela 0.0 0.025 1.191
Vietnam 0.0 0.0 0.233
Wales 0.17
World 0.05 0.317 25.823
Yemen 0.0 0.0 0.0
Zambia 0.0 0.05 8.571
Zimbabwe 0.0 0.053 6.285
Location Immunity Deaths/1M Cases/1M

Methodology

We use oversimplified rolling immunity formula for a date. We use deaths instead of use cases to overcome the problem of inaccurate use cases reporting. The exact ratio is configurable; we use 1 death for 100 use cases based on known death rate for now around 50 use cases per a death and estimating unreported use cases as 50 use cases per 1 death:

rolling_immunity_for_a_date = (estimated_new_cases + new_vaccinations) / population
estimated_new_cases = new_deaths * 100

We count one case to be equal to one vaccination shot. For the model we use the decreasing ration (180 - days)/180 meaning that you will lose all the immunity you had acquired in 180 days. Again, this parameter is configurable and might be changed in other models.

rolling_immunity_for_a_date = 100%
rolling_immunity_for_a_date_in_180_days = 0%

Please take a look at the full algorithm.