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(2022-08-16 06:08)

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 0.021 5.711
Africa 0.07 0.011 1.347
Albania 0.0 0.4 214.683
Algeria 0.0 0.0 2.868
Angola 0.0 0.0 0.0
Argentina 0.16 0.398 98.436
Armenia 0.0 0.102
Asia 0.11 0.118 87.219
Australia 0.07 3.037 850.973
Austria 0.07
Azerbaijan 0.02 0.18 43.828
Bahrain 0.01 0.195 402.72
Bangladesh 0.05 0.008 1.295
Belarus 0.0 0.0 0.0
Belgium 0.04
Benin 0.0 0.0 0.0
Bolivia 0.07 0.402 198.696
Bosnia and Herzegovina 0.01 1.747
Botswana 0.0 0.11
Brazil 0.12
Bulgaria 0.03 1.1 183.689
Burkina Faso 0.0 0.0 0.0
Burundi 0.0 0.0
Cambodia 0.03 0.0 1.851
Cameroon 0.0 0.011
Canada 0.09
Central African Republic 0.0 0.0 0.576
Chad 0.0 0.0 0.058
Chile 0.35 1.385 536.4
China 0.07 0.0 1.167
Colombia 0.01
Congo 0.0 0.0 1.518
Costa Rica 0.01 0.721
Cote d'Ivoire 0.0 0.021 2.282
Croatia 0.02 3.343 261.92
Cuba 0.24 0.0 7.691
Czechia 0.03 1.101 171.158
Democratic Republic of Congo 0.0 0.001
Denmark 0.02
Djibouti 0.01 0.0 0.0
Dominican Republic 0.02 0.0 28.41
Ecuador 0.12 0.0 0.0
Egypt 0.0 0.008 0.0
El Salvador 0.0 0.09 0.0
Equatorial Guinea 0.0 0.0 10.139
Eritrea 0.0 0.0 0.71
Estonia 0.03 0.645
Eswatini 0.0 0.0 2.636
Ethiopia 0.0 0.002 0.311
Europe 0.05 1.203 265.257
European Union 0.05 1.412 340.618
Finland 0.03
France 0.08
Gabon 0.0 0.0 4.943
Gambia 0.0 0.0 4.816
Georgia 0.01 0.304
Germany 0.06
Ghana 0.0 0.0 0.191
Greece 0.07
Guatemala 0.01 0.86 124.137
Guinea 0.0 0.021 0.655
Guinea-Bissau 0.0 0.0 0.832
Haiti 0.0 0.0
High income 0.1 1.394 538.086
Honduras 0.0 0.125
Hong Kong 0.3 0.477 605.771
Hungary 0.01
India 0.11 0.039 12.45
Indonesia 0.03 0.068 19.724
Iran 0.01 0.785 56.463
Iraq 0.0 0.003
Ireland 0.04
Israel 0.02 0.999 206.19
Italy 0.07 2.228 448.787
Jamaica 0.02 0.707 43.397
Japan 0.28 1.724 1571.83
Jordan 0.0 0.09
Kazakhstan 0.0 0.037 76.152
Kenya 0.0 0.003 0.261
Kosovo 0.01 0.641 431.43
Kuwait 0.0 0.0 18.117
Kyrgyzstan 0.02 0.0
Laos 0.01 0.0 9.889
Latvia 0.02 0.61
Lebanon 0.03 0.715 258.529
Lesotho 0.0 0.125
Liberia 0.0 0.0 0.825
Libya 0.0 0.021 12.853
Lithuania 0.02 0.615 440.313
Low income 0.06 0.006 0.846
Lower middle income 0.12 0.062 13.005
Madagascar 0.0 0.005 0.208
Malawi 0.0 0.022 0.603
Malaysia 0.06 0.251 114.864
Mali 0.0 0.0 0.059
Mauritania 0.0 0.0 1.579
Mauritius 0.0 0.44 0.0
Mexico 0.02
Moldova 0.01
Mongolia 0.0 0.085
Morocco 0.0 0.046 3.202
Mozambique 0.0 0.013 0.588
Myanmar 0.0 0.003 0.507
Namibia 0.0 0.056 0.0
Nepal 0.1 0.081 13.817
Netherlands 0.0 0.253
New Zealand 0.12 859.388
Nicaragua 0.0 0.0 1.355
Niger 0.0 0.0 0.074
Nigeria 0.0 0.0
North America 0.09 0.992 223.811
North Korea 0.0 0.0 0.0
North Macedonia 0.01 1.766
Norway 0.05 29.93
Oceania 0.07 2.148 607.84
Oman 0.0 0.0
Pakistan 0.12 0.009
Palestine 0.0 0.083
Panama 0.01 0.295
Papua New Guinea 0.0 0.0 0.287
Paraguay 0.01
Peru 0.25 1.352 257.262
Philippines 0.0 0.287 35.107
Poland 0.04 93.185
Portugal 0.02 0.805
Puerto Rico 0.0
Qatar 0.05 0.0 268.897
Romania 0.01 1.249 305.839
Russia 0.02 0.371 164.386
Rwanda 0.0 0.0 0.615
Saudi Arabia 0.0 0.032 3.672
Senegal 0.0 0.0 1.16
Serbia 0.01 1.933 880.879
Sierra Leone 0.0 0.017 0.068
Singapore 0.07 0.524 903.571
Slovakia 0.01 0.0 0.0
Slovenia 0.01 1.753 666.088
Somalia 0.0 0.0 0.0
South Africa 0.04 0.0 0.0
South America 0.15 0.733 106.952
South Korea 0.1 1.05 2407.91
South Sudan 0.0 0.0 0.572
Spain 0.01
Sri Lanka 0.01 0.23 8.188
Sudan 0.0 0.006 0.169
Sweden 0.01
Switzerland 0.03 0.345
Syria 0.0 0.047 1.581
Taiwan 0.25 1.233 897.974
Tajikistan 0.0 0.0 0.0
Tanzania 0.0 0.0
Thailand 0.11 0.455 29.203
Timor 0.0 0.108 4.11
Togo 0.0 0.0 1.537
Trinidad and Tobago 0.02 1.03 195.887
Tunisia 0.01
Turkey 0.05 0.0 0.0
Turkmenistan 0.0
Uganda 0.0 0.0 0.0
Ukraine 0.0 0.075 20.287
United Arab Emirates 0.0 0.031 92.089
United Kingdom 0.09
United States 0.08 1.381 337.273
Upper middle income 0.08 0.213 41.731
Uruguay 0.13 0.334
Uzbekistan 0.0 0.0 0.876
Venezuela 0.0 0.051 9.053
Vietnam 0.09 0.006 26.772
World 0.1 0.313 103.261
Yemen 0.0 0.0 0.035
Zambia 0.04 0.0
Zimbabwe 0.09 0.036 0.822
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.