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(2022-10-01 06:26)

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 0.014 3.652
Africa 0.06 0.004 0.852
Albania 0.0 0.0 15.663
Algeria 0.0 0.0 0.136
Angola 0.0 0.0 0.0
Argentina 0.1
Armenia 0.0 0.205
Asia 0.09 0.056 26.234
Australia 0.04 1.631 208.666
Austria 0.08 0.576 1174.229
Azerbaijan 0.01 0.208 12.841
Bahrain 0.01 0.0 253.64
Bangladesh 0.01 0.013 3.736
Belarus 0.0 0.0 0.0
Belgium 0.14
Benin 0.0 0.0 0.0
Bolivia 0.05 0.095 6.777
Bosnia and Herzegovina 0.01 0.786 23.104
Botswana 0.0 0.11
Brazil 0.07 0.191 32.059
Bulgaria 0.02 0.436 99.355
Burkina Faso 0.0 0.0
Burundi 0.0 0.0 1.446
Cambodia 0.0 0.0 0.431
Cameroon 0.0 0.0 0.0
Canada 0.11 0.76 63.163
Central African Republic 0.0 0.0 0.236
Chad 0.0 0.0 0.083
Chile 0.23 1.004 184.152
China 0.03 0.0 0.611
Colombia 0.0
Congo 0.0 0.0 0.0
Costa Rica 0.01
Cote d'Ivoire 0.0 0.016 0.442
Croatia 0.03 1.443 159.284
Cuba 0.23 0.0 0.977
Czechia 0.04 1.291 246.849
Democratic Republic of Congo 0.0 0.001 0.061
Denmark 0.01 1.171 201.319
Djibouti 0.01 0.0 0.0
Dominican Republic 0.02 0.0 0.0
Ecuador 0.08 0.056
Egypt 0.0 0.0 0.0
El Salvador 0.0 0.0 0.0
Equatorial Guinea 0.0 0.0 0.262
Eritrea 0.0 0.0 0.079
Estonia 0.04
Eswatini 0.0 0.0 1.318
Ethiopia 0.0 0.0 0.096
Europe 0.05 0.667 322.945
European Union 0.05 0.675 404.564
Finland 0.02
France 0.06 0.477 643.311
Gabon 0.0 0.0 0.0
Gambia 0.0 0.0 0.0
Georgia 0.0 0.0 0.0
Germany 0.05 1.038 743.636
Ghana 0.0 0.0
Greece 0.06
Guatemala 0.01 0.381 42.106
Guinea 0.0 0.0 0.0
Guinea-Bissau 0.0 0.0 0.0
Haiti 0.0 0.0 0.0
High income 0.09 0.837 286.068
Honduras 0.0 0.014 3.961
Hong Kong 0.26 1.182 552.609
Hungary 0.01
India 0.13 0.017 2.819
Indonesia 0.0 0.066 6.382
Iran 0.0 0.096 3.854
Iraq 0.0 0.01
Ireland 0.03
Israel 0.01 0.169 110.937
Italy 0.05 0.646 493.935
Jamaica 0.01 0.657 14.499
Japan 0.27
Jordan 0.0 0.077
Kazakhstan 0.0 0.015 4.353
Kenya 0.0 0.008 0.148
Kosovo 0.0 0.0 2.164
Kuwait 0.0 0.0 0.0
Kyrgyzstan 0.02 0.0 1.86
Laos 0.0 0.0 3.252
Latvia 0.01 0.991 631.221
Lebanon 0.01 0.255 31.777
Lesotho 0.0 0.0 0.0
Liberia 0.0 0.0 0.0
Libya 0.0 0.0 0.848
Lithuania 0.01 0.359 380.795
Low income 0.05 0.002 0.463
Lower middle income 0.11 0.035 5.391
Madagascar 0.0 0.0 0.079
Malawi 0.0 0.014 0.165
Malaysia 0.03 0.123 53.566
Mali 0.0 0.007 0.28
Mauritania 0.0 0.031 0.805
Mauritius 0.0 0.22
Mexico 0.0 0.107 10.212
Moldova 0.01
Mongolia 0.0 0.0
Morocco 0.0 0.0 0.366
Mozambique 0.0 0.0 0.414
Myanmar 0.0 0.016
Namibia 0.0 0.0 0.0
Nepal 0.04 0.01 1.123
Netherlands 0.0 0.106 125.751
New Zealand 0.11
Nicaragua 0.0 0.0 0.521
Niger 0.0 0.0 0.057
Nigeria 0.0 0.0 0.195
North America 0.07 0.838 88.124
North Korea 0.0 0.0 0.0
North Macedonia 0.01 0.543 30.02
Norway 0.1 13.855
Oceania 0.04 1.149 155.942
Oman 0.0 0.0
Pakistan 0.09 0.002 0.203
Palestine 0.0 0.0 0.0
Panama 0.01 0.23
Papua New Guinea 0.0 0.057 0.546
Paraguay 0.01 0.17 10.314
Peru 0.19 0.547 18.165
Philippines 0.0 0.272 19.927
Poland 0.05 0.515 95.128
Portugal 0.01 0.555 242.688
Puerto Rico 0.0
Qatar 0.02 0.0 287.974
Romania 0.01 0.392 59.18
Russia 0.03 0.697 283.722
Rwanda 0.0 0.0 0.053
Saudi Arabia 0.0 0.048 3.227
Senegal 0.0 0.0 0.626
Serbia 0.01 1.559 334.152
Sierra Leone 0.0 0.0 0.0
Singapore 0.04 0.236 542.368
Slovakia 0.01 0.367 141.923
Slovenia 0.01 0.876 1035.059
Somalia 0.0 0.0 0.059
South Africa 0.02 4.604
South America 0.1 0.215 28.769
South Korea 0.07 0.841 555.876
South Sudan 0.0 0.0 0.0
Spain 0.01 0.511 58.609
Sri Lanka 0.0 0.052 0.577
Sudan 0.0 0.0 0.0
Sweden 0.01
Switzerland 0.02 0.181
Syria 0.0 0.0 0.248
Taiwan 0.19 1.856 1728.129
Tajikistan 0.0 0.0 0.0
Tanzania 0.0 0.0 0.222
Thailand 0.07 0.13 8.849
Timor 0.0 0.0 0.973
Togo 0.0 0.017 2.512
Trinidad and Tobago 0.02
Tunisia 0.0 0.035
Turkey 0.04
Turkmenistan 0.0
Uganda 0.0 0.0 0.0
Ukraine 0.0 0.561 153.639
United Arab Emirates 0.0 0.046 40.53
United Kingdom 0.04
United States 0.08 1.301 138.38
Upper middle income 0.05 0.101 25.723
Uruguay 0.06
Uzbekistan 0.0 0.0 0.411
Venezuela 0.0 0.0 2.27
Vietnam 0.04 0.003 13.473
World 0.08 0.178 55.385
Yemen 0.0 0.0 0.004
Zambia 0.02 0.0 0.675
Zimbabwe 0.04 0.027 1.572
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.