Edit page in Livemark
(2022-12-06 06:09)

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 0.007 1.636
Africa 0.05 0.006 1.939
Albania 0.0 0.0 4.172
Algeria 0.0 0.0 0.108
Angola 0.0 0.008 1.04
Argentina 0.05 0.028
Armenia 0.0 0.103 4.675
Asia 0.05 0.078 49.272
Australia 0.04 0.791 575.065
Austria 0.11 0.687 533.884
Azerbaijan 0.0 0.097 3.682
Bahrain 0.01 0.0 76.657
Bangladesh 0.04 0.003 0.089
Belarus 0.0 0.0 0.0
Belgium 0.22 0.355 100.672
Benin 0.0 0.0 0.621
Bolivia 0.02 0.047
Bosnia and Herzegovina 0.01 0.177 4.418
Botswana 0.0 0.0 129.643
Brazil 0.03 0.522 163.674
Bulgaria 0.02 0.421 23.508
Burkina Faso 0.0 0.0 0.0
Burundi 0.0 0.0
Cambodia 0.0 0.0 0.716
Cameroon 0.0 0.0 0.0
Canada 0.16 53.325
Central African Republic 0.0 0.0 0.0
Chad 0.0 0.0 0.008
Chile 0.07 1.312 193.105
China 0.01 0.0 23.968
Colombia 0.0 0.0
Congo 0.0 0.0 0.0
Costa Rica 0.0 0.414
Cote d'Ivoire 0.0 0.0 0.02
Croatia 0.03 1.489 62.738
Cuba 0.11 0.0 0.866
Czechia 0.06 1.416 374.391
Democratic Republic of Congo 0.0 0.001 0.358
Denmark 0.01 0.753 175.54
Djibouti 0.0 0.0 0.0
Dominican Republic 0.01 0.0
Ecuador 0.02 0.0 0.0
Egypt 0.0 0.001 0.0
El Salvador 0.0 0.0 0.0
England 0.23
Equatorial Guinea 0.0 0.0 0.085
Eritrea 0.0 0.0 0.0
Estonia 0.05 0.97
Eswatini 0.0 0.0
Ethiopia 0.0 0.0 0.308
Europe 0.05 0.778 212.488
European Union 0.05 0.95 319.498
Finland 0.03
France 0.05 0.999 825.796
Gabon 0.0 0.0 0.06
Gambia 0.0 0.0 0.0
Georgia 0.0 0.038
Germany 0.07 1.249 316.886
Ghana 0.0 0.0 0.06
Greece 0.07
Guatemala 0.0 0.064 49.252
Guinea 0.0 0.0 0.0
Guinea-Bissau 0.0 0.0 0.0
Haiti 0.0 0.0 0.197
High income 0.1 0.919 338.688
Honduras 0.0 0.11
Hong Kong 0.18 2.06 1251.306
Hungary 0.01
India 0.06 0.002 0.173
Indonesia 0.0 0.151 15.021
Iran 0.0 0.019 0.468
Iraq 0.0 0.006 2.09
Ireland 0.02
Israel 0.01 0.559 169.95
Italy 0.05
Jamaica 0.01 0.0 0.0
Japan 0.3 1.36 862.547
Jordan 0.0 0.0 0.0
Kazakhstan 0.0 0.007
Kenya 0.0 0.0 1.105
Kosovo 0.0 0.0 0.481
Kuwait 0.0 0.0 2.51
Kyrgyzstan 0.02 0.0 0.28
Laos 0.0 0.0 4.914
Latvia 0.01 1.235 277.354
Lebanon 0.0 0.104 9.29
Lesotho 0.0 0.0 0.0
Liberia 0.0 0.0 0.216
Libya 0.0 0.0 0.336
Lithuania 0.02 0.312 139.945
Low income 0.03 0.002 0.387
Lower middle income 0.07 0.025 2.387
Madagascar 0.0 0.01
Malawi 0.0 0.0 0.021
Malaysia 0.01 0.248 55.348
Mali 0.0 0.0 0.013
Mauritania 0.0 0.0 0.03
Mauritius 0.0 0.22 1021.949
Mexico 0.0
Moldova 0.01 0.218
Mongolia 0.0 0.042
Morocco 0.0 0.004 3.627
Mozambique 0.0 0.009 0.147
Myanmar 0.0 0.0 0.338
Namibia 0.0 0.0 0.0
Nepal 0.01 0.0 0.126
Netherlands 0.0
New Zealand 0.06 951.924
Nicaragua 0.0 0.0 0.37
Niger 0.0 0.0 0.0
Nigeria 0.0 0.0 0.0
North America 0.1 0.56 104.966
North Korea 0.0 0.0 0.0
North Macedonia 0.01 1.569
Northern Ireland 0.19
Norway 0.08 29.311
Oceania 0.04 0.533 449.926
Oman 0.0 0.0 0.0
Pakistan 0.04 0.001 0.111
Palestine 0.0 0.0 0.0
Panama 0.0 0.454 438.301
Papua New Guinea 0.0 0.0 6.028
Paraguay 0.0 0.19
Peru 0.11
Philippines 0.0 0.176 9.301
Poland 0.04 0.122 11.122
Portugal 0.01
Puerto Rico 0.0
Qatar 0.0 0.0 172.215
Romania 0.0 0.094 13.836
Russia 0.02 0.381 42.569
Rwanda 0.0 0.0 0.84
Saudi Arabia 0.0 0.059 1.42
Scotland 0.01
Senegal 0.0 0.0 0.115
Serbia 0.01 0.79 83.055
Sierra Leone 0.0 0.0 0.017
Singapore 0.02 0.101 214.982
Slovakia 0.01 0.329 37.692
Slovenia 0.01 0.876 619.453
Somalia 0.0 0.0 0.0
South Africa 0.01 4.859
South America 0.04 0.361 138.085
South Korea 0.03 0.935 1040.871
South Sudan 0.0 0.0 0.052
Spain 0.01
Sri Lanka 0.0 0.02 0.412
Sudan 0.0 0.003 0.055
Sweden 0.01
Switzerland 0.08 0.229
Syria 0.0 0.0 0.071
Taiwan 0.12 1.339 604.447
Tajikistan 0.0 0.0 0.0
Tanzania 0.0 0.0
Thailand 0.02
Timor 0.0 0.0 1.917
Togo 0.0 0.0 0.065
Trinidad and Tobago 0.01 0.093
Tunisia 0.0 0.0 0.936
Turkey 0.02 0.0 0.0
Turkmenistan 0.0
Uganda 0.0 0.0 0.444
Ukraine 0.0 0.317 19.161
United Arab Emirates 0.0 0.0 11.878
United Kingdom 0.02
United States 0.11 0.871 164.896
Upper middle income 0.03 0.094 41.645
Uruguay 0.01 0.167 63.732
Uzbekistan 0.0 0.0 4.357
Venezuela 0.0 0.005 2.726
Vietnam 0.0 0.01 4.778
Wales 0.24
World 0.06 0.185 67.367
Yemen 0.0 0.0 0.0
Zambia 0.0 0.0 0.178
Zimbabwe 0.01
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.