This chart shows primary vaccination progress in the people space across UN subregions and measures the share of the total population for each income group that is vaccinated with at least 1 dose and fully vaccinated as per the protocol. Note that this shows primary vaccination progress and does not include boosters. 

The jagged pattern particularly for Eastern Asia reflects the fact that we show the data as made available, i.e. we do not interpolate or extrapolate observations that are missing for certain countries. The main reason for the jagged pattern of Eastern Asia is the fact that China did not regularly disclose how doses administered resulted in full vaccination progress. 

For more details on the intricacies of measuring global vaccination progress, check out this postOther details are found below in the background section.  


Pandem-ic uses the World Bank income classification as a major building block in the analysis of the impact of the pandemic.


The income classification groups countries in four buckets by per capita income levels: high-income countries (HICs), upper-middle-income countries (UMICs), lower-middle-income countries (LMICs) and low-income countries (LICs). We use the current FY2023 classification, which determines the thresholds of the buckets as follows:  


  • LICs are defined as those with a GNI per capita, calculated using the World Bank Atlas method, of $1,085 or less in 2021;
  • LMICs are those with a GNI per capita between $1,086 and $4,255; 
  • UMICS are those with a GNI per capita between $4,256 and $13,205;
  • HICs are those with a GNI per capita greater than $13,205.


A good part of this site also analyzes the pandemic by region (where we use the World Bank regional classification and the UN geo-scheme of subregions). In both cases (i.e. across income groups and regions), the universe of countries is based on the World Bank income classification. More on that in the next note.

The universe of countries on this website is determined as follows.


  • We start with the FY2023 World Bank income classification, which comprises of World Bank member countries as well as other economies with populations over 30,000 people (see World Development Indicators database). This means that we exclude the Holy See, Cook Islands and Niue since they are are not included in the WB income classification.  
  • We then narrow down this list to 196 countries/economies by retaining only the member states of the UN, one non-member state with observer status (the State of Palestine listed as West Bank and Gaza in the income classification) and two members/observers in UN Specialized Agencies  (Kosovo and Chinese Taipei listed as Taiwan, China in the income classification). This leads to the exclusion of a further 22 territories and dependencies (21 HIC and 1 UMIC) from the World Bank income classification. 


Note that the vaccination data is pulled from Our World in Data, which utilizes a slightly different universe of locations. In sticking with the above 196 countries and economies, we have made the following adjustments relative to the OWID universe.

  • Given their UN membership status, we extract the following UN members from US totals and list them separately:
    • Federated States of Micronesia;
    • Marshall Islands;
    • Republic of Palau.
  • Conversely, given that they are not identified as separate members of the UN or UN specialized agencies, we do not separately mention the following entities but instead include their data into the totals of the country they are a territory or dependency of:
    • Hong Kong SAR and Macao SAR are added to China totals;
    • Faroe Islands and Greenland are added to Denmark totals;
    • Aruba and Curaçao are added to the totals of The Netherlands;
    • Data for Anguilla, Bermuda, Cayman Islands, Falkland Islands, Gibraltar, Guernsey, Isle of Man, Jersey, Montserrat, Saint Helena and Turks and Caicos are add to the totals of the United Kingdom.


For each of the above adjustments to the vaccination data, we make adjustments to the demographic data that vaccine information is related to (including population size, age structure and priority group size).


Finally, note that no adjustments are required to the totals for France as its overseas territories and dependencies are already included.

Most of the vaccine data on this site converts the doses that have been administered into "double-dose equivalents" (DDEs). That is because the vaccine landscape is diverse with different treatment protocols, Most vaccines require 2 doses but there are some that require just 1 (e.g. CanSino, Johnson & Johnson and the stand-alone version of Soberana Plus) and others that require 3 (Abdala, the combination of Soberana 2 & Soberana Plus and ZF2001). These are considered primary vaccinations which when completed result in full vaccination status - they are not boosters. Given this diversity of protocols, we would like to bring them all into a common denominator so that doses administered can be compared properly. On this site, we choose double-dose vaccines as the common denominator.


Converting into DDEs means that we multiply the number of single doses administered by 2 for a single-dose protocol and 2/3 for a three-dose protocol. For example, if a country had administered 100 doses under a single-dose protocol, we would count it as 200.  Conversely, if a country had administered 300 doses under a three-dose protocol, the number of doses would be scaled down to 200. In both cases, we would achieve full vaccination at 200 DDE doses per 100 people. 


Why not just sum up unadjusted doses?


That would lead to two problems:

  • First, the resulting numbers cannot be compared across countries as we would be comparing apples and oranges. Suppose two countries A and B have administered both 50 doses per 100 people. But in country A the 50 doses are all single-dose protocol, whereas in country B they are all two-dose protocol. Clearly, the vaccination effort in country A would be more advanced than in country B. But comparison of unadjusted doses would not capture that. 
  • Second, the full-vaccination milestone would vary from country to country depending on the mix of vaccines in use. Not only would we therefore not be able to compare vaccination progress across countries, we would also lack a common measure of the distance that still needs to be travelled to achieve full vaccination.  If we're at 50 doses per 100 people, will we have completed population-level vaccination when we reach 100, 200, or somewhere in between? There is no way to tell since the threshold will depend on the mix of vaccines used. If we only use single-dose protocols, 100 would be the goal post. It would be 200 if we only used double-dose protocols. It would be somewhere in the middle if we use a mix.


The DDE adjustments tackles these 2 problems. Thanks to the adjustment, we can compare countries's progress regardless of the mix of vaccines they are using. We also have a common threshold of full vaccination at 200 doses per 100 people or 200%. Moreover, this threshold is invariant across countries and over time. It is a steady milestone against which progress can be evaluated.


If the DDE approach allows us to measure progress towards full vaccination, what is the difference then with alternative measures such as the share of the total population that is fully vaccinated? The answer is that the DDE approach is less demanding in terms of the data that is required. In order to calculate the full vaccination rate (the share of the total population that is fully vaccinated), we need to have timely information on whether each dose administered contributes to the start, the completion or both of a vaccination cycle (both would be the case of a single-dose protocol). The DDE approach requires information about single versus multiple dose vaccinations, but it is agnostic and therefore less demanding about whether a dose of a multiple-dose vaccine represents full or partial vaccination. That is a major advantage given that for several countries such data is not available on a timely basis (see methodological annex here for a full discussion). 


Note on Cuban vaccines and data: Cuba currently administers different protocols: Abdala (3-dose vaccine), Soberana 2 combined with Soberana Plus (considered a 3-dose vaccine for children) and Soberana Plus stand-alone (considered a 1-dose vaccine for those with prior COVID). Based on this distinction, we derive from the data published by the Cuban authorities that total doses under 1-dose protocol (Soberana Plus stand-alone) equal the difference between people fully vaccinated and people vaccinated with three shots. Total doses under 3-dose protocols are therefore total doses administered minus doses administered under the 1-dose protocol. 

China publishes on a daily basis the total number of doses administered, but this includes both primary vaccine doses and booster doses. Data on booster doses are not published on a daily basis. For this reason the data presented on the website incorporate the following adjustments to complete the primary and booster vaccination series for China. Note that these adjustments are made to the data for mainland China, which are afterwards aggregated with those of Macao SAR and Hong Kong SAR.


First, the earliest datapoint for boosters in mainland China is for November 5, 2021: 37 million.  Since we don't have a clear anchor point for the actual starting point of the series, we assume that November 5 is also the starting point of the underlying booster program - an assumption which abstracts from reality but may be relatively innocuous given the "small" size of the number relative to the total population.


Second, we interpolate the data on days between published data points. For example, China published data on boosters on November 5 and 12. For the days in between, we interpolated the data on boosters as follows. We first calculated the ratio of the change in total vaccinations to the change in total boosters. We then multiplied this ratio with the daily observed change in total vaccinations, which approximates the daily increments in boosters based on the share of boosters in total vaccinations over the entire period and the daily change in total vaccinations. We then calculated the cumulative sum of the daily increments in boosters to arrive at the evolving stock of boosters.


Third, we extrapolate the data for the most recent days where we have information on total vaccinations but no information yet on total boosters, so that there is no anchor point as in the interpolation. For this reason, we use the ratio of the change in total vaccinations to the change in total boosters of the most recent period for which we have two observations of both variables.  In other words, if 70% of  new vaccinations between November 5 and 12 were boosters, then we would apply that ratio to the the daily changes in vaccinations after November 12 assuming that after November 12 there are no data points on total boosters (just an example as China did irregularly update the booster information afterwards).

The distribution of vaccine doses across countries is described on the basis of data on the actual doses administered. The reasons why the numbers may be low can vary widely and include international vs domestic factors as well as supply versus demand factors.  At this moment in time, however, the most likely reason for low numbers is related to international supply bottlenecks. 


The charts that describe vaccine sufficiency for the priority group do not measure actual vaccination of the priority group. That is currently impossible to consistently assess at the global level given data gaps. Instead, it describes, on the basis of the size of the priority group and the number of doses administered, the extent to which the priority group could have been covered through the vaccination efforts up through the latest available data point.


The vaccine sufficiency measure would approximate actual vaccination if governments act as “benevolent social planners” interested in vaccinating their most vulnerable groups first. Even if that is the case, discrepancies may arise as it also requires common agreement about who the most vulnerable groups are.  The charts assume that the vulnerable group consists of medical professionals under 60 and the entire 60+ population. In practice, governments around the world define their priority group in many different ways. The definition on this site is a minimal one in the interest of cross-country comparability and data availability.


How do we calculate the size of the priority group?


  • The trackers include all people aged 60+ as well as medical workers (specifically physicians, nurses and midwives on the basis of the WHO’s Health Workforce Accounts) who are under 60 (to avoid double counting). 
  • We use the World Development Indicator data set of the World Bank on Health, Nutrition and Population Statistics to retrieve global information on physicians, nurses and midwives and filter out the latest observation. The WHO’s Health Workforce Accounts are then used to calculate the share that is younger than 60 (to avoid overlap with the 60+ population, which is separately included). 
  • A number of adjustments are made to deal with data gaps. In cases where information is missing on the 55-64 or 65+ buckets, we apply the share of that age cohort observed in peer groups. For this purpose, we calculate the median share by income classification and apply that to the missing values. 
  • Finally, since the Health Workforce Accounts do not provide more granular data of the 55-64 age cohort, we divide this cohort into 2 to get an estimate of the 60- group within this cohort. 


More refined estimates of the health workforce will undoubtedly be available at the country level. In the interest of building a global dataset we rely however on this method which has the advantage of imposing transparent assumptions on all countries in an equal way.

Several visualizations distinguish between the extensive and intensive margins of vaccination progress. The extensive margin captures the extent to which countries are participating in vaccination efforts.  In the polar bar charts on this website, this can be observed in the circular progress bar, which shows the share of countries, by income group, that report nonzero vaccination data.  The visualization also colors the country codes of countries in the outer circle according to whether positive vaccination data is being reported (blue means yes; grey means no).


The intensive margin measures vaccination progress within countries. In the polar bar visualizations, the outward-oriented bars capture the efforts of countries to raise the coverage ratio to the maximum of 200 (on the vertical axis).