This chart offers two different perspectives on the global distribution of cumulative mortality since the start of this pandemic.

The first bar shows “post-pandemic” estimates of the mortality distribution that isolate the effect of demography (the combination of population size and age structure). The simulation reflects a counterfactual where we allow variation in demographic variables to impact on the distribution of cumulative COVID-19 mortality while keeping all other variables constant and identical across countries. 

That means everyone faces the same epidemiological odds in terms of getting infected and facing death once infected. In other words, infection prevalence rates (IPRs) are constant and identical across countries, age cohort and over time; age-adjusted infection fatality rates (IFRs) vary across age cohorts but are the same across countries and over time.

The second bar shows the mortality distribution through the lens of excess death estimates. These concern estimates of all-cause mortality over and beyond what one would expect in “normal times”. They do capture more broadly the effect of the pandemic as the estimates are not purely limited to COVID-19 mortality. More details on the methodology can be found in the note below.

The comparison fo the two bars provides an insight about the progression of the pandemic and the extent to which mortality outcomes compare to what one might expect due to variation in demography. The fact that the excess death share of the developing world is a lot higher than what is implied by the simulation implies that its assumptions of identical IPRs and IFRs across the world are clearly violated and have in combination driven up the mortality toll. 

Note that the counterfactual simulations are a thought experiment to interpret the role of demography. They should not be interpreted as forecasts as there are many other confounding factors that affect mortality. More details on the methodology underpinning this chart can be found in our paper.


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.

Excess mortality can be defined as the gap between the total number of deaths that occur for any reason and the amount that would be expected under normal circumstances.  Given the massive undercounting of the mortality toll both directly and indirectly attributed to COVID-19, excess mortality provides a useful way to get a glimpse of the true mortality toll.


Unfortunately, however, data on excess mortality are not universally available. Only 84 countries release some sort of data (national or subnational; regular or one-off) on excess deaths.  This is where the excess deaths model of The Economist comes in as it tries to fill the gaps on the basis of a well-calibrated model that takes advantage of various types of data that CAN be observed.


At its core, the model relies on a machine-learning algorithm (a gradient booster) that learns from official excess-mortality data and over 100 other statistical indicators. Where data on excess deaths is available, they are used.  Where such data are not available, the model fills the gaps in the form of single-point estimates.


Given the vast degree of uncertainty surrounding any point estimate, the model then uses a bootstrapping method to calculate standard errors. This amounts to using subsets of the full dataset (in terms of country-week pairs) and training different gradient-boosting models on each of these data subsets. The central estimate is derived then from the trained model on the full set of data, whereas the middle 95 of the predications generated by the 100 other models produce the 95% confidence intervals. 


Further details on the methodology can be found here and the full model can be viewed here.