Demography acts as a beacon towards which pandemic mortality outcomes gravitate. Accordingly, demography (the combination of population size and age structure) represents a structural trend that has supported (and will continue to support) a shift of the mortality burden of the pandemic towards the developing world.
The simulations in the chart (red bar on the right) isolate the effect of demography on mortality outcomes across World Bank regions as summarized in their share in the global death toll. The simulation reflects a counterfactual where all other variables are held constant. That means everyone gets infected at the same rate and faces the same age-adjusted infection fatality risk (capturing the age-discriminating nature of COVID-19).
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. At the same time, we do see that the mortality share has evolved rather dramatically since the start of the pandemic into the direction of what these demographic beacons suggest (the dark and light blue bars on the left and in the middle).
The simulations suggest that given their older age structure we should expect that regions with a large share of seniors in their total population attract a higher share of mortality than their share in global population.
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 FY2022 classification, which determines the thresholds of the buckets as follows:
See here for a dynamic visualization of how the income classification of countries has changed over time through the current FY2022 classification
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.
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.
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.