This chart shows the evolution over time of the ‘relative severity ratio’ at the level of the World Bank region of Sub-Saharan Africa (SSA) and makes a comparison of COVID-19 mortality with the top causes of death in 2019 at the level of this aggregation.
The relative severity ratio relates COVID-19 mortality to the level and profile of pre-pandemic mortality. The ratio itself is defined simply as the ratio between (1) the total weekly number of deaths with COVID-19 as the underlying cause and (2) the total number of all-cause deaths in 2019 during a similar length of time. In light of data constraints and to foster global comprehensiveness, we take total all-cause mortality for 2019 and scale it down to the period of a week for the comparison under (2).
The relative severity ratio is then used to make two types of comparisons.
Note that the expression of mortality in relative terms is a useful way to communicate the severity of the pandemic. Countries will have adapted to their specific patterns of mortality. Deviations from this pattern may create pressure points, such as on the health system. Comparisons with previous patterns give a country-specific and intuitive flavor of the severity of the COVID-19 pandemic. A statement such as “COVID-19 is claiming more lives than the top cause of death did in 2019” may convey a better feel for the severity of the pandemic than a reference to a mortality rate (total deaths per 100k people).
Finally, note that comparisons with top causes of death are with reference to the 133 disease families of the 2019 Global Burden of Disease study (at the third level of ICD-10). We generally select the top nth cause of death, which most closely approximates the peak COVID-19 severity ratio from below. More details on the concept of relative severity are in the paper of Schellekens and Sourrouille (2020) that developed the concept, which can be found here.
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.