This chart shows the evolution over time of daily COVID-19 cases per capita across UN subregions. To remove intra-week volatility in the reported data, the indicator is transformed into a 7-day trailing average (the average value of the latest observation and the preceding 6 days).
The case rate expresses COVID-19 cases relative to the size of the population. By relating COVID-19 cases to population size, we get a measure of the prevalence of confirmed infections within the community tested. In contrast to the absolute case toll, the case rate provides an indication of the performance of country or group of countries in terms of protecting its population against infection.
It should be noted that the different regional groups shown here are of very different population size dimensions. In light of these differences in population size, the absolute case toll will be quite different given the differences in rates.
Finally, note that limitations to testing will mean that the number of confirmed cases is lower than the true number of infections.
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.