Top 50 Countries - Covid-19 disease dynamics

Following on from my analysis of US states yesterday, today we look at the situation for major countries around the world using the same technique. (See chart below). Ideally you want to be low down on the y-axis (representing a low infection rate), be a small bubble (low absolute case size) and with decreasing trend (heading to the right). By contrast countries with large, growing numbers of new cases with high infection rates such as Brazil, Russia, Peru are a major cause for concern and Chile, Belarus and Saudi Arabia may well see significantly more cases before they turn the corner into decline. 

However, unlike the US analysis where we can assume we are most likely comparing apples with apples, we must treat this analysis with a degree of caution - different countries are measuring cases in different ways, so the case loads could represent differing levels of severity at different points in the disease lifecycle. Even worse would be the scenario where the basis of measurement is also changing which would give really misleading results.

A good example of this is the UK - the number of Pillar 1 cases (high severity, likely to be hospital admissions) have been falling steadily and yet the UK figures below shows a plateau in the chart above. This is caused by the recent inclusion of Pillar 2 results into the daily reporting, Pillar 2 tests are pro-active results from key workers, hence the results are misleading.

So, by all means enjoy the chart, but do treat with caution since we may not be comparing like for like. I suspect some form of segmentation looking at ratio of deaths of new cases would help identify how cases are being diagnosed - those with a higher death rate are probably nearer the hospital admission point, those with lower numbers will be more pro-active tests in the wider community.