Coronavirus: Learning How to Dance

Part 1: A Dancing Masterclass, or What We Can Learn from Countries Around the World

1. The State of the World

This graph shows a row per country. For each country, the redder the day, the closer it is to the maximum number of daily cases. Countries with the reddest all the way to the right are still seeing their worst days in terms of new official cases, whereas countries with more yellow / green days at the end have left the worst behind. Conditional formatting is per row, so each row has the entire gradient from green to red. I show absolute cases, not relative to population (ie, not “per capita”), because I’m not judging the country’s response, but rather whether it has an outbreak that is being controlled. That is independent from the country’s population. I chose countries instead of regions or cities because they are the political level that can have the biggest impact on the management of the crisis. I don’t show day-on-day percentage changes because they are skewed towards early growth. Note that this represents official cases: that means that countries that are having a recent surge in testing will see a recent surge in cases. It might not represent an outbreak, just the reporting of it. I still think this is the relevant representation, because true cases are unknown, official cases are widely communicated, and there is not enough granularity of testing per day per country to account for that effect. Some countries might look good here just because they’re not testing much lately.

2. The Dance Masterclass: A Journey into the Future

China’s Dance after the Hammer

Taiwan’s Eternal Dance

South Korea’s Little Hammer and Scalpel

Source: Reuters
We choose % tests that turn out positive because it’s the best way to assess how good testing is. We introduced it in our last article, Coronavirus: Out of Many, One. Total number of tests is meaningless if a country is big or there are lots of cases. Tests per capita makes no sense if there’s few cases. But a low % of positives tells you a country is testing a lot of people in comparison to the magnitude of their problem. Some people agree.

Singapore’s Critical Missteps

In fact it would be slightly higher than 4%, because app usage is likely to work in clusters. For example, if one uses it in a family, it’s more likely that the rest of the family uses it too. But even if we assume a 25% higher tracking thanks to clusters, we’re still at an overall 5% of contacts traced.
In this graph I’m assuming a 30% penetration, which is 50% higher than what Singapore has communicated. More notes inside the chart. Link to quick model.

2 MSc in Engineering. Stanford MBA. Ex-Consultant. Creator of applications with >20M users. Currently leading a billion-dollar business @ Course Hero

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