I’ve stopped doing my own graphs to check trends for coronavirus. The 91-divoc.com website has added daily cases and daily deaths time series as well as moving 7-day average time trends to its visualizations, and has plots for countries for absolute numbers and rates per million population, and also for US States.  Looking at these yesterday, it is clear that apart from New York the US epidemic is not declining and for at least a dozen States, including Texas, North Carolina and California, the daily cases are trending upwards.  Likely as a result of the relaxation of social distancing with media reports of clusters of cases associated with various gatherings.

The Imperial College coronavirus modelling team published a new study on Monday which estimated that the lockdowns and other restrictions on daily life prevented around 3.1 million deaths in 11 European countries (Flaxman et al, https://www.nature.com/articles/s41586-020-2405-7). The study worked back from observed deaths (with all their limitations) up to 4 May to estimate transmission that occurred several weeks prior and hence the reproduction number R. They found that the various lockdowns and other restrictions on public life had reduced the reproduction number by an average 82%, bringing it below 1. They then ran the model to predict the number of deaths if no restrictions had been implemented to estimate the restrictions prevented 3.1 million deaths.

Overall, they estimated that between 12 and 15 million people had been infected in the period, or between 3.2 and 4 percent of the population of the 11 countries. The table below shows the estimated infection rate for each of the 11 countries.

I’d recently seen a plot of US states coronavirus rates against population density showing a strong correlation. I did a similar plot below for the 11 European countries, with linear trend line. Belgium and Sweden are outliers on the high side for infection rates, and Germany, Denmark, Austria and Switzerland on the low side. Of course, not too much should be read into this, as this analysis should really be done at subnational level, rural/urban at minimum or preferably, city, town, rural, which would require more detailed geographic breakdown within countries of the deaths.

Of course, the situation in Switzerland has been of particular interest to me as I live in Geneva, one of the hardest hit cantons. Regularly updated time series data are available for Swiss cantons at Covid-19 Information for Switzerland.

I plotted the total reported coronavirus deaths as at 8 June 2020 as rates per million population by canton in the graph below. I thought about doing a density plot, but as some cantons have populations confined to valleys between Alpine peaks, the density issue may not be straightforward. Instead, I highlighted the language differences. The yellow bar denotes the canton in which Italian is the dominant language, Ticino, and it has the highest death rates because it borders northern Italy where the epidemic hit early and hard, and the border was not closed until well into the epidemic. The blue denotes cantons where French is the dominant language, and red where German is the dominant language.  Clearly Romandy (the Swiss French population in the west of Switzerland) was hit much harder than Alemannic Switzerland (the part that speaks the Alemannic dialect of German known as Schweitzer Deutsche.

The Swiss Government has issued a statement explaining that the Swiss lock-down rules were applied equally in all cantons. The main reason for the high rates in French and Italian speaking cantons were because of the high rates in the neighbouring countries and high proportions of cross-border workers. Geneva has a very high population of frontaliers (people who live in neighbouring France and commute to Geneva to work or go to school) as well as a high level of international visitors associated with tourism, skiing, and business and NGO headquarters. Additionally, when the epidemic hit Europe there were large numbers of skiers heading through Geneva to the Alps, from countries including France and Britain. In contrast, infection rates in Germany and Austria, neighbouring the German-speaking parts of Switzerland, have been much lower than in France and Italy.

Social distancing has been progressively relaxing since April 27^th and most children are back at school, and businesses open but with relatively unenforced limits on interpersonal distances. If rates start to go back up, as is happening in many US States now, I have no doubt the Swiss will efficiently and without polarizing debate reintroduce relevant restrictions.