From one French demographer (INED) in Le Monde [my translation], with a clustering of French departments into three classes [the figures on the above map are the lags after the first death in Haut-Rhin]:
One of the surprising aspects of the analyses and commentaries on the Covid-19 epidemic is the absence of statistics. Every evening, however, we are bombarded with figures, and many sites, from Public Health France (SpF) to Johns-Hopkins University (Maryland), abound in data.
But a number carries a meaning only in reference to other figures. This is where the real statistics start. However, apart from comparing the number of contagions and deaths by country and date, little has been learned from the data, which could provide useful information on the nature and progression of the epidemic (…)
We can see that the diversity of close contacts is one of the keys to the evolution of the epidemic. Instead of reasoning on abstract coefficients such as the famous average number R⁰ of contagions per person, we should be able to delve into the details of these contagions. We see here that traffic axes, institutions and housing probably occupy a strategic position towards an explanation.
This analysis is inevitably limited to the nature of the data and their possible faults. It would be useful to collect more detailed information on the nature of the contacts of each new case of contagion and to analyze it, or even to carry out random surveys with Covid-19 test, in a word, to make the statistics.
As lockdown rules concerning outdoor activities were rather restrictive (run alone, away from other people, at most one hour and at most 1km away from home), I used the network of streets around my house to design a 13km circuit that was never replicating more than intersecting previously visited roads. And I ran it every one of the 60 days of the lockdown.
