Other than the number of cases, a key metric is ICU load. There's a good visualisation here https://covid.visium.ch/ ... the peak ICU load at the end of March / early April was 62% (the source of this data is described here: https://github.com/schoolofdata-ch/swiss-hospital-data ) ... in other words hospitals weren't overloaded the way they were in Italy for example.
IMO some of the main reasons for that were;
- Switzerland has an effective / working healthcare system
- Personal health is taken pretty seriously; there's a general emphasis on fitness and good diet - Switzerland has one of the lowest obesity rates in Europe for example https://en.wikipedia.org/wiki/Obesity_in_Switzerland
- Measures like shutting schools and working from home were implemented pretty quickly at the beginning of March.
- As a whole people behaved sensibly. Advice from the government was taken. There was no panic but largely people adapted to the new situation very quickly
- Finally, in general, the social systems works well here. There aren't large numbers of homeless and most people furloughed or who lost their job will have received at least 80% of their normal salary, so you don't have large numbers of people living in unhealthy conditions that would help a virus spread
A couple of graphs to visualize it -
https://interaktiv.tagesanzeiger.ch/2020/wuhan-schweiz/
However keep in mind that there's just 8.5 mil people in Switzerland. That's around the same as New York City and 2/3 of the size of Moscow.
I'm an author of this study (and long time lurker), happy and surprised to see it posted here.
Some findings HN readers might find interesting, I'm referring to figures in the manuscript[1] and it's appendix.
- R0 has started to decrease before the government measures (Fig 2). It even reaches 1 simultaneously to the main "lockdown" measure.
- Mobility (from google mobility reports) also started to decrease before the measures (Fig. 3), but R0 also started to decrease before even mobility (Fig. 3).
- People awareness seemed to rise before government measures and mobility decrease, consistent with google trends (Appendix Fig. 14). Might explain why R0 starts to decrease so early.
- The appendix contains an interesting data analysis of hospitalization processes, with data on env. 1'000 patients. The length of stay in ICU are incredibly long. To answer the question: How long ?, we performed a survival analysis. It's necessary as estimates (such as the mean) are biased towards shorter stays.
- A serology study is conducted at the moment in Geneva. It seems that our estimates of seroprevalence (Fig. 5: only 3% country wide by April 24) are consistent with the study (Appendix Fig. 7). We were quite proud of that, as these results were unknown to us at the time.
- Method wise: There is different way of estimating R0 given hospitalization, death, cases:
(i) Most estimates are done with methods "deconvoluting" the data using the distributions (Cori et al, Wallinga and Teunis, implemented in the EpiEstim R package). It can works very well, but it's tricky to have unbiased estimates (see [2]).
(ii) Other methods involve choosing a breakpoint and calibrating to R0: before and after breakpoint. Variations of this method involve calibrating the breakpoint date, choosing a shape (e.g spline) and calibrating all the parameters. These method rely on some assumptions on the decrease. A incorrect assumption leads to biased estimates. E.g it would seem reasonable to assume R0 to decrease on the day of the government measures. But from what we estimated it wasn't the case in Switzerland. So your estimate of R0 post-measure would be lower than what it is really (to catch-up).
The method used here uses the full timeserie and no assumptions. First we built an hidden-markov model of COVID-19 transmission and hospitalization (see diagram Appendix Fig. 4). We performed frequentist inference (using [3]) of relevant parameters. Last, we "filter" R0 as a state of our model: R0 is a random walk, with calibrated variance and using particle filter we keep only R0 timeseries that support the underlying data. Therefore we impose no assumption on R0.
[1] https://smw.ch/article/doi/smw.2020.20295 and Appendix
I think wearing masks and gloves reduced cases, not lock down per se
I don't understand why people keep wasting time with this.
Governments are both not doing widespread testing to see how many people have the virus, and taking draconian measures to contain its spread.
From what we know, 90% of the population already could already have it. Every time I read of somebody testing a big group of people a huge percentage has it, but the virus is so mild that they didn't even know—since it caused no symptoms.
Looks like there are many other diseases and problems that cause dozen of times more deaths, so either there's a reason why this is getting special treatment, or it's just a complete loss of common sense here, in my opinion.
Living in Switzerland: People changed behavior drastically.
Hand shaking completely disappeared, oncoming walking people making a bend to guarantee the distance of 6 feet, hand sanitizers for everybody everywhere, businesses and associations have to publish government-approved conceptions of protection against infections before re-opening and many other little changes.
However relatively little face masks, no curfew and as of today life is going on not quite as before. For example, next week swimming courses for children resume, but parents are asked to send the children already clothed for swimming and are warned if they don't follow the guidelines they and their children will be excluded. In the local hospital all visitors are given face masks to be worn without exception by a Securitas (a private police) employee.