| 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 [2] https://github.com/keyajoshi/Pan_response [3] https://en.wikipedia.org/wiki/Iterated_filtering |
What about individual regions? I ask because most of the dynamics I've seen originate around hotspots, and thus it may be interesting to see how individual regions were affected (because it may drive different responses).
E.g., the Spain seroprevalence study said 5% country wide. But the estimates per regions can be as up to 3 times that (Madrid itself is ~11%).