The Swiss government issued an ordinance stating that restaurants can be kept open when R < 1, and must close when R > 1 at the cantonal level. Worth having some reflections about how R values should influence policy decisions. A thread. 1/n admin.ch/opc/de/classif…
In Switzerland, these policy decisions will be made based on point estimates of R that are provided with two (!) decimal places. For example, R is currently given as 0.98 in the canton of Valais. 2/n covid19.admin.ch/en/repro/val?t…
To be honest, the ordinance sounds a bit like a technocratic idiocy to me. But let’s have a look at how @GOVUK uses R as a guide during the pandemic response. 3/n gov.uk/guidance/the-r…
"Estimates for R and growth rates are shown as a range, and the true values are likely to lie within this range." The current range in the UK is reported as 1.1-1.2. Note the careful attention to uncertainty in these estimates. 4/n
"The UK estimates of R and growth rate are averages over very different epidemiological situations and should be regarded as a guide to the general trend rather than a description of the epidemic state.” 5/n
“Given the increasingly localised approach to managing the epidemic, particularly between nations, UK level estimates are less meaningful than previously." 6/n
"When the numbers of cases or deaths are at low levels and/or there is a high degree of variability in transmission across a region, then care should be taken when interpreting estimates of R and the growth rate." 7/n
"Different modelling groups use different data sources to estimate these values using mathematical models that simulate the spread of infections. Some may even use all these sources of information to adjust their models to better reflect the real-world situation.” 8/n
“There is uncertainty in all these data sources so estimates can vary between different models, so we do not rely on just one model; evidence from several models is considered, discussed, combined, and the growth rate and R are then presented as ranges.” 9/n
“The most likely true values are somewhere within the ranges." 10/n
Bottom line: Considering the amount of uncertainty that comes with estimates of R, don’t base your policy decisions solely on values of R. 11/n
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Letzten Freitag hat der Bundesrat eine Art "Ampelsystem" eingeführt. Macht dieses und die in den Medien diskutierten Anpassungen aus epidemiologischer Sicht Sinn? In meinen Augen lautet die Antwort ganz klar nein. Ein Thread. 1/n edi.admin.ch/edi/de/home/do…
Zuerst einmal: Wozu benötigt man ein Ampelsystem? Ein solches System erlaubt bei klar definierten Schwellenwerten verschiedene Massnahmenpakete schnell einzuleiten. 2/n
Dies erlaubt es der Bevölkerung auf anstehende Massnahmen vorbereitet zu sein, bietet deutlich mehr Planungssicherheit und kann im besten Fall einen raschen Anstieg der Epidemie verhindern oder zumindest abschwächen. 3/n
What is the overall infection fatality ratio (IFR) of #SARSCoV2 in Germany? The widely discussed Gangelt study now got published in @NatureComms. There are a number of issues with its estimate of the IFR. A thread. (1/n) nature.com/articles/s4146…
What do we already know about the IFR in Germany? At @ISPMBern, we published a modeling study that estimates the IFR at 0.8% (95% CI: 0.5%-1.1%) for Bavaria and 0.7% (95% CI: 0.5%-1.3%) for Baden-Württemberg based on 1,049 and 802 deaths. (2/n) journals.plos.org/plosmedicine/a…
The IFR can also be estimated from seroprevalence studies. A recently published report from Munich (Bavaria) found an IFR of 0.8% (0.6%-1.1%), confirming our earlier estimates. (3/n) klinikum.uni-muenchen.de/Abteilung-fuer…
In the current situation, it is critically important to follow the epidemic trends of #SARSCoV2 across age groups and cantons. Changes in the number of daily confirmed cases, hospitalizations, ICU occupancy and deaths can all provide meaningful insights. (2/n)
One can fit a negative binomial generalized linear model (glm.nb in R) to the data with reported numbers as a response variable and date and weekend as predictors. This allows to estimate the exponential increase or decrease of the different indicators of the epidemic. (3/n)
The @SwissScience_TF proposes to halve confirmed #SARSCoV2 cases every two weeks to reach less than 500 cases in January. What does this mean regarding the reproduction number? A thread about doubling times and half-lifes of #SARSCoV. (1/n)
The figure (in German) shows the relationship between the reproduction number and the time in which the number of new #SARSCoV infections double (red) and reduce by 50% (green). (2/n)
In early March 2020, the virus spread uncontrolled in Switzerland with a reproduction number of 2.8 which corresponds to a doubling time of 3 days. (3/n)
Die @SwissScience_TF schlägt vor, dass wir die Fallzahlen alle zwei Wochen halbieren, um im Januar wieder auf unter 500 täglich bestätigte Fällen zu gelangen. Was bedeutet das bezüglich der Reproduktionszahl? Ein Thread zu Verdoppelungs- und Halbwertszeiten bei #SARSCoV2. (1/n)
Die Grafik zeigt den Zusammenhang zwischen der Reproduktionszahl und der Zeit in welcher sich die Anzahl Neuinfektionen von #SARSCoV2 verdoppeln (rot) bzw. halbieren (grün). (2/n)
Anfang März 2020 konnte sich das Virus in der Schweiz unkontrolliert ausbreiten, was einer Reproduktionszahl von 2,8 und einer Verdoppelungszeit von 3 Tagen entsprach. (3/n)
Reichen die seit 29. Oktober geltenden Massnahmen um eine Überlastung des Gesundheitswesens zu verhindern und die Anzahl Neuinfektionen mit #SARSCoV2 wieder auf ein überschaubares Niveau zu bringen? Thread über die zentrale Rolle einer frühzeitigen Einführung von Massnahmen. 1/n
Der Bericht wies darauf hin, dass die Testpositivität über die letzen sieben Tage bereits 7% betrug, was über dem von der @WHO empfohlenen Grenzwert von 5% lag. 3/n