@christianbaye13 & me have been thinking about the big differences in mortality rates so far during the corona outbreak in Europe. The differences in case-fatality rates are humongous. Italy has one of 6% while countries like Norway, Denmark & Germany have rates still close to 0. 
Of course the rates will likely converge somewhat over time as the virus spreads and there are many effects in this. Division bias may play a role, as does the time since the outbreak.
However, we thought maybe there is something to learn in this in order to help to organize our lives in a way to minimize the risks for us and we pose to others. We picked up a striking data difference that @AndreasShrugged pointed out.
It has been established now that, importantly also unlike the Spanish flu, #COVID-19 is particularly deadly for the elderly and overproportionally creates the need for intensive care in these groups. Medical research and treatment are front and center to combatting this crisis
and as scientists we believe in the power of medical research and science. Yet, we as social scientists thought how we can also add something to combatting the crisis. We have been thinking, why is it that in some countries more elderly are infected than in others?
Our hypothesis is that differences in social interactions and social networks might play a key role. These differences might stem from cultural or institutional differences across countries.
The idea is simple. Suppose, in country A almost all interaction is within one group of people, i.e. working age people hang out amongst themselves and as second group of people, the elderly, do the same.
At the same time in country B, interaction is often across generations. The young and the old live together and interact, for example, by taking care of grandchildren or young workers how still live with their parents as they cannot afford to live on their own.
Since #corona was likely "imported" to Europe mostly through work related travel, a country of type A should see an INITIALLY much more contained outbreak, with much less need for intensive care and much less fatalities relative to the size of the outbreak.
Given this hypothesis, we asked how we could operationalize this idea. So we turned to the World Value Survey data (worldvaluessurvey.org/wvs.jsp) and calculated from this source the share of people between age 30-49 who live with their parents.
This share varies dramatically across countries. From shares below 5% in countries like France, Switzerland, and the Netherlands, to cases like Japan, China, South Korea, and Italy with shares above 20%.
If we take this share as measure of intergenerational interaction (how many red arrows there are), then already a simple figure highlights our key idea.
Here is what it looks like, when we plot it against the case-fatality-rate (CFR) for all industrialized economies with more than 100 cases (as of March 12)
Italy is surely an outlier in both dimensions and the CFR will hopefully go down in the end. However, what the graph tells us is that the structure of social interactions seems to clearly matter and that social distancing needs to regard particularly the elderly.
Potentially this effect will also go away over time as the virus finds its way into the elderly population and can then spread within because of course elderly are not a socially entirely disconnected set of people.
Those countries with low fatality rates, like Germany, should take this as a warning sign. Likely the low initial fatality rates are unfortunately not here to stay, once the virus spreads.
At the same time, we hope this little bit of data analysis helps us to better understand, how pivotal it is to keep the elderly uninfected and what role social networks and links play in this.
What is more, it may provide a warning sign for those countries where the elderly and the young live close together, how important it is to contain the virus there early on. These countries are within Europe in particular such as Serbia, Poland Bulgaria, Croatia, or Slovenia.
We also looked across States within Germany at the share of households with household members age 65 and older living with working age family members (age 30-49 as before).
Also within Germany we find some noticeable variation that might provide an early warning sign to policymakers in some of the heavily affected states like Bayern and Baden-Württemberg.
As social scientists, we know how important behavioral responses to changes in the environment are. Indeed, this is what we strongly hope for and in the best case when people reduce social interactions contagion might go down and keep also the number of deaths low.
The crisis might be much less severe than what we predict today but only if we react to the situation in due time. @c_drosten @rki_de @SMerler @UniBonn @henrikenderlein @FazitBlog @JoPennekamp @FabrizioZilibo1 @Hagelueken @basbrinkmann @schieritz @lisakatharina @PatrickBernau
