'Quarantine' - for how long?

Using Data to explore: "Quarantine everyone - or just the "at risk" people and over 60s (over 50s(?))?" 

I trust the consensus of our world healthcare experts, and our politicians. Between them, if they say quarantining whole country's is the best policy, then I accept that. What this leads me to ask is - how long for? 

This provides excellent data to introduce Probability and Statistics to answer "real" (non-contrived) application questions of critical importance.  Get your students experiencing, live and first hand, the work of epidemiologists, medical advisers etc.

I've found it hard/very hard to get data on:

• Age distribution of those in a CRITICAL (requiring hospitalisation certainly, ventilation machines(?) - the definitions of 'critical' are not always clear, or the same, between sources) 
• Comparable data on age distribution of number of cases (statista.com is only source I've found so far, but the age countries used from one country to next are not identical, this article comparing Italy and Korea is interesting: A Tale of Two Death Rates: Italy and Korea (I've not followed up their data sources to check . . )).
• My father-in-law, Gilles, just sent me (Fri 20th March) this excellent report from Imperial College that contains AGE DISTRIBUTION DATA. Further reports from Imperial college availalbe here THAT also look well worth following (visit the websites of reputed universities in your home/host country, I imagine you'll find similar, high-quality, authoritative reports for the situtaion in your home/host country)

Much of what we're hearing seems to suggest that Covid-19 is particularly dangerous for the over 60s and those with underlying health conditions. This lead me to wonder if, after 15 days / 30 days / x days (?) a feasible alternative could be to safeguard and protect our over 60s and those with underlying health conditions with a quarantine, but, to reduce the economic consequences and resulting political/social issues this may cause (which will be very hard to calculate and pinpoint) allow under 60s (under 50s ? The cut-off would need to be data-drive) to return to 'normal' life, but with the added precautions of hand-washing, everyone knowing the symptoms of Covid-19 and keeping 2m between ourselves, as much as possible.

Is there any evidence to support this course of action? 

I found this article (author's credentials as a Standford Professor of medicine follow) interesting, and measured. 

John P.A. Ioannidis is professor of medicine, of epidemiology and population health, of biomedical data science, and of statistics at Stanford University and co-director of Stanford’s Meta-Research Innovation Center

. . and looked up age distribution graphs by:

• number of cases
• death rate
• 'Severe' (I didn't manage to find this information, so used the below "% of deaths per total cases" age distribution data as a guideline for the proportion of the total number of "severe" conditions accounted for by each age group)

Given as I was unable to drag myself away for researching this data, and that it was a question that, I felt, only mathematics could really answer, I put together some graphs ready to investigate this question with my 14-16 year old classes later this week. 

I think I would start just by giving them the below graphs and above article (which is long and uses very adult reasoning and language - so for many I'll just ask them to higlight, in a quick, scan-read, any key points that stand out) and ask them:

Q1) How would you use the below graphs to make a case, for or against, a quarantine only for people with a"health condition or over 60 (or 50?)" after 15 days, 30 days general population quarantine? 

AGE DISTRIBUTION of those infected with COVID-19 (South Korea)

Source: www.statista.com/statistics/1102730/south-korea-coronavirus-cases-by-age/

DISTRIBUTION of total covid-19 cases that were/are: 'mild', 'severe' and 'critical' COVID-19 (China)

Source: https://ourworldindata.org/coronavirus

Source: https://ourworldindata.org/coronavirus

Q2) What assumptions have we needed to make to be able to use these graphs all together? 

Q2 (b)  Using the above graphs figures, I came up with a very rough estimates (data, assumptions etc. etc.):

under 50s chance of dieing from Covid-19: 

\(\approx\) 0.350% \(\approx\) 7 in 2000 \(\approx\) 1 in 286 

and a probability of being admitted to hospital for assistance overcoming the effects/to be able to recover from Covid-19 as:

\(\approx\)1.31% \(\approx\)1 in 76 

(i) What do you get? Show all  your working out

Based on these figures, would you recommend such a policy?