This is a component of the ad hoc covid19 data project connected to the FUFF platform (fuff.org)
http://fuff.org/data/cr0.html
At the bottom of this page are tabs. they link to the other sheets/pages
important examples on other web pages:
the danger of averages - (this is also about the effects of clusterization):
http://fuff.org/data/cr2_about_clustering_and_averages.html
the miracle of math(sk)s - (it is valid for other interventions as well):
http://fuff.org/data/cr2_example_masks.html
the risk with protecting risk groups
see tab '4 cluster example'
some other example results for different parameters
initial model, without the cluster system, no measures
population:	80,000,000		devide:	100	0		depth:	1		days:	15
no measures, majority of population behaves normal
population:	80,000,000		devide:	85	15		depth:	1.15		days:	15
sluggish reaction, early loosening of measures
population:	80,000,000		devide:	67	33		depth:	1.15		days:	15
earlly drastic measures, standard values, less strict after 100 days  // because of clusterization infections increase although average spread is only 1/person -> the dangers of averages example for explanation!
population:	80,000,000		devide:	67	33		depth:	1.15		days:	15
earlly drastic measures, excessive testing and monitoring reduces the number of days infected/person, so that spread/person is half of that what you see in the chart
population:	80,000,000		devide:	67	33		depth:	1.15		days:	8
however loosening the grip, still leads to...
population:	80,000,000		devide:	67	33		depth:	1.15		days:	8
initial model, without the cluster system, but with a succession of measures and loosening
population:	80,000,000		devide:	100	0		depth:	1		days:	15
another example of buying time
population:	80,000,000		devide:	75	25		depth:	1.1		days:	15
initial model, without the cluster system, excessive testing and monitoring reduces the number of days infected/person, so that spread/person is half of that what you see in the chart
population:	80,000,000		devide:	100	0		depth:	1		days:	8
this effect makes me curious: is there a flaw in the model, or does 'herd immunity' work here? I have not had time yet to check this
new: I modified the model so that you have two variable parameters over time for measures: lockdown (reducing the spread prob for each person), and new: excessive testing (reducing the period of days a person spreads before becoming isolated)
sluggish reaction, early loosening of measures
population:	80,000,000		devide:	67	33		depth:	1.15
here you can see the possible importance of clustering. without clustering the parameters suggest a solution that might be is not there:
population:	80,000,000		devide:	100	0		depth:	1