There are a couple of interesting questions here. The first is the intersection of personal freedoms with looking out for the safety of one’s neighbors. It is risky to draw too much of an analogy, however,... the Coronavirus pandemic situation is a little like public cigarette smoking restrictions. The other person should have a right to smoke cigarettes and endure any consequences that might arise from the behavior. On the other hand I should be able to expect to share a public space without undue worry my neighbor’s behavior is going seriously impact my well being. The second question concerns the capacity of the infrastructure and social fabric to resist the strain of an uncontained progression of Coronavirus infection.
The question of whether people should be compelled to stay out of public places depends on who you ask. Personally, I have a preexisting condition, and I would like to to be able to go to the grocery store when it becomes an absolute necessity and have the courtesy of the other shoppers to wear masks to contain infectious particles they might not be aware they are emitting.
The impact on infrastructure and social structure is something that may be modeled. The state health authority where I live estimated the person-to-person replication rate was r0=2.5 when the outbreak started locally. That number could be debated because testing was very sporadic at that time. The current replication rate after social distancing, etc. is estimated to be r0=0.95, which suggests without aggravating factors, the disease would eventually extinguish. The initial fatality rate in this locale is IFR=1.4%. Influenza type A was estimated to have an r0=1.16 in the 2017 flu season. The Coronavirus SARS-2 that causes COVID-19 is apparently considerably more transmissible than influenza the A; exponentially more infectious it appears. Of course the numbers have to be taken under advisement because they come from a state health authority.
The best way to see what the paragraph above means is to run it through a model, which is available on The web site Five Thirty Eight:
https://fivethirtyeight.com/features/wi ... t-save-us/ Scroll down in the article to find the interactive model. The model includes intentional statistical noise, so you’ll see different outcomes affected by initial conditions each time you run the model. Note the larger the r0 value, the greater the sensitivity to minor variations in initial condition noise. What you’ll see is about the same to twice as many total deaths (which unless you’re one of the unlucky, isn’t that bad) between running r0=1 and r0=2.5. The variability between runs with r0=1 is much less, which means it is easier for the government to make an appropriately sized response at the right time. The r0=1 takes around 400 days to reach herd immunity. The r0=2.5 model peaks around 75 days typically and reaches herd immunity typically in less than 100 days though on one run it took 400 days. The big difference between the two r0 values is that r0=2.5 results in peak infection and death rates up to ten times the peak as with r0=1. It is much more difficult for a government to mount an appropriately sized and timed response with r0=2.5. New York City narrowly escaped a run-away peak that would have been several times what its health care network could handle. Even people who aren’t hospitalized may be seriously ill. Several of my acquaintances did get infected and were incapacitated for several weeks with severe flu-like symptoms. The people unable to work for several weeks could also have been up to ten times as many at the peak infection rate.
One thing which I believe significantly blunted the peak of the infection rate where I live is that public schools were closed almost immediately when the outbreak was detected. The neighboring state with almost the same population number waited a week longer to close schools and has about three times as many COVID-19 cases and twice as many deaths at the time I am writing this.