A semi-quantitative framework for long-term thinking about the COVID-19 pandemic

I think the current rush to invoke extreme flatten-the-curve measures needs to be accompanied by careful thought about what we'll do once the measures have had the desired effect.  In particular, how long would restrictive measures need to remain in force, and how will we decide when they can be lifted?  And how can we mitigate the personal, social and economic harms of the measures while they remain in place?So I've created a series of semi-quantitative graphs to help.  ('Semi-quantitative means that there are numbers on the axes and specific doubling times for periods of exponential growth, but the finer details are rough approximations.)Here's the tl;dr for the first 6 months:Points to note:  The Y-axis is log-scale, so small differences in height indicate big differences in numbers of infected people.Five different scenarios are considered, with plausible effects on doubling time of % infected.Restrictive measures are assumed to reduce peak % infected and eventual equilibrium.For all but the most extreme scenario, infection levels remain high ( ≥1%) even after 6 months.It will be very hard to justify lifting restrictions that have been effective.Here's the tl;dr if the costly restrictions are lifted after 7 months of misery:Points to note:In all cases, lifting restrictions makes % infected much worse (remember, log-scale...).The more effective the restrictions were in limiting total infections, the worse the second wave on infection is, and the ...
Source: RRResearch - Category: Molecular Biology Authors: Source Type: blogs