  I employ a simple mathematical model of an epidemic process to evaluate how
four basic quantities: the reproduction number (R), the numbers of sensitive
(S) and infectious individuals(I), and total community size (N) affect
strategies to control COVID-19. Numerical simulations show that strict
suppression measures at the beginning of an epidemic can create low infectious
numbers, which thereafter can be managed by mitigation measures over longer
periods to flatten the epidemic curve. The stronger the suppression measure,
the faster it achieves the low numbers of infections that are conducive to
subsequent management. We discuss the predictions of this analysis and how it
fits into longer-term sequences of measures, including using the herd immunity
concept to leverage acquired immunity.
