  An epidemiological model for COVID-19 was developed and implemented in
MATLAB/GNU Octave for use by public health practitioners, policy makers and the
general public. The model distinguishes four stages in the disease: infected,
sick, seriously sick, and better. The model was preliminarily parameterized
based on observations of the spread of the disease. The model is consistent
with a mortality rate of 1.5 %. Preliminary simulations with the model indicate
that concepts such as "herd immunity" and "flattening the curve" are highly
misleading in the context of this virus. Public policies based on these
concepts are inadequate to protect the population. Only reducing the R0 of the
virus below 1 is an effective strategy for maintaining the death burden of
COVID-19 within the normal range of seasonal flu. As R0 values estimated with
the model range from 2.82 worldwide outside of China and 3.83 in the Western
world in late February - early March 2020, this means social distancing with
effectiveness greater than 65 % (worldwide) or 75 % (Western world) are needed
to combat the virus successfully.
