  The Dynamical Survival Analysis (DSA) is a framework for modeling epidemics
based on mean field dynamics applied to individual (agent) level history of
infection and recovery. Recently, DSA has been shown to be an effective tool in
analyzing complex non-Markovian epidemic processes that are otherwise difficult
to handle using standard methods. One of the advantages of DSA is its
representation of typical epidemic data in a simple although not explicit form
that involves solutions of certain differential equations. In this work we
describe how a complex non-Markovian DSA model may be applied to a specific
data set with the help of appropriate numerical and statistical schemes. The
ideas are illustrated with a data example of the COVID-19 epidemic in Ohio.
