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The Dynamical Survival Analysis (DSA) is a framework for modeling epidemicsbased on mean field dynamics applied to individual (agent) level history ofinfection and recovery. Recently, DSA has been shown to be an effective tool inanalyzing complex non-Markovian epidemic processes that are otherwise difficultto handle using standard methods. One of the advantages of DSA is itsrepresentation of typical epidemic data in a simple although not explicit formthat involves solutions of certain differential equations. In this work wedescribe how a complex non-Markovian DSA model may be applied to a specificdata set with the help of appropriate numerical and statistical schemes. Theideas are illustrated with a data example of the COVID-19 epidemic in Ohio.

Likelihood-Free Dynamical Survival Analysis Applied to the COVID-19 Epidemic in Ohio