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

TL;DR

This paper applies a likelihood-free dynamical survival analysis framework to model the COVID-19 epidemic in Ohio, demonstrating its effectiveness in handling complex non-Markovian epidemic processes using numerical and statistical methods.

Contribution

It introduces a method to apply complex non-Markovian DSA models to real epidemic data with numerical and statistical techniques.

Findings

Successful modeling of COVID-19 in Ohio using DSA

Demonstrated the approach's ability to handle non-Markovian dynamics

Provided a framework for analyzing complex epidemic data

Abstract

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.