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Likelihood-Free Dynamical Survival Analysis applied to the COVID-19 epidemic in Ohio

  • Received: 19 September 2022 Revised: 09 December 2022 Accepted: 12 December 2022 Published: 20 December 2022
  • 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, the Dynamical Survival Analysis (DSA) method 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 Dynamical Survival Analysis (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 Dynamical Survival Analysis (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.

    Citation: Colin Klaus, Matthew Wascher, Wasiur R. KhudaBukhsh, Grzegorz A. Rempała. Likelihood-Free Dynamical Survival Analysis applied to the COVID-19 epidemic in Ohio[J]. Mathematical Biosciences and Engineering, 2023, 20(2): 4103-4127. doi: 10.3934/mbe.2023192

    Related Papers:

  • 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, the Dynamical Survival Analysis (DSA) method 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 Dynamical Survival Analysis (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 Dynamical Survival Analysis (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.



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