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Preprint Open Access

Adjoint-based Data Assimilation of an Epidemiology Model for the Covid-19 Pandemic in 2020

Sesterhenn, Jörn Lothar

Data assimilation is used to optimally fit a classical epidemiology  model to the Johns Hopkins data of the Covid-19 pandemic. The optimisation is based on the confirmed cases and confirmed deaths. This is the only data available with reasonable accuracy. Infection and recovery rates can be infered from the model as well as the model parameters.  The parameters can be linked with government actions or events like the end of the holiday season. Based on this numbers predictions for the future can be made and control targets specified.

With other words:

       We look for a solution to a given model which fits the given
      data in an optimal sense. Having that  solution, we have all


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  • Lemke, Mathias, Liming Cai, Julius Reiss, Heinz Pitsch, and Jörn Sesterhenn. 2018. "Adjoint-Based Sensitivity Analysis of Quantities of Interest of Complex Combustion Models." Combustion Theory and Modelling, July, 1–17. https: //

  • Robert Koch Institut. 2020. "COVID-19: Fallzahlen in Deutschland und weltweit" Fallzahlen.html.

  • Systems Science, Johns Hopkins University Center for, and Engineering. 2020. "Novel Coronavirus (Covid-19) Cases." COVID-19.

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