There is a newer version of this record available.

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


Final Draft. CSV Files of prognosis data and updates to the figures will be published here:
Files (640.2 kB)
Name Size
640.2 kB Download
  • Ferguson et al. (2020), "Impact of Non-Pharmaceutical Interventions (NPIs) to Reduce COVID- 19 Mortality and Healthcare Demand,"

  • Hethcote, Herbert W. 2000. "The Mathematics of Infectious Diseases." SIAM Review 42 (4): 599–653.

  • 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: //

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

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

All versions This version
Views 2,3611,142
Downloads 1,436862
Data volume 913.9 MB551.9 MB
Unique views 2,0751,059
Unique downloads 1,240774


Cite as