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Projecting the Spread of Covid-19 for Germany

Author

Listed:
  • Jean Roch Donsimoni
  • René Glawion
  • Bodo Plachter
  • Klaus Wälde

Abstract

We model the evolution of the number of individuals that are reported to be sick with Covid-19 in Germany. Our theoretical framework builds on a continuous time Markov chain with four states: healthy without infection, sick, healthy after recovery or after infection but without symptoms and dead. Our quantitative solution matches the number of sick individuals up to the most recent observation and ends with a share of sick individuals following from infection rates and sickness probabilities. We employ this framework to study inter alia the expected peak of the number of sick individuals in a scenario without public regulation of social contacts. We also study the effects of public regulations. For all scenarios we report the expected end of the CoV-2 epidemic.

Suggested Citation

  • Jean Roch Donsimoni & René Glawion & Bodo Plachter & Klaus Wälde, 2020. "Projecting the Spread of Covid-19 for Germany," CESifo Working Paper Series 8183, CESifo.
  • Handle: RePEc:ces:ceswps:_8183
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    References listed on IDEAS

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    5. Andrew Atkeson, 2020. "What Will be the Economic Impact of COVID-19 in the US? Rough Estimates of Disease Scenarios," Staff Report 595, Federal Reserve Bank of Minneapolis.
    6. Diamond, Peter A, 1982. "Aggregate Demand Management in Search Equilibrium," Journal of Political Economy, University of Chicago Press, vol. 90(5), pages 881-894, October.
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    10. Christian Bayer & Klaus Wälde, 2010. "Matching and Saving in Continuous Time: Proofs," CESifo Working Paper Series 26-A, CESifo.
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    Cited by:

    1. Busch, Christopher & Ludwig, Alexander & Santaeulàlia-Llopis, Raül, 2020. "Emerging evidence of a silver lining: A ridge walk to avoid an economic catastrophe in Italy and Spain," SAFE White Paper Series 67, Leibniz Institute for Financial Research SAFE.
    2. Jean Roch Donsimoni & René Glawion & Bodo Plachter & Klaus Wälde, 2020. "Projektion der COVID-19-Epidemie in Deutschland [Projecting the Spread of COVID-19 for Germany]," Wirtschaftsdienst, Springer;ZBW - Leibniz Information Centre for Economics, vol. 100(4), pages 272-276, April.
    3. Klaus Wälde, 2020. "How to remove the testing bias in CoV-2 statistics," Working Papers 2021, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    4. Michael Berlemann & Erik Haustein, 2020. "Right and Yet Wrong: A Spatio-Temporal Evaluation of Germany's Covid-19 Containment Policy," CESifo Working Paper Series 8446, CESifo.
    5. Gabler, Janos & Raabe, Tobias & Röhrl, Klara, 2020. "People Meet People: A Microlevel Approach to Predicting the Effect of Policies on the Spread of COVID-19," IZA Discussion Papers 13899, Institute of Labor Economics (IZA).
    6. Ulrich Glogowsky & Emanuel Hansen & Simeon Schächtele, 2020. "How Effective Are Social Distancing Policies? Evidence on the Fight against Covid-19 from Germany," CESifo Working Paper Series 8361, CESifo.
    7. Tödter, Karl-Heinz, 2020. "Ein SIRD-Modell zur Infektionsdynamik mit endogener Behandlungskapazität und Lehren für Corona-Statistiken," IMFS Working Paper Series 141, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).

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    More about this item

    Keywords

    Corona; Covid-19; SARS-CoV-2; spread of infection; Markov model; Germany; projection.;
    All these keywords.

    JEL classification:

    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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