IDEAS home Printed from
   My bibliography  Save this paper

Projecting the Spread of COVID19 for Germany


  • Jean Roch Donsimoni

    () (Johannes Gutenberg University Mainz)

  • René Glawion

    () (Hamburg University)

  • Bodo Plachter

    () (Johannes Gutenberg University Mainz)

  • Klaus Wälde

    () (Johannes Gutenberg University Mainz)


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 so- lution 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 con- tacts. We also study the effects of public regulations. For all scenarios we report the expected end of the CoV-2 epidemic. We have four general findings: First, current epidemiological thinking implies that the long-run effects of the epidemic only depend on the aggregate long-run infection rate and on the individual risk to turn sick after an infection. Any measures by individuals and the public therefore only influence the dynamics of spread of CoV- 2. Second, predictions about the duration and level of the epidemic must strongly distinguish between the officially reported numbers (Robert Koch Institut, RKI) and actual numbers of sick individuals. Third, given the current (scarce) medical knowledge about long-run infection rate and individual risks to turn sick, any pre- diction on the length (duration in months) and strength (e.g. maximum numbers of sick individuals on a given day) is subject to a lot of uncertainty. Our predictions therefore offer robustness analyses that provide ranges on how long the epidemic will last and how strong it will be. Fourth, public interventions that are already in place and that are being discussed can lead to more and less severe outcomes of the epidemic. If an intervention takes place too early, the epidemic can actually be stronger than with an intervention that starts later. Interventions should therefore be contingent on current infection rates in regions or countries. Concerning predictions about COVID-19 in Germany, we find that the long-run number of sick individuals (that are reported to the RKI), once the epidemic is over, will lie between 500 thousand and 5 million individuals. While this seems to be an absurd large range for a precise projection, this reflects the uncertainty about the long-run infection rate in Germany. If we assume that Germany will follow the good scenario of Hubei (and we are even a bit more conservative given discussions about data quality), we will end up with 500 thousand sick individuals over the entire epidemic. If by contrast we believe (as many argue) that once the epidemic is over 70% of the population will have been infected (and thereby immune), we will end up at 5 million cases. Defining the end of the epidemic by less than 100 newly reported sick individuals per day, we find a large variation depending on the effectiveness of governmental pleas and regulations to reduce social contacts. An epidemic that is not influenced by public health measures would end mid June 2020. With public health measures lasting for few weeks, the end is delayed by around one month or two. The ad- vantage of the delay, however, is to reduce the peak number of individuals that are simultaneously sick. When we believe in long-run infection rates of 70%, this number is equally high for all scenarios we went through and well above 1 million. When we can hope for the Hubei-scenario, the maximum number of sick individuals will be around 200 thousand only Whatever value of the range of long-run infection rates we want to assume, the epidemic will last at least until June, with extensive and potentially future public health measures, it will last until July. In the worst case, it will last until end of August. We emphasize that all projections are subject to uncertainty and permanent mon- itoring of observed incidences are taken into account to update the projection. The most recent projections are available at https://www.macro.economics.uni-

Suggested Citation

  • Jean Roch Donsimoni & René Glawion & Bodo Plachter & Klaus Wälde, 2020. "Projecting the Spread of COVID19 for Germany," Working Papers 2006, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
  • Handle: RePEc:jgu:wpaper:2006

    Download full text from publisher

    File URL:
    File Function: First version, 2020
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    1. Greg Kaplan & Benjamin Moll & Giovanni L. Violante, 2018. "Monetary Policy According to HANK," American Economic Review, American Economic Association, vol. 108(3), pages 697-743, March.
    2. Hoang Khieu & Klaus Wälde, 2018. "Capital Income Risk and the Dynamics of the Wealth Distribution," Working Papers 1814, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    3. Christian BAYER & Klaus WALDE, 2010. "Matching and Saving in Continuous Time: Proofs," Discussion Papers (IRES - Institut de Recherches Economiques et Sociales) 2010014, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
    4. Andrew Caplin & John Leahy, 2001. "Psychological Expected Utility Theory and Anticipatory Feelings," The Quarterly Journal of Economics, Oxford University Press, vol. 116(1), pages 55-79.
    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.
    7. Florian Dorn & Clemens Fuest & Marcell Göttert & Carla Krolage & Stefan Lautenbacher & Sebastian Link & Andreas Peichl & Magnus Reif & Stefan Sauer & Marc Stöckli & Klaus Wohlrabe & Timo Wollmershäuse, 2020. "Die volkswirtschaftlichen Kosten des Corona-Shutdown für Deutschland: Eine Szenarienrechnung," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 73(04), pages 29-35, April.
    8. Andrew Atkeson, 2020. "What Will Be the Economic Impact of COVID-19 in the US? Rough Estimates of Disease Scenarios," NBER Working Papers 26867, National Bureau of Economic Research, Inc.
    9. Pissarides, Christopher A, 1985. "Short-run Equilibrium Dynamics of Unemployment Vacancies, and Real Wages," American Economic Review, American Economic Association, vol. 75(4), pages 676-690, September.
    10. Christian BAYER & Klaus WALDE, 2010. "Matching and Saving in Continuous Time: Proofs," LIDAM Discussion Papers IRES 2010014, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
    11. Christian Bayer & Klaus Wälde, 2010. "Matching and Saving in Continuous Time: Theory," Working Papers 1004, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz, revised 13 Jan 2010.
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. 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.
    2. 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.
    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bayer, Christian & Rendall, Alan D. & Wälde, Klaus, 2019. "The invariant distribution of wealth and employment status in a small open economy with precautionary savings," Journal of Mathematical Economics, Elsevier, vol. 85(C), pages 17-37.
    2. Flórez, Luz A., 2017. "Informal sector under saving: A positive analysis of labour market policies," Labour Economics, Elsevier, vol. 44(C), pages 13-26.
    3. Jeremy Lise, 2013. "On-the-Job Search and Precautionary Savings," Review of Economic Studies, Oxford University Press, vol. 80(3), pages 1086-1113.
    4. John Gibson & Garth Heutel, 2020. "Pollution and Labor Market Search Externalities Over the Business Cycle," NBER Working Papers 27445, National Bureau of Economic Research, Inc.
    5. Fedorets, Alexandra & Lottmann, Franziska & Stops, Michael, 2019. "Job matching in connected regional and occupational labour markets," EconStor Open Access Articles, ZBW - Leibniz Information Centre for Economics, pages 1085-1098.
    6. Petrosky-Nadeau, Nicolas & Wasmer, Etienne, 2015. "Macroeconomic dynamics in a model of goods, labor, and credit market frictions," Journal of Monetary Economics, Elsevier, vol. 72(C), pages 97-113.
    7. Altig, Dave & Baker, Scott & Barrero, Jose Maria & Bloom, Nicholas & Bunn, Philip & Chen, Scarlet & Davis, Steven J. & Leather, Julia & Meyer, Brent & Mihaylov, Emil & Mizen, Paul & Parker, Nicholas &, 2020. "Economic uncertainty before and during the COVID-19 pandemic," Journal of Public Economics, Elsevier, vol. 191(C).
    8. William Hawkins, 2013. "Worker Flows under Mismatch," 2013 Meeting Papers 479, Society for Economic Dynamics.
    9. Meimei Wang & Steffen Flessa, 2020. "Modelling Covid-19 under uncertainty: what can we expect?," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(5), pages 665-668, July.
    10. Baert, Stijn & Lippens, Louis & Moens, Eline & Weytjens, Johannes & Sterkens, Philippe, 2020. "The COVID-19 Crisis and Telework: A Research Survey on Experiences, Expectations and Hopes," IZA Discussion Papers 13229, Institute of Labor Economics (IZA).
    11. Sniekers, F.J.T., 2013. "Endogenous Beveridge cycles and the volatility of unemployment," CeNDEF Working Papers 13-12, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    12. Jonathan Heathcote & Fabrizio Perri, 2018. "Wealth and Volatility," Review of Economic Studies, Oxford University Press, vol. 85(4), pages 2173-2213.
    13. Sterk, Vincent, 2016. "The dark corners of the labor market," LSE Research Online Documents on Economics 86244, London School of Economics and Political Science, LSE Library.
    14. Laura Alfaro & Anusha Chari & Andrew N. Greenland & Peter K. Schott, 2020. "Aggregate and Firm-Level Stock Returns During Pandemics, in Real Time," NBER Working Papers 26950, National Bureau of Economic Research, Inc.
    15. Jeremy Lise & Shannon Seitz & Jeffrey Smith, 2015. "Evaluating search and matching models using experimental data," IZA Journal of Labor Economics, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 4(1), pages 1-35, December.
    16. Alexandron-Lavon, Anat & Epstein, Gil S. & Lindner-Pomerantz, Renana, 2018. "The effect of ideological positions on job market interaction: A spatial analysis," Journal of Economic Behavior & Organization, Elsevier, vol. 145(C), pages 261-274.
    17. Michael Barnett & Greg Buchak & Constantine Yannelis, 2020. "Epidemic Responses Under Uncertainty," NBER Working Papers 27289, National Bureau of Economic Research, Inc.
    18. Tesfatsion, Leigh, 1998. "Ex Ante Capacity Effects in Evolutionary Labor Markets with Adaptive Search," ISU General Staff Papers 199810010700001046, Iowa State University, Department of Economics.
    19. Chen, Been-Lon & Mo, Jie-Ping & Wang, Ping, 2002. "Market frictions, technology adoption and economic growth," Journal of Economic Dynamics and Control, Elsevier, vol. 26(11), pages 1927-1954, September.
    20. Dustmann, Christian & Glitz, Albrecht & Vogel, Thorsten, 2010. "Employment, wages, and the economic cycle: Differences between immigrants and natives," European Economic Review, Elsevier, vol. 54(1), pages 1-17, January.

    More about this item


    Corona; COVID19; 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

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jgu:wpaper:2006. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Research Unit IPP). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.