IDEAS home Printed from https://ideas.repec.org/p/rim/rimwps/20-23.html
   My bibliography  Save this paper

Bridging the COVID-19 Data and the Epidemiological Model using Time Varying Parameter SIRD Model

Author

Listed:
  • Cem Cakmaklı

    (Koc University, Turkey; Rimini Centre for Economic Analysis)

  • Yasin Simsek

    (Duke University, USA)

Abstract

This paper extends the canonical model of epidemiology, the SIRD model, to allow for time-varying parameters for real-time measurement and prediction of the trajectory of the Covid-19 pandemic. Time variation in model parameters is captured using the generalized autoregressive score modeling structure designed for the typical daily count data related to the pandemic. The resulting specification permits a flexible yet parsimonious model with a low computational cost. The model is extended to allow for unreported cases as well. Results suggest that these cases' effects on the parameter estimates diminish with the increasing number of testing. Full sample results show that the flexible framework captures the successive waves of the pandemic accurately. A real-time exercise indicates that the proposed structure delivers timely and precise information on the pandemic's current stance. This superior performance, in turn, transforms into accurate predictions of the confirmed cases.

Suggested Citation

  • Cem Cakmaklı & Yasin Simsek, 2020. "Bridging the COVID-19 Data and the Epidemiological Model using Time Varying Parameter SIRD Model," Working Paper series 20-23, Rimini Centre for Economic Analysis, revised Feb 2021.
  • Handle: RePEc:rim:rimwps:20-23
    as

    Download full text from publisher

    File URL: http://rcea.org/RePEc/pdf/wp20-23.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Fokianos, Konstantinos & Rahbek, Anders & Tjøstheim, Dag, 2009. "Poisson Autoregression," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1430-1439.
    2. Manski, Charles F. & Molinari, Francesca, 2021. "Estimating the COVID-19 infection rate: Anatomy of an inference problem," Journal of Econometrics, Elsevier, vol. 220(1), pages 181-192.
    3. Siem Jan Koopman & André Lucas & Marcel Scharth, 2016. "Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models," The Review of Economics and Statistics, MIT Press, vol. 98(1), pages 97-110, March.
    4. Cleo Anastassopoulou & Lucia Russo & Athanasios Tsakris & Constantinos Siettos, 2020. "Data-based analysis, modelling and forecasting of the COVID-19 outbreak," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-21, March.
    5. Fernández-Villaverde, Jesús & Jones, Charles I., 2022. "Estimating and simulating a SIRD Model of COVID-19 for many countries, states, and cities," Journal of Economic Dynamics and Control, Elsevier, vol. 140(C).
    6. Hortaçsu, Ali & Liu, Jiarui & Schwieg, Timothy, 2021. "Estimating the fraction of unreported infections in epidemics with a known epicenter: An application to COVID-19," Journal of Econometrics, Elsevier, vol. 220(1), pages 106-129.
    7. Richard A. Davis, 2003. "Observation-driven models for Poisson counts," Biometrika, Biometrika Trust, vol. 90(4), pages 777-790, December.
    8. René Ferland & Alain Latour & Driss Oraichi, 2006. "Integer‐Valued GARCH Process," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(6), pages 923-942, November.
    9. Daron Acemoglu & Victor Chernozhukov & Ivàn Werning & Michael D. Whinston, 2020. "A Multi-Risk SIR Model with Optimally Targeted Lockdown," CeMMAP working papers CWP14/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. Drew Creal & Siem Jan Koopman & André Lucas, 2013. "Generalized Autoregressive Score Models With Applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(5), pages 777-795, August.
    11. Chen, Cathy W.S. & Lee, Sangyeol, 2016. "Generalized Poisson autoregressive models for time series of counts," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 51-67.
    12. Ali Hortaçsu & Jiarui Liu & Timothy Schwieg, 2020. "Estimating the Fraction of Unreported Infections in Epidemics with a Known Epicenter: An Application to COVID-19," Working Papers 2020-37, Becker Friedman Institute for Research In Economics.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Alexander Chudik & M. Hashem Pesaran & Alessandro Rebucci, 2021. "COVID-19 Time-Varying Reproduction Numbers Worldwide: An Empirical Analysis of Mandatory and Voluntary Social Distancing," Globalization Institute Working Papers 407, Federal Reserve Bank of Dallas.
    2. Cem Çakmaklı & Selva Demiralp & Ṣebnem Kalemli-Özcan & Sevcan Yeşiltaş & Muhammed A. Yıldırım, 2021. "The Economic Case for Global Vaccinations: An Epidemiological Model with International Production Networks," NBER Working Papers 28395, National Bureau of Economic Research, Inc.

    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. Cem Cakmakli & Yasin Simsek, 2023. "Bridging the Covid-19 Data and the Epidemiological Model using Time-Varying Parameter SIRD Model," Papers 2301.13692, arXiv.org.
    2. Jonas E. Arias & Jesús Fernández-Villaverde & Juan F. Rubio-Ramirez & Minchul Shin, 2021. "Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs," Working Papers 21-18, Federal Reserve Bank of Philadelphia.
    3. Garriga, Carlos & Manuelli, Rody & Sanghi, Siddhartha, 2022. "Optimal management of an epidemic: Lockdown, vaccine and value of life," Journal of Economic Dynamics and Control, Elsevier, vol. 140(C).
    4. Kent A. Smetters, 2020. "Stay-at-home orders and second waves: a graphical exposition," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 45(2), pages 94-103, September.
    5. Jonas E. Arias & Jesús Fernández-Villaverde & Juan F. Rubio-Ramírez & Minchul Shin, 2021. "Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs," CESifo Working Paper Series 8977, CESifo.
    6. Jonas E. Arias & Jesús Fernández-Villaverde & Juan Rubio Ramírez & Minchul Shin, 2021. "The Causal Effects of Lockdown Policies on Health and Macroeconomic Outcomes," NBER Working Papers 28617, National Bureau of Economic Research, Inc.
    7. Daniel L. Millimet & Christopher F. Parmeter, 2022. "COVID‐19 severity: A new approach to quantifying global cases and deaths," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1178-1215, July.
    8. Xinyang Wang & Dehui Wang & Kai Yang, 2021. "Integer-valued time series model order shrinkage and selection via penalized quasi-likelihood approach," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(5), pages 713-750, July.
    9. Paolo Gorgi, 2020. "Beta–negative binomial auto‐regressions for modelling integer‐valued time series with extreme observations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(5), pages 1325-1347, December.
    10. Roland Pongou & Guy Tchuente & Jean-Baptiste Tondji, 2020. "An Economic Model of Health-vs-Wealth Prioritization During COVID-19: Optimal Lockdown, Network Centrality, and Segregation," Working Papers 2009E Classification-E61,, University of Ottawa, Department of Economics.
    11. Robert Jung & A. Tremayne, 2011. "Useful models for time series of counts or simply wrong ones?," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(1), pages 59-91, March.
    12. Gourieroux, C. & Jasiak, J., 2023. "Time varying Markov process with partially observed aggregate data: An application to coronavirus," Journal of Econometrics, Elsevier, vol. 232(1), pages 35-51.
    13. Francisco Blasques & Siem Jan Koopman & André Lucas, 2014. "Maximum Likelihood Estimation for correctly Specified Generalized Autoregressive Score Models: Feedback Effects, Contraction Conditions and Asymptotic Properties," Tinbergen Institute Discussion Papers 14-074/III, Tinbergen Institute.
    14. Douc, R. & Doukhan, P. & Moulines, E., 2013. "Ergodicity of observation-driven time series models and consistency of the maximum likelihood estimator," Stochastic Processes and their Applications, Elsevier, vol. 123(7), pages 2620-2647.
    15. Fernández-Villaverde, Jesús & Jones, Charles I., 2022. "Estimating and simulating a SIRD Model of COVID-19 for many countries, states, and cities," Journal of Economic Dynamics and Control, Elsevier, vol. 140(C).
    16. Giovanni Angelini & Giuseppe Cavaliere & Enzo D'Innocenzo & Luca De Angelis, 2022. "Time-Varying Poisson Autoregression," Papers 2207.11003, arXiv.org.
    17. Jordan J Bird & Chloe M Barnes & Cristiano Premebida & Anikó Ekárt & Diego R Faria, 2020. "Country-level pandemic risk and preparedness classification based on COVID-19 data: A machine learning approach," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-20, October.
    18. David Berger & Kyle Herkenhoff & Chengdai Huang & Simon Mongey, 2022. "Testing and Reopening in an SEIR Model," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 43, pages 1-21, January.
    19. Kitagawa, Toru & Wang, Guanyi, 2023. "Who should get vaccinated? Individualized allocation of vaccines over SIR network," Journal of Econometrics, Elsevier, vol. 232(1), pages 109-131.
    20. Roland Pongou & Guy Tchuente & Jean-Baptiste Tondji, 2021. "Optimally Targeting Interventions in Networks during a Pandemic: Theory and Evidence from the Networks of Nursing Homes in the United States," Papers 2110.10230, arXiv.org.

    More about this item

    Keywords

    Covid-19; SIRD; Observation driven models; Score models; Count data; Time-varying parameters;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • I19 - Health, Education, and Welfare - - Health - - - Other

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    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:rim:rimwps:20-23. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Marco Savioli (email available below). General contact details of provider: https://edirc.repec.org/data/rcfeait.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.