IDEAS home Printed from https://ideas.repec.org/a/spr/qualqt/v56y2022i6d10.1007_s11135-022-01340-w.html
   My bibliography  Save this article

COVID-19: average time from infection to death in Poland, USA, India and Germany

Author

Listed:
  • Antoni Wiliński

    (WSB University)

  • Łukasz Kupracz

    (Koszalin University of Technology)

  • Aneta Senejko

    (WSB University)

  • Grzegorz Chrząstek

    (WSB University)

Abstract

There are many discussions in the media about an interval (delay) from the time of the infections to deaths. Apart from the curiosity of the researchers, defining this time interval may, under certain circumstances, be of great organizational and economic importance. The study considers an attempt to determine this difference through the correlations of shifted time series and a specific bootstrapping that allows finding the distance between local maxima on the series under consideration. We consider data from Poland, the USA, India and Germany. The median of the difference’s distribution is quite consistent for such diverse countries. The main conclusion of our research is that the searched interval has rather a multimodal form than unambiguously determined.

Suggested Citation

  • Antoni Wiliński & Łukasz Kupracz & Aneta Senejko & Grzegorz Chrząstek, 2022. "COVID-19: average time from infection to death in Poland, USA, India and Germany," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 4729-4746, December.
  • Handle: RePEc:spr:qualqt:v:56:y:2022:i:6:d:10.1007_s11135-022-01340-w
    DOI: 10.1007/s11135-022-01340-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11135-022-01340-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11135-022-01340-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Leopold Simar & Paul Wilson, 2000. "A general methodology for bootstrapping in non-parametric frontier models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(6), pages 779-802.
    2. Jiang, Feiyu & Zhao, Zifeng & Shao, Xiaofeng, 2023. "Time series analysis of COVID-19 infection curve: A change-point perspective," Journal of Econometrics, Elsevier, vol. 232(1), pages 1-17.
    3. Maleki, Mohsen & Mahmoudi, Mohammad Reza & Heydari, Mohammad Hossein & Pho, Kim-Hung, 2020. "Modeling and forecasting the spread and death rate of coronavirus (COVID-19) in the world using time series models," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    4. Salvatore F. Pileggi, 2021. "Life before COVID-19: how was the World actually performing?," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(5), pages 1871-1888, October.
    5. Jeremy Berkowitz & Lutz Kilian, 2000. "Recent developments in bootstrapping time series," Econometric Reviews, Taylor & Francis Journals, vol. 19(1), pages 1-48.
    6. Zeroual, Abdelhafid & Harrou, Fouzi & Dairi, Abdelkader & Sun, Ying, 2020. "Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    7. Takis Venetoklis, 2021. "Exogenous shocks and citizens’ satisfaction with governmental policies: can empirical evidence from the 2008 financial crisis help us understand better the effects of the COVID-19 pandemic?," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(6), pages 1973-2000, December.
    8. Chiara Sotis, 2021. "How do Google searches for symptoms, news and unemployment interact during COVID-19? A Lotka–Volterra analysis of google trends data," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(6), pages 2001-2016, December.
    9. Salgotra, Rohit & Gandomi, Mostafa & Gandomi, Amir H, 2020. "Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    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. Bello, Piera & Rocco, Lorenzo, 2022. "Education and COVID-19 excess mortality," Economics & Human Biology, Elsevier, vol. 47(C).

    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. Hwang, Eunju, 2022. "Prediction intervals of the COVID-19 cases by HAR models with growth rates and vaccination rates in top eight affected countries: Bootstrap improvement," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    2. Medeiros, Marcelo C. & Street, Alexandre & Valladão, Davi & Vasconcelos, Gabriel & Zilberman, Eduardo, 2022. "Short-term Covid-19 forecast for latecomers," International Journal of Forecasting, Elsevier, vol. 38(2), pages 467-488.
    3. Zehra Taşkın, 2021. "Forecasting the future of library and information science and its sub-fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1527-1551, February.
    4. Schaum, A. & Bernal-Jaquez, R. & Alarcon Ramos, L., 2022. "Data-assimilation and state estimation for contact-based spreading processes using the ensemble kalman filter: Application to COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    5. Christian Acal & Manuel Escabias & Ana M. Aguilera & Mariano J. Valderrama, 2021. "COVID-19 Data Imputation by Multiple Function-on-Function Principal Component Regression," Mathematics, MDPI, vol. 9(11), pages 1-23, May.
    6. Kalantari, Mahdi, 2021. "Forecasting COVID-19 pandemic using optimal singular spectrum analysis," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    7. Tayarani N., Mohammad-H., 2021. "Applications of artificial intelligence in battling against covid-19: A literature review," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    8. Li, Xingchen & Xu, Guangcheng & Wu, Jie & Xu, Chengzhen & Zhu, Qingyuan, 2024. "Evaluation of bank efficiency by considering the uncertainty of nonperforming loans," Omega, Elsevier, vol. 126(C).
    9. Franz R. Hahn, 2007. "Determinants of Bank Efficiency in Europe. Assessing Bank Performance Across Markets," WIFO Studies, WIFO, number 31499, March.
    10. Berkowitz, J. & Birgean, I. & Kilian, L., 1999. "On the Finite-Sample Accuracy of Nonparametric Resampling Algorithms for Economic Time Series," Papers 99-01, Michigan - Center for Research on Economic & Social Theory.
    11. Kerstin Enflo & Per Hjertstrand, 2009. "Relative Sources of European Regional Productivity Convergence: A Bootstrap Frontier Approach," Regional Studies, Taylor & Francis Journals, vol. 43(5), pages 643-659.
    12. Thomas George & Chuan-Yang Hwang & Tavy Ronen, 2010. "Bootstrap refinements in tests of microstructure frictions," Review of Quantitative Finance and Accounting, Springer, vol. 35(1), pages 47-70, July.
    13. Goncalves, Silvia & Kilian, Lutz, 2004. "Bootstrapping autoregressions with conditional heteroskedasticity of unknown form," Journal of Econometrics, Elsevier, vol. 123(1), pages 89-120, November.
    14. Guochang Wang & Wai Keung Li & Ke Zhu, 2018. "New HSIC-based tests for independence between two stationary multivariate time series," Papers 1804.09866, arXiv.org.
    15. Léopold Simar & Paul Wilson, 2000. "Statistical Inference in Nonparametric Frontier Models: The State of the Art," Journal of Productivity Analysis, Springer, vol. 13(1), pages 49-78, January.
    16. George E. Halkos & Nickolaos G. Tzeremes, 2015. "Measuring Seaports' Productivity: A Malmquist Productivity Index Decomposition Approach," Journal of Transport Economics and Policy, University of Bath, vol. 49(2), pages 355-376, April.
    17. Yamaka, Woraphon & Zhang, Xuefeng & Maneejuk, Paravee & Ramos, Vicente, 2023. "Asymmetric effects of third-country exchange rate risk: A Markov switching approach," Annals of Tourism Research, Elsevier, vol. 103(C).
    18. Omtzigt Pieter & Fachin Stefano, 2002. "Bootstrapping and Bartlett corrections in the cointegrated VAR model," Economics and Quantitative Methods qf0212, Department of Economics, University of Insubria.
    19. Jeon, Byung M. & Sickles, Robin, 2001. "The Role of Environmental Factors in Growth Accounting: A Nonparametric Analysis," Working Papers 2001-08, Rice University, Department of Economics.
    20. Eling, Martin & Jia, Ruo, 2018. "Business failure, efficiency, and volatility: Evidence from the European insurance industry," International Review of Financial Analysis, Elsevier, vol. 59(C), pages 58-76.

    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:spr:qualqt:v:56:y:2022:i:6:d:10.1007_s11135-022-01340-w. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.