IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0240150.html
   My bibliography  Save this article

A bivariate prediction approach for adapting the health care system response to the spread of COVID-19

Author

Listed:
  • Paolo Berta
  • Paolo Paruolo
  • Stefano Verzillo
  • Pietro Giorgio Lovaglio

Abstract

The spread of COVID-19 implied a large and fast increase of demand for intensive care services. To face this increase in demand, health care systems need to adapt their response by increasing hospital beds, intensive care unit (ICU) capacity and by (re-)deploying doctors and other personnel. This paper proposes a forecast approach based on the Vector Error Correction model for the daily counts of hospitalized patients with symptoms and of patients in ICU, using publicly available data on the current COVID-19 outbreak in Italy, Switzerland and Spain. The level of analysis is the local government managing the health care system response, which corresponds to regions for Italy. The one-week-ahead forecasts are validated with out-of-sample data over successive weeks; they are found to provide timely and robust prediction of ICU capacity needs in Lombardy, the most-affected Italian region, starting from the sample of the first 2 weeks of data. The same methodology is successfully validated on other Italian regions, Switzerland and Spain. This approach may be used in other countries/regions/provinces to help adapt the health care system response to COVID-19 (or other similar disease); for this purpose, the open-source software code to produce the forecasts is provided with the paper.

Suggested Citation

  • Paolo Berta & Paolo Paruolo & Stefano Verzillo & Pietro Giorgio Lovaglio, 2020. "A bivariate prediction approach for adapting the health care system response to the spread of COVID-19," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-14, October.
    • Handle: RePEc:plo:pone00:0240150
    DOI: 10.1371/journal.pone.0240150
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0240150
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0240150&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0240150?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
    ---><---

    References listed on IDEAS

    as
    1. Kuzin, Vladimir & Marcellino, Massimiliano & Schumacher, Christian, 2011. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area," International Journal of Forecasting, Elsevier, vol. 27(2), pages 529-542.
    2. Kenichiro McAlinn & Knut Are Aastveit & Jouchi Nakajima & Mike West, 2020. "Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(531), pages 1092-1110, July.
    3. Berta, P. & Lovaglio, P.G. & Paruolo, P. & Verzillo, S., 2020. "Real Time Forecasting of Covid-19 Intensive Care Units demand," Health, Econometrics and Data Group (HEDG) Working Papers 20/16, HEDG, c/o Department of Economics, University of York.
    4. Chris Tofallis, 2015. "A better measure of relative prediction accuracy for model selection and model estimation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(8), pages 1352-1362, August.
    5. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    6. Hendry, David F., 1995. "Dynamic Econometrics," OUP Catalogue, Oxford University Press, number 9780198283164.
    7. Ghysels, Eric, 2016. "Macroeconomics and the reality of mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 294-314.
    8. Johansen, Soren, 1991. "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models," Econometrica, Econometric Society, vol. 59(6), pages 1551-1580, November.
    9. McAlinn, Kenichiro & West, Mike, 2019. "Dynamic Bayesian predictive synthesis in time series forecasting," Journal of Econometrics, Elsevier, vol. 210(1), pages 155-169.
    10. Fotios Petropoulos & Spyros Makridakis, 2020. "Forecasting the novel coronavirus COVID-19," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-8, March.
    11. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2020. "The M4 Competition: 100,000 time series and 61 forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(1), pages 54-74.
    12. Johannes Stübinger, 2019. "Statistical arbitrage with optimal causal paths on high-frequency data of the S&P 500," Quantitative Finance, Taylor & Francis Journals, vol. 19(6), pages 921-935, June.
    13. Chris Tofallis, 2015. "A better measure of relative prediction accuracy for model selection and model estimation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(3), pages 524-524, March.
    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. Costase Ndayishimiye & Christoph Sowada & Patrycja Dyjach & Agnieszka Stasiak & John Middleton & Henrique Lopes & Katarzyna Dubas-Jakóbczyk, 2022. "Associations between the COVID-19 Pandemic and Hospital Infrastructure Adaptation and Planning—A Scoping Review," IJERPH, MDPI, vol. 19(13), pages 1-22, July.

    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. Berta, P. & Lovaglio, P.G. & Paruolo, P. & Verzillo, S., 2020. "Real Time Forecasting of Covid-19 Intensive Care Units demand," Health, Econometrics and Data Group (HEDG) Working Papers 20/16, HEDG, c/o Department of Economics, University of York.
    2. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    3. Adu, Raymond & Litsios, Ioannis & Baimbridge, Mark, 2019. "Real exchange rate and asymmetric shocks in the West African Monetary Zone (WAMZ)," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 59(C), pages 232-249.
    4. Kenichiro McAlinn, 2021. "Mixed‐frequency Bayesian predictive synthesis for economic nowcasting," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1143-1163, November.
    5. Bardsen, Gunnar & Eitrheim, Oyvind & Jansen, Eilev S. & Nymoen, Ragnar, 2005. "The Econometrics of Macroeconomic Modelling," OUP Catalogue, Oxford University Press, number 9780199246502.
    6. Mont'Alverne Duarte, Angelo & Gaglianone, Wagner Piazza & de Carvalho Guillén, Osmani Teixeira & Issler, João Victor, 2021. "Commodity prices and global economic activity: A derived-demand approach," Energy Economics, Elsevier, vol. 96(C).
    7. Thomas B. Götz & Alain W. Hecq, 2019. "Granger Causality Testing in Mixed‐Frequency VARs with Possibly (Co)Integrated Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 40(6), pages 914-935, November.
    8. Chen, Ping-Yu & Chen, Bo-Yu & Tsai, Pei-Hui & Chen, Chi-Chung, 2015. "Evaluating the impacts of a carbon tax on imported forest products—evidence from Taiwan," Forest Policy and Economics, Elsevier, vol. 50(C), pages 45-52.
    9. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
    10. Singh, Manish K. & Gómez-Puig, Marta & Sosvilla-Rivero, Simón, 2021. "Quantifying sovereign risk in the euro area," Economic Modelling, Elsevier, vol. 95(C), pages 76-96.
    11. Wang, Zijun & Bessler, David A, 2002. "The Homogeneity Restriction and Forecasting Performance of VAR-Type Demand Systems: An Empirical Examination of US Meat Consumption," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(3), pages 193-206, April.
    12. Charles G. Renfro, 2009. "The Practice of Econometric Theory," Advanced Studies in Theoretical and Applied Econometrics, Springer, number 978-3-540-75571-5, July-Dece.
    13. Franses,Philip Hans & Dijk,Dick van & Opschoor,Anne, 2014. "Time Series Models for Business and Economic Forecasting," Cambridge Books, Cambridge University Press, number 9780521520911.
    14. Jongwanich, Juthathip & Kohpaiboon, Archanun, 2008. "Private Investment: Trends and Determinants in Thailand," World Development, Elsevier, vol. 36(10), pages 1709-1724, October.
    15. Juthathip Jongwanich, 2010. "Determinants of Export Performance in East and Southeast Asia," The World Economy, Wiley Blackwell, vol. 33(1), pages 20-41, January.
    16. Sunil Sharma & Neil R. Ericsson, 1998. "Broad money demand and financial liberalization in Greece," Empirical Economics, Springer, vol. 23(3), pages 417-436.
    17. Lütkepohl, Helmut, 1999. "Vector autoregressive analysis," SFB 373 Discussion Papers 1999,31, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    18. Samargandi, Nahla & Fidrmuc, Jan & Ghosh, Sugata, 2014. "Financial development and economic growth in an oil-rich economy: The case of Saudi Arabia," Economic Modelling, Elsevier, vol. 43(C), pages 267-278.
    19. Alejandro D. Jacobo & Simón Sosvilla‐Rivero, 2021. "An empirical examination of purchasing power parity: Argentina 1810–2016," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2064-2073, April.
    20. Peter Prazmowski, 2014. "Ricardian equivalence and fiscal distortions in the Dominican Republic," Empirical Economics, Springer, vol. 46(1), pages 109-125, February.

    More about this item

    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:plo:pone00:0240150. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.