IDEAS home Printed from https://ideas.repec.org/a/inm/orinte/v54y2024i6p500-516.html
   My bibliography  Save this article

An Adaptive Research Approach to COVID-19 Forecasting for Regional Health Systems in England

Author

Listed:
  • Lidia Betcheva

    (Cambridge Judge Business School, University of Cambridge, Cambridge CB2 1AG, United Kingdom)

  • Feryal Erhun

    (Cambridge Judge Business School, University of Cambridge, Cambridge CB2 1AG, United Kingdom)

  • Antoine Feylessoufi

    (Cambridge Judge Business School, University of Cambridge, Cambridge CB2 1AG, United Kingdom; School of Management, University College London, London E14 5AA, United Kingdom)

  • Peter Fryers

    (National Health Service England, Cambridge CB21 5XB, United Kingdom)

  • Paulo Gonçalves

    (Cambridge Judge Business School, University of Cambridge, Cambridge CB2 1AG, United Kingdom; Faculty of Economics, Universitá della Svizzera Italiana, 6900 Lugano, Switzerland)

  • Houyuan Jiang

    (Cambridge Judge Business School, University of Cambridge, Cambridge CB2 1AG, United Kingdom)

  • Paul Kattuman

    (Cambridge Judge Business School, University of Cambridge, Cambridge CB2 1AG, United Kingdom)

  • Tom Pape

    (Cambridge Judge Business School, University of Cambridge, Cambridge CB2 1AG, United Kingdom; Public Health England, Cambridge CB21 5XA, United Kingdom)

  • Anees Pari

    (Cambridge Judge Business School, University of Cambridge, Cambridge CB2 1AG, United Kingdom; Public Health England, Cambridge CB21 5XA, United Kingdom)

  • Stefan Scholtes

    (Cambridge Judge Business School, University of Cambridge, Cambridge CB2 1AG, United Kingdom)

  • Carina Tyrrell

    (Public Health England, Cambridge CB21 5XA, United Kingdom)

Abstract

We describe the real-time participatory modeling work that our team of academics, public health officials, and clinical decision makers undertook to support the regional efforts to tackle COVID-19 in the East of England (EoE). Our team studied questions to address the pandemic’s rapidly evolving current and near-future epidemiological state as well as short-term (a few weeks) and medium-term (several months) bed capacity demand. Frequent data input from and consultations with our public health and clinical partners allowed our academic team to apply dynamic data-driven approaches using time series and system dynamics modeling. Our portfolio of models provided decision makers with the ability to ask nuanced questions. It allowed them to explore and explain different aspects of the pandemic and make more informed capacity plans in the EoE and its subregions. Our novel time series models have already been applied to India in collaboration with Indian health authorities, and the system dynamics model has been used in the canton of Ticino in Switzerland. Therefore, our work may address future epidemiological crises beyond the EoE, especially when used in conjunction with other methods as an ensemble. Additionally, the knowledge gained through our experiences and documented in this paper may guide academic-practitioner collaborations in rapid response to future disasters.

Suggested Citation

  • Lidia Betcheva & Feryal Erhun & Antoine Feylessoufi & Peter Fryers & Paulo Gonçalves & Houyuan Jiang & Paul Kattuman & Tom Pape & Anees Pari & Stefan Scholtes & Carina Tyrrell, 2024. "An Adaptive Research Approach to COVID-19 Forecasting for Regional Health Systems in England," Interfaces, INFORMS, vol. 54(6), pages 500-516, November.
    • Handle: RePEc:inm:orinte:v:54:y:2024:i:6:p:500-516
    DOI: 10.1287/inte.2023.0009
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/inte.2023.0009
    Download Restriction: no
    File URL: https://libkey.io/10.1287/inte.2023.0009?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. Dolton, Peter, 2021. "The Statistical Challenges Of Modelling Covid-19," National Institute Economic Review, National Institute of Economic and Social Research, vol. 257, pages 46-82, August.
    2. Leonardo J. Basso & Marcel Goic & Marcelo Olivares & Denis Sauré & Charles Thraves & Aldo Carranza & Gabriel Y. Weintraub & Julio Covarrubia & Cristian Escobedo & Natalia Jara & Antonio Moreno & Demia, 2023. "Analytics Saves Lives During the COVID-19 Crisis in Chile," Interfaces, INFORMS, vol. 53(1), pages 9-31, January.
    3. Hamsa Bastani & Kimon Drakopoulos & Vishal Gupta & Jon Vlachogiannis & Christos Hadjichristodoulou & Pagona Lagiou & Gkikas Magiorkinis & Dimitrios Paraskevis & Sotirios Tsiodras, 2022. "Interpretable Operations Research for High-Stakes Decisions: Designing the Greek COVID-19 Testing System," Interfaces, INFORMS, vol. 52(5), pages 398-411, September.
    4. Petropoulos, Fotios & Makridakis, Spyros & Stylianou, Neophytos, 2022. "COVID-19: Forecasting confirmed cases and deaths with a simple time series model," International Journal of Forecasting, Elsevier, vol. 38(2), pages 439-452.
    5. Fantazzini, Dean, 2020. "Short-term forecasting of the COVID-19 pandemic using Google Trends data: Evidence from 158 countries," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 59, pages 33-54.
    6. Pinar Keskinocak & Buse Eylul Oruc & Arden Baxter & John Asplund & Nicoleta Serban, 2020. "The impact of social distancing on COVID19 spread: State of Georgia case study," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-16, October.
    7. Gongyu Chen & Xinyu Fei & Huiwen Jia & Xian Yu & Siqian Shen, 2022. "The University of Michigan Implements a Hub-and-Spoke Design to Accommodate Social Distancing in the Campus Bus System Under COVID-19 Restrictions," Interfaces, INFORMS, vol. 52(6), pages 539-552, November.
    8. Jeffrey Ely & Andrea Galeotti & Jakub Steiner, 2021. "Rotation as Contagion Mitigation," Management Science, INFORMS, vol. 67(5), pages 3117-3126, May.
    9. Ba Chu & Shafiullah Qureshi, 2020. "Predicting the COVID-19 pandemic in Canada and the US," Economics Bulletin, AccessEcon, vol. 40(3), pages 2565-2585.
    10. Richard M Wood & Christopher J McWilliams & Matthew J Thomas & Christopher P Bourdeaux & Christos Vasilakis, 2020. "COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care," Health Care Management Science, Springer, vol. 23(3), pages 315-324, September.
    11. G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1.
    12. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    13. Parbat, Debanjan & Chakraborty, Monisha, 2020. "A python based support vector regression model for prediction of COVID19 cases in India," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    14. Fernando Alvarez & David Argente & Francesco Lippi, 2021. "A Simple Planning Problem for COVID-19 Lock-down, Testing, and Tracing," American Economic Review: Insights, American Economic Association, vol. 3(3), pages 367-382, September.
    15. Jakob Heins & Jan Schoenfelder & Steffen Heider & Axel R. Heller & Jens O. Brunner, 2022. "A Scalable Forecasting Framework to Predict COVID-19 Hospital Bed Occupancy," Interfaces, INFORMS, vol. 52(6), pages 508-523, November.
    16. Seth Flaxman & Swapnil Mishra & Axel Gandy & H. Juliette T. Unwin & Thomas A. Mellan & Helen Coupland & Charles Whittaker & Harrison Zhu & Tresnia Berah & Jeffrey W. Eaton & Mélodie Monod & Azra C. Gh, 2020. "Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe," Nature, Nature, vol. 584(7820), pages 257-261, August.
    17. Ioannidis, John P.A. & Cripps, Sally & Tanner, Martin A., 2022. "Forecasting for COVID-19 has failed," International Journal of Forecasting, Elsevier, vol. 38(2), pages 423-438.
    18. Oliva, Rogelio, 2003. "Model calibration as a testing strategy for system dynamics models," European Journal of Operational Research, Elsevier, vol. 151(3), pages 552-568, December.
    19. Amy B. Gore & Mary E. Kurz & Matthew J. Saltzman & Blake Splitter & William C. Bridges & Neil J. Calkin, 2022. "Clemson University’s Rotational Attendance Plan During COVID-19," Interfaces, INFORMS, vol. 52(6), pages 553-567, November.
    Full references (including those not matched with items on IDEAS)

    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. Bradley S. Price & John P. Saldanha & Bernardo F. Quiroga & Sally L. Hodder, 2024. "Maintaining Healthcare Capacity in Rural America by Replenishing Personal Protective Equipment: The Case from West Virginia," Interfaces, INFORMS, vol. 54(6), pages 517-536, November.
    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. Ciamac C. Moallemi & Utkarsh Patange, 2024. "Hybrid Scheduling with Mixed-Integer Programming at Columbia Business School," Interfaces, INFORMS, vol. 54(3), pages 222-240, May.
    4. Brave, Scott A. & Gascon, Charles & Kluender, William & Walstrum, Thomas, 2021. "Predicting benchmarked US state employment data in real time," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1261-1275.
    5. Pelagatti, Matteo & Maranzano, Paolo, 2021. "Assessing the effectiveness of the Italian risk-zones policy during the second wave of COVID-19," Health Policy, Elsevier, vol. 125(9), pages 1188-1199.
    6. Hausmann, Ricardo & Schetter, Ulrich, 2022. "Horrible trade-offs in a pandemic: Poverty, fiscal space, policy, and welfare," World Development, Elsevier, vol. 153(C).
    7. Bennedsen, Mikkel & Hillebrand, Eric & Koopman, Siem Jan, 2021. "Modeling, forecasting, and nowcasting U.S. CO2 emissions using many macroeconomic predictors," Energy Economics, Elsevier, vol. 96(C).
    8. Giorgio Fabbri & Salvatore Federico & Davide Fiaschi & Fausto Gozzi, 2024. "Mobility decisions, economic dynamics and epidemic," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 77(1), pages 495-531, February.
    9. Phillip M. Yelland & Shinji Kim & Renée Stratulate, 2010. "A Bayesian Model for Sales Forecasting at Sun Microsystems," Interfaces, INFORMS, vol. 40(2), pages 118-129, April.
    10. Karlsson, Sune, 2013. "Forecasting with Bayesian Vector Autoregression," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 791-897, Elsevier.
    11. Salinas, David & Flunkert, Valentin & Gasthaus, Jan & Januschowski, Tim, 2020. "DeepAR: Probabilistic forecasting with autoregressive recurrent networks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1181-1191.
    12. Roy Cerqueti & Raffaella Coppier & Alessandro Girardi & Marco Ventura, 2022. "The sooner the better: lives saved by the lockdown during the COVID-19 outbreak. The case of Italy," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 46-70.
    13. Jos Jansen & Jasper de Winter, 2016. "Improving model-based near-term GDP forecasts by subjective forecasts: A real-time exercise for the G7 countries," DNB Working Papers 507, Netherlands Central Bank, Research Department.
    14. Alexopoulos, Thomas A., 2017. "The growing importance of natural gas as a predictor for retail electricity prices in US," Energy, Elsevier, vol. 137(C), pages 219-233.
    15. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    16. Peter McAdam & Anders Warne, 2024. "Density forecast combinations: The real‐time dimension," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1153-1172, August.
    17. Theodosiou, Marina, 2011. "Forecasting monthly and quarterly time series using STL decomposition," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1178-1195, October.
    18. Berta, P. & Bratti, M. & Fiorio, C.V. & Pisoni, E. & Verzillo, S., 2021. "Administrative border effects in Covid-19 related mortality," Health, Econometrics and Data Group (HEDG) Working Papers 21/21, HEDG, c/o Department of Economics, University of York.
    19. Sergio Contreras-Espinoza & Francisco Novoa-Muñoz & Szabolcs Blazsek & Pedro Vidal & Christian Caamaño-Carrillo, 2022. "COVID-19 Active Case Forecasts in Latin American Countries Using Score-Driven Models," Mathematics, MDPI, vol. 11(1), pages 1-17, December.
    20. Florian Peters & Simas Kucinskas, 2018. "Measuring Biases in Expectation Formation," Tinbergen Institute Discussion Papers 18-058/IV, Tinbergen Institute.

    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:inm:orinte:v:54:y:2024:i:6:p:500-516. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.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.