IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i12p6563-d577284.html
   My bibliography  Save this article

Modeling Provincial Covid-19 Epidemic Data Using an Adjusted Time-Dependent SIRD Model

Author

Listed:
  • Luisa Ferrari

    (Department of Statistical Science, University College London, London WC1E 6BT, UK
    These authors contributed equally to this work.)

  • Giuseppe Gerardi

    (Department of Economics, Management and Quantitative Methods, University of Milan, 20122 Milan, Italy
    These authors contributed equally to this work.)

  • Giancarlo Manzi

    (Department of Economics, Management and Quantitative Methods and Data Science Research Center, University of Milan, 20122 Milan, Italy
    These authors contributed equally to this work.)

  • Alessandra Micheletti

    (Department of Environmental Science and Policy and Data Science Research Center, University of Milan, 20122 Milan, Italy
    These authors contributed equally to this work.)

  • Federica Nicolussi

    (Department of Economics, Management and Quantitative Methods and Data Science Research Center, University of Milan, 20122 Milan, Italy
    These authors contributed equally to this work.)

  • Elia Biganzoli

    (Department of Clinical Sciences and Community Health and Data Science Research Center, University of Milan, 20122 Milan, Italy
    These authors contributed equally to this work.)

  • Silvia Salini

    (Department of Economics, Management and Quantitative Methods and Data Science Research Center, University of Milan, 20122 Milan, Italy
    These authors contributed equally to this work.)

Abstract

In this paper, we develop a forecasting model for the spread of COVID-19 infection at a provincial (i.e., EU NUTS-3) level in Italy by using official data from the Italian Ministry of Health integrated with data extracted from daily official press conferences of regional authorities and local newspaper websites. This data integration is needed as COVID-19 death data are not available at the NUTS-3 level from official open data channels. An adjusted time-dependent SIRD model is used to predict the behavior of the epidemic; specifically, the number of susceptible, infected, deceased, recovered people and epidemiological parameters. Predictive model performance is evaluated using comparison with real data.

Suggested Citation

  • Luisa Ferrari & Giuseppe Gerardi & Giancarlo Manzi & Alessandra Micheletti & Federica Nicolussi & Elia Biganzoli & Silvia Salini, 2021. "Modeling Provincial Covid-19 Epidemic Data Using an Adjusted Time-Dependent SIRD Model," IJERPH, MDPI, vol. 18(12), pages 1-20, June.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:12:p:6563-:d:577284
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/12/6563/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/12/6563/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Thomas Hale & Noam Angrist & Rafael Goldszmidt & Beatriz Kira & Anna Petherick & Toby Phillips & Samuel Webster & Emily Cameron-Blake & Laura Hallas & Saptarshi Majumdar & Helen Tatlow, 2021. "A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker)," Nature Human Behaviour, Nature, vol. 5(4), pages 529-538, April.
    2. McKenzie, Jordi, 2011. "Mean absolute percentage error and bias in economic forecasting," Economics Letters, Elsevier, vol. 113(3), pages 259-262.
    3. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    4. C. A. Glasbey & D. J. Allcroft, 2008. "A spatiotemporal auto‐regressive moving average model for solar radiation," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 57(3), pages 343-355, June.
    5. Alessandra Micheletti & Giacomo Aletti & Giulia Ferrandi & Danilo Bertoni & Daniele Cavicchioli & Roberto Pretolani, 2020. "A weighted $$\chi ^2$$ χ 2 test to detect the presence of a major change point in non-stationary Markov chains," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(4), pages 899-912, December.
    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. Şuayip Yüzbaşı & Gamze Yıldırım, 2023. "A Pell–Lucas Collocation Approach for an SIR Model on the Spread of the Novel Coronavirus (SARS CoV-2) Pandemic: The Case of Turkey," Mathematics, MDPI, vol. 11(3), pages 1-22, January.

    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. Kim, Sungil & Kim, Heeyoung, 2016. "A new metric of absolute percentage error for intermittent demand forecasts," International Journal of Forecasting, Elsevier, vol. 32(3), pages 669-679.
    2. repec:prg:jnlcfu:v:2022:y:2022:i:1:id:572 is not listed on IDEAS
    3. Judit Temesvary & Andrew Wei, 2021. "Domestic Lending and the Pandemic: How Does Banks' Exposure to Covid-19 Abroad Affect Their Lending in the United States?," Finance and Economics Discussion Series 2021-056r1, Board of Governors of the Federal Reserve System (U.S.), revised 17 Nov 2021.
    4. Jacqueline Ruth & Steffen Willwacher & Oliver Korn, 2022. "Acceptance of Digital Sports: A Study Showing the Rising Acceptance of Digital Health Activities Due to the SARS-CoV-19 Pandemic," IJERPH, MDPI, vol. 19(1), pages 1-16, January.
    5. Hannah Carver & Tracey Price & Danilo Falzon & Peter McCulloch & Tessa Parkes, 2022. "Stress and Wellbeing during the COVID-19 Pandemic: A Mixed-Methods Exploration of Frontline Homelessness Services Staff Experiences in Scotland," IJERPH, MDPI, vol. 19(6), pages 1-20, March.
    6. Phurichai Rungcharoenkitkul, 2021. "Macroeconomic effects of COVID‐19: A mid‐term review," Pacific Economic Review, Wiley Blackwell, vol. 26(4), pages 439-458, October.
    7. Carlos Díaz & Sebastian Fossati & Nicolás Trajtenberg, 2022. "Stay at home if you can: COVID‐19 stay‐at‐home guidelines and local crime," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 19(4), pages 1067-1113, December.
    8. Chang, Andrew C. & Hanson, Tyler J., 2016. "The accuracy of forecasts prepared for the Federal Open Market Committee," Journal of Economics and Business, Elsevier, vol. 83(C), pages 23-43.
    9. Joël Cariolle & Florian Léon, 2022. "How internet helped firms to cope with COVID-19," Working Papers hal-03592617, HAL.
    10. Xiao Chen & Hanwei Huang & Jiandong Ju & Ruoyan Sun & Jialiang Zhang, 2022. "Endogenous cross-region human mobility and pandemics," CEP Discussion Papers dp1860, Centre for Economic Performance, LSE.
    11. Ling Tang & Chengyuan Zhang & Tingfei Li & Ling Li, 2021. "A novel BEMD-based method for forecasting tourist volume with search engine data," Tourism Economics, , vol. 27(5), pages 1015-1038, August.
    12. Alejandro G. Graziano & Yuan Tian, 2023. "Trade disruptions along the global supply chain," Discussion Papers 2023-06, University of Nottingham, GEP.
    13. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
    14. Khan, Nawab Ali & Azhar, Mohd & Rahman, Mohd Nayyer & Akhtar, Mohd Junaid, 2022. "Scale development and validation for usage of social networking sites during COVID-19," Technology in Society, Elsevier, vol. 70(C).
    15. Michael Vössing & Niklas Kühl & Matteo Lind & Gerhard Satzger, 2022. "Designing Transparency for Effective Human-AI Collaboration," Information Systems Frontiers, Springer, vol. 24(3), pages 877-895, June.
    16. Yekaterina Chzhen & Jennifer Symonds & Dympna Devine & Júlia Mikolai & Susan Harkness & Seaneen Sloan & Gabriela Martinez Sainz, 2022. "Learning in a Pandemic: Primary School children’s Emotional Engagement with Remote Schooling during the spring 2020 Covid-19 Lockdown in Ireland," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 15(4), pages 1517-1538, August.
    17. Frank, Johannes, 2023. "Forecasting realized volatility in turbulent times using temporal fusion transformers," FAU Discussion Papers in Economics 03/2023, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    18. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    19. Jeon, Yunho & Seong, Sihyeon, 2022. "Robust recurrent network model for intermittent time-series forecasting," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1415-1425.
    20. Snyder, Ralph D. & Ord, J. Keith & Koehler, Anne B. & McLaren, Keith R. & Beaumont, Adrian N., 2017. "Forecasting compositional time series: A state space approach," International Journal of Forecasting, Elsevier, vol. 33(2), pages 502-512.
    21. Paulo Júlio & Pedro M. Esperança, 2012. "Evaluating the forecast quality of GDP components: An application to G7," GEE Papers 0047, Gabinete de Estratégia e Estudos, Ministério da Economia, revised Apr 2012.

    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:gam:jijerp:v:18:y:2021:i:12:p:6563-:d:577284. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.