IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0240578.html
   My bibliography  Save this article

On the use of growth models to understand epidemic outbreaks with application to COVID-19 data

Author

Listed:
  • Chénangnon Frédéric Tovissodé
  • Bruno Enagnon Lokonon
  • Romain Glèlè Kakaï

Abstract

The initial phase dynamics of an epidemic without containment measures is commonly well modelled using exponential growth models. However, in the presence of containment measures, the exponential model becomes less appropriate. Under the implementation of an isolation measure for detected infectives, we propose to model epidemic dynamics by fitting a flexible growth model curve to reported positive cases, and to infer the overall epidemic dynamics by introducing information on the detection/testing effort and recovery and death rates. The resulting modelling approach is close to the Susceptible-Infectious-Quarantined-Recovered model framework. We focused on predicting the peaks (time and size) in positive cases, active cases and new infections. We applied the approach to data from the COVID-19 outbreak in Italy. Fits on limited data before the observed peaks illustrate the ability of the flexible growth model to approach the estimates from the whole data.

Suggested Citation

  • Chénangnon Frédéric Tovissodé & Bruno Enagnon Lokonon & Romain Glèlè Kakaï, 2020. "On the use of growth models to understand epidemic outbreaks with application to COVID-19 data," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-14, October.
    • Handle: RePEc:plo:pone00:0240578
    DOI: 10.1371/journal.pone.0240578
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0240578
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0240578&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0240578?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. Păcurar, Cristina-Maria & Necula, Bogdan-Radu, 2020. "An analysis of COVID-19 spread based on fractal interpolation and fractal dimension," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    2. Arianna Agosto & Paolo Giudici, 2020. "A Poisson Autoregressive Model to Understand COVID-19 Contagion Dynamics," Risks, MDPI, vol. 8(3), pages 1-8, July.
    3. Kosmas Kosmidis & Panos Macheras, 2020. "A fractal kinetics SI model can explain the dynamics of COVID-19 epidemics," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-9, August.
    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. Adam Goliński & Peter Spencer, 2021. "Modeling the Covid‐19 epidemic using time series econometrics," Health Economics, John Wiley & Sons, Ltd., vol. 30(11), pages 2808-2828, November.
    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. Antonio Barrera & Patricia Román-Román & Juan José Serrano-Pérez & Francisco Torres-Ruiz, 2021. "Two Multi-Sigmoidal Diffusion Models for the Study of the Evolution of the COVID-19 Pandemic," Mathematics, MDPI, vol. 9(19), pages 1-29, September.

    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. Javier Cifuentes-Faura & Ursula Faura-Martínez & Matilde Lafuente-Lechuga, 2022. "Mathematical Modeling and the Use of Network Models as Epidemiological Tools," Mathematics, MDPI, vol. 10(18), pages 1-14, September.
    2. Verma, Shubham Kumar & Kumar, Satish, 2024. "Fractal dimension analysis of financial performance of resulting companies after mergers and acquisitions," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    3. Guglielmo Maria Caporale & Woo-Young Kang & Fabio Spagnolo & Nicola Spagnolo, 2022. "The COVID-19 pandemic, policy responses and stock markets in the G20," International Economics, CEPII research center, issue 172, pages 77-90.
    4. František Božek & Irena Tušer, 2021. "Measures for Ensuring Sustainability during the Current Spreading of Coronaviruses in the Czech Republic," Sustainability, MDPI, vol. 13(12), pages 1-22, June.
    5. Shanshan Wan & Zhuo Chen & Cheng Lyu & Ruofan Li & Yuntao Yue & Ying Liu, 2022. "Research on disaster information dissemination based on social sensor networks," International Journal of Distributed Sensor Networks, , vol. 18(3), pages 15501329221, March.
    6. Cooper, Ian & Mondal, Argha & Antonopoulos, Chris G., 2020. "Dynamic tracking with model-based forecasting for the spread of the COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    7. Cooper, Ian & Mondal, Argha & Antonopoulos, Chris G., 2020. "A SIR model assumption for the spread of COVID-19 in different communities," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    8. 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.
    9. Pau Fonseca i Casas & Joan Garcia i Subirana & Víctor García i Carrasco & Xavier Pi i Palomés, 2021. "SARS-CoV-2 Spread Forecast Dynamic Model Validation through Digital Twin Approach, Catalonia Case Study," Mathematics, MDPI, vol. 9(14), pages 1-17, July.
    10. Girardi, Alessandro & Ventura, Marco, 2023. "The cost of waiting and the death toll in Italy during the first wave of the covid-19 pandemic," Health Policy, Elsevier, vol. 134(C).
    11. Song, Jialu & Xie, Hujin & Gao, Bingbing & Zhong, Yongmin & Gu, Chengfan & Choi, Kup-Sze, 2021. "Maximum likelihood-based extended Kalman filter for COVID-19 prediction," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    12. Ruz, Soumendra Nath, 2023. "Amazing aspects of inequality indices (Gini and Kolkata Index) of COVID-19 confirmed cases in India," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P2).
    13. Li, Shaoran & Linton, Oliver, 2021. "When will the Covid-19 pandemic peak?," Journal of Econometrics, Elsevier, vol. 220(1), pages 130-157.
    14. Mati, Sagiru, 2021. "Do as your neighbours do? Assessing the impact of lockdown and reopening on the active COVID-19 cases in Nigeria," Social Science & Medicine, Elsevier, vol. 270(C).
    15. Memon, Zaibunnisa & Qureshi, Sania & Memon, Bisharat Rasool, 2021. "Assessing the role of quarantine and isolation as control strategies for COVID-19 outbreak: A case study," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    16. Bhardwaj, Rashmi & Bangia, Aashima, 2020. "Data driven estimation of novel COVID-19 transmission risks through hybrid soft-computing techniques," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    17. Dorn, Florian & Lange, Berit & Braml, Martin & Gstrein, David & Nyirenda, John L.Z. & Vanella, Patrizio & Winter, Joachim & Fuest, Clemens & Krause, Gérard, 2023. "The challenge of estimating the direct and indirect effects of COVID-19 interventions – Toward an integrated economic and epidemiological approach," Economics & Human Biology, Elsevier, vol. 49(C).
    18. Huang, Chiou-Jye & Shen, Yamin & Kuo, Ping-Huan & Chen, Yung-Hsiang, 2022. "Novel spatiotemporal feature extraction parallel deep neural network for forecasting confirmed cases of coronavirus disease 2019," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    19. Musa Ganaka Kubi & Son-Allah Mallaka Philemon & Olope Ganiu Ibrahim, 2020. "Forecasting the Confirmed Cases of COVID-19 in Selected West African Countries Using ARIMA Model Technique," International Journal of Research and Innovation in Applied Science, International Journal of Research and Innovation in Applied Science (IJRIAS), vol. 5(8), pages 141-144, August.
    20. Umar Albalawi & Mohammed Mustafa, 2022. "Current Artificial Intelligence (AI) Techniques, Challenges, and Approaches in Controlling and Fighting COVID-19: A Review," IJERPH, MDPI, vol. 19(10), pages 1-24, May.

    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:pone00:0240578. 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.