IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v140y2020ics0960077920306068.html
   My bibliography  Save this article

Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm

Author

Listed:
  • Yeşilkanat, Cafer Mert

Abstract

Novel Coronavirus pandemic, which negatively affected public health in social, psychological and economical terms, spread to the whole world in a short period of 6 months. However, the rate of increase in cases was not equal for every country. The measures implemented by the countries changed the daily spreading speed of the disease. This was determined by changes in the number of daily cases. In this study, the performance of the Random Forest (RF) machine learning algorithm was investigated in estimating the near future case numbers for 190 countries in the world and it is mapped in comparison with actual confirmed cases results. The number of confirmed cases between 23/01/2020 - 17/06/2020 were divided into 3 main sub-datasets: training sub-data, testing sub-data (interpolation data) and estimating sub-data (extrapolation data) for the random forest model. At the end of the study, it has been found that R2 values for testing sub-data of RF model estimates range between 0.843 and 0.995 (average R2= 0.959), and RMSE values between 141.76 and 526.18 (mean RMSE = 259.38); and that R2 values for estimating sub-data range between 0.690 and 0.968 (mean R2 = 0.914), and RMSE values between 549.73 and 2500.79 (mean RMSE = 909.37). These results show that the random forest machine learning algorithm performs well in estimating the number of cases for the near future in case of an epidemic like Novel Coronavirus, which outbreaks suddenly and spreads rapidly.

Suggested Citation

  • Yeşilkanat, Cafer Mert, 2020. "Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    • Handle: RePEc:eee:chsofr:v:140:y:2020:i:c:s0960077920306068
    DOI: 10.1016/j.chaos.2020.110210
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077920306068
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2020.110210?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. Hodgkinson, Tarah & Andresen, Martin A., 2020. "Show me a man or a woman alone and I'll show you a saint: Changes in the frequency of criminal incidents during the COVID-19 pandemic," Journal of Criminal Justice, Elsevier, vol. 69(C).
    2. Postnikov, Eugene B., 2020. "Estimation of COVID-19 dynamics “on a back-of-envelope”: Does the simplest SIR model provide quantitative parameters and predictions?," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    3. Barmparis, G.D. & Tsironis, G.P., 2020. "Estimating the infection horizon of COVID-19 in eight countries with a data-driven approach," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    4. Singh, Sarbjit & Parmar, Kulwinder Singh & Kumar, Jatinder & Makkhan, Sidhu Jitendra Singh, 2020. "Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    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. Milad Haghani & Michiel C. J. Bliemer, 2020. "Covid-19 pandemic and the unprecedented mobilisation of scholarly efforts prompted by a health crisis: Scientometric comparisons across SARS, MERS and 2019-nCoV literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2695-2726, December.
    2. da Silva, Ramon Gomes & Ribeiro, Matheus Henrique Dal Molin & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2020. "Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    3. Mudassar Arsalan & Omar Mubin & Fady Alnajjar & Belal Alsinglawi, 2020. "COVID-19 Global Risk: Expectation vs. Reality," IJERPH, MDPI, vol. 17(15), pages 1-10, August.
    4. 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.
    5. Méndez-Gordillo, Alma Rosa & Cadenas, Erasmo, 2021. "Wind speed forecasting by the extraction of the multifractal patterns of time series through the multiplicative cascade technique," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    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. Lydia Cheung & Philip Gunby, 2023. "The Initial and Dynamic Effects of the COVID-19 Pandemic on Crime in New Zealand," Working Papers in Economics 23/03, University of Canterbury, Department of Economics and Finance.
    9. Khan, Syed Abdul Rehman & Razzaq, Asif & Yu, Zhang & Shah, Adeel & Sharif, Arshian & Janjua, Laeeq, 2022. "Disruption in food supply chain and undernourishment challenges: An empirical study in the context of Asian countries," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
    10. Fokas, A.S. & Cuevas-Maraver, J. & Kevrekidis, P.G., 2020. "A quantitative framework for exploring exit strategies from the COVID-19 lockdown," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    11. Balmori de la Miyar, Jose Roberto & Hoehn-Velasco, Lauren & Silverio-Murillo, Adan, 2021. "Druglords don’t stay at home: COVID-19 pandemic and crime patterns in Mexico City," Journal of Criminal Justice, Elsevier, vol. 72(C).
    12. Daniele Proverbio & Françoise Kemp & Stefano Magni & Andreas Husch & Atte Aalto & Laurent Mombaerts & Alexander Skupin & Jorge Gonçalves & Jose Ameijeiras-Alonso & Christophe Ley, 2021. "Dynamical SPQEIR model assesses the effectiveness of non-pharmaceutical interventions against COVID-19 epidemic outbreaks," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-21, May.
    13. Koutsellis, Themistoklis & Nikas, Alexandros, 2020. "A predictive model and country risk assessment for COVID-19: An application of the Limited Failure Population concept," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    14. Bagal, Dilip Kumar & Rath, Arati & Barua, Abhishek & Patnaik, Dulu, 2020. "Estimating the parameters of susceptible-infected-recovered model of COVID-19 cases in India during lockdown periods," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    15. Gaetano Perone, 2022. "Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(6), pages 917-940, August.
    16. Iván Area & Henrique Lorenzo & Pedro J. Marcos & Juan J. Nieto, 2021. "One Year of the COVID-19 Pandemic in Galicia: A Global View of Age-Group Statistics during Three Waves," IJERPH, MDPI, vol. 18(10), pages 1-14, May.
    17. Singh, Sarbjit & Parmar, Kulwinder Singh & Makkhan, Sidhu Jitendra Singh & Kaur, Jatinder & Peshoria, Shruti & Kumar, Jatinder, 2020. "Study of ARIMA and least square support vector machine (LS-SVM) models for the prediction of SARS-CoV-2 confirmed cases in the most affected countries," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    18. Yichen Shen & Rong Fu & Haruko Noguchi, 2021. "COVID‐19's Lockdown and Crime Victimization: The State of Emergency under the Abe Administration," Asian Economic Policy Review, Japan Center for Economic Research, vol. 16(2), pages 327-348, July.
    19. Batistela, Cristiane M. & Correa, Diego P.F. & Bueno, Átila M & Piqueira, José Roberto C., 2021. "SIRSi compartmental model for COVID-19 pandemic with immunity loss," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    20. Charu Arora & Poras Khetarpal & Saket Gupta & Nuzhat Fatema & Hasmat Malik & Asyraf Afthanorhan, 2023. "Mathematical Modelling to Predict the Effect of Vaccination on Delay and Rise of COVID-19 Cases Management," Mathematics, MDPI, vol. 11(4), pages 1-15, February.

    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:eee:chsofr:v:140:y:2020:i:c:s0960077920306068. 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: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    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.