IDEAS home Printed from https://ideas.repec.org/a/bla/jorssa/v185y2022i1p84-101.html
   My bibliography  Save this article

An ensemble method for early prediction of dengue outbreak

Author

Listed:
  • Soudeep Deb
  • Sougata Deb

Abstract

Predicting a dengue outbreak well ahead of time is of immense importance to healthcare personnel. In this study, an ensemble method based on three different types of models has been developed. The proposed approach combines negative binomial regression, autoregressive integrated moving average model and generalized linear autoregressive moving average model through a vector autoregressive structure. Lagged values of terrain and climate covariates are used as regressors. Real‐life application using data from San Juan and Iquitos shows that the proposed method usually incurs a mean absolute error of less than 10 cases when the predictions are made 8 weeks in advance. Furthermore, using model confidence set procedure, it is also shown that the proposed method always outperforms other candidate models in providing early prediction for a dengue epidemic.

Suggested Citation

  • Soudeep Deb & Sougata Deb, 2022. "An ensemble method for early prediction of dengue outbreak," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 84-101, January.
    • Handle: RePEc:bla:jorssa:v:185:y:2022:i:1:p:84-101
    DOI: 10.1111/rssa.12714
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/rssa.12714
    Download Restriction: no
    File URL: https://libkey.io/10.1111/rssa.12714?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. Hamdan, Nur ’Izzati & Kilicman, Adem, 2018. "A fractional order SIR epidemic model for dengue transmission," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 55-62.
    2. Benjamin M.A. & Rigby R.A. & Stasinopoulos D.M., 2003. "Generalized Autoregressive Moving Average Models," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 214-223, January.
    3. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    4. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    5. Richard A. Davis, 2003. "Observation-driven models for Poisson counts," Biometrika, Biometrika Trust, vol. 90(4), pages 777-790, December.
    6. Tatiana Petukhova & Davor Ojkic & Beverly McEwen & Rob Deardon & Zvonimir Poljak, 2018. "Assessment of autoregressive integrated moving average (ARIMA), generalized linear autoregressive moving average (GLARMA), and random forest (RF) time series regression models for predicting influenza," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-17, June.
    7. Anna L Buczak & Benjamin Baugher & Linda J Moniz & Thomas Bagley & Steven M Babin & Erhan Guven, 2018. "Ensemble method for dengue prediction," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-23, January.
    8. Chakraborty, Tanujit & Chattopadhyay, Swarup & Ghosh, Indrajit, 2019. "Forecasting dengue epidemics using a hybrid methodology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    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. Raffaele Mattera & Philipp Otto, 2023. "Network log-ARCH models for forecasting stock market volatility," Papers 2303.11064, arXiv.org.
    2. Panja, Madhurima & Chakraborty, Tanujit & Nadim, Sk Shahid & Ghosh, Indrajit & Kumar, Uttam & Liu, Nan, 2023. "An ensemble neural network approach to forecast Dengue outbreak based on climatic condition," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).

    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. Billé, Anna Gloria & Gianfreda, Angelica & Del Grosso, Filippo & Ravazzolo, Francesco, 2023. "Forecasting electricity prices with expert, linear, and nonlinear models," International Journal of Forecasting, Elsevier, vol. 39(2), pages 570-586.
    2. Zhu, Qinwen & Diao, Xundi & Wu, Chongfeng, 2023. "Volatility forecast with the regularity modifications," Finance Research Letters, Elsevier, vol. 58(PA).
    3. Harvey,Andrew C., 2013. "Dynamic Models for Volatility and Heavy Tails," Cambridge Books, Cambridge University Press, number 9781107034723, September.
    4. Fokianos, Konstantinos & Rahbek, Anders & Tjøstheim, Dag, 2009. "Poisson Autoregression," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1430-1439.
    5. Zheng, Tingguo & Xiao, Han & Chen, Rong, 2015. "Generalized ARMA models with martingale difference errors," Journal of Econometrics, Elsevier, vol. 189(2), pages 492-506.
    6. Pedro H. C. Sant’Anna, 2017. "Testing for Uncorrelated Residuals in Dynamic Count Models With an Application to Corporate Bankruptcy," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(3), pages 349-358, July.
    7. Zheng, Tingguo & Chen, Rong, 2017. "Dirichlet ARMA models for compositional time series," Journal of Multivariate Analysis, Elsevier, vol. 158(C), pages 31-46.
    8. Tingguo Zheng & Han Xiao & Rong Chen, 2022. "Generalized autoregressive moving average models with GARCH errors," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(1), pages 125-146, January.
    9. Tianqing Liu & Xiaohui Yuan, 2013. "Random rounded integer-valued autoregressive conditional heteroskedastic process," Statistical Papers, Springer, vol. 54(3), pages 645-683, August.
    10. 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.
    11. Drew Creal & Siem Jan Koopman & André Lucas, 2008. "A General Framework for Observation Driven Time-Varying Parameter Models," Tinbergen Institute Discussion Papers 08-108/4, Tinbergen Institute.
    12. Vurukonda Sathish & Siuli Mukhopadhyay & Rashmi Tiwari, 2022. "Autoregressive and moving average models for zero‐inflated count time series," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 76(2), pages 190-218, May.
    13. Carlos Henrique Dias Cordeiro de Castro & Fernando Antonio Lucena Aiube, 2023. "Forecasting inflation time series using score‐driven dynamic models and combination methods: The case of Brazil," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 369-401, March.
    14. Panja, Madhurima & Chakraborty, Tanujit & Nadim, Sk Shahid & Ghosh, Indrajit & Kumar, Uttam & Liu, Nan, 2023. "An ensemble neural network approach to forecast Dengue outbreak based on climatic condition," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    15. Tingguo Zheng & Han Xiao & Rong Chen, 2021. "Generalized Autoregressive Moving Average Models with GARCH Errors," Papers 2105.05532, arXiv.org.
    16. Maia, Gisele de Oliveira & Barreto-Souza, Wagner & Bastos, Fernando de Souza & Ombao, Hernando, 2021. "Semiparametric time series models driven by latent factor," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1463-1479.
    17. Amaro, Raphael & Pinho, Carlos & Madaleno, Mara, 2022. "Forecasting the Value-at-Risk of energy commodities: A comparison of models and alternative distribution functions," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 65, pages 77-101.
    18. Yao Rao & David Harris & Brendan McCabe, 2022. "A semi‐parametric integer‐valued autoregressive model with covariates," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(3), pages 495-516, June.
    19. Wagner Barreto‐Souza & Hernando Ombao, 2022. "The negative binomial process: A tractable model with composite likelihood‐based inference," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(2), pages 568-592, June.
    20. Han, Chulwoo & Park, Frank C., 2022. "A geometric framework for covariance dynamics," Journal of Banking & Finance, Elsevier, vol. 134(C).

    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:bla:jorssa:v:185:y:2022:i:1:p:84-101. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.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.