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An ensemble method for early prediction of dengue outbreak

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  • 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
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    References listed on IDEAS

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    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).
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    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).

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