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Ensemble method for dengue prediction

Author

Listed:
  • Anna L Buczak
  • Benjamin Baugher
  • Linda J Moniz
  • Thomas Bagley
  • Steven M Babin
  • Erhan Guven

Abstract

Background: In the 2015 NOAA Dengue Challenge, participants made three dengue target predictions for two locations (Iquitos, Peru, and San Juan, Puerto Rico) during four dengue seasons: 1) peak height (i.e., maximum weekly number of cases during a transmission season; 2) peak week (i.e., week in which the maximum weekly number of cases occurred); and 3) total number of cases reported during a transmission season. A dengue transmission season is the 12-month period commencing with the location-specific, historical week with the lowest number of cases. At the beginning of the Dengue Challenge, participants were provided with the same input data for developing the models, with the prediction testing data provided at a later date. Methods: Our approach used ensemble models created by combining three disparate types of component models: 1) two-dimensional Method of Analogues models incorporating both dengue and climate data; 2) additive seasonal Holt-Winters models with and without wavelet smoothing; and 3) simple historical models. Of the individual component models created, those with the best performance on the prior four years of data were incorporated into the ensemble models. There were separate ensembles for predicting each of the three targets at each of the two locations. Principal findings: Our ensemble models scored higher for peak height and total dengue case counts reported in a transmission season for Iquitos than all other models submitted to the Dengue Challenge. However, the ensemble models did not do nearly as well when predicting the peak week. Conclusions: The Dengue Challenge organizers scored the dengue predictions of the Challenge participant groups. Our ensemble approach was the best in predicting the total number of dengue cases reported for transmission season and peak height for Iquitos, Peru.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pone00:0189988
    DOI: 10.1371/journal.pone.0189988
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    Cited by:

    1. Zhichao Li, 2022. "Forecasting Weekly Dengue Cases by Integrating Google Earth Engine-Based Risk Predictor Generation and Google Colab-Based Deep Learning Modeling in Fortaleza and the Federal District, Brazil," IJERPH, MDPI, vol. 19(20), pages 1-16, October.
    2. Prashant Rangarajan & Sandeep K Mody & Madhav Marathe, 2019. "Forecasting dengue and influenza incidences using a sparse representation of Google trends, electronic health records, and time series data," PLOS Computational Biology, Public Library of Science, vol. 15(11), pages 1-24, November.
    3. 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).
    4. Yanhui Guo & Yi Feng & Fuli Qu & Li Zhang & Bingyu Yan & Jingjing Lv, 2020. "Prediction of hepatitis E using machine learning models," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-12, September.
    5. 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.

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