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Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia

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Listed:
  • Naizhuo Zhao
  • Katia Charland
  • Mabel Carabali
  • Elaine O Nsoesie
  • Mathieu Maheu-Giroux
  • Erin Rees
  • Mengru Yuan
  • Cesar Garcia Balaguera
  • Gloria Jaramillo Ramirez
  • Kate Zinszer

Abstract

The robust estimate and forecast capability of random forests (RF) has been widely recognized, however this ensemble machine learning method has not been widely used in mosquito-borne disease forecasting. In this study, two sets of RF models were developed at the national (pooled department-level data) and department level in Colombia to predict weekly dengue cases for 12-weeks ahead. A pooled national model based on artificial neural networks (ANN) was also developed and used as a comparator to the RF models. The various predictors included historic dengue cases, satellite-derived estimates for vegetation, precipitation, and air temperature, as well as population counts, income inequality, and education. Our RF model trained on the pooled national data was more accurate for department-specific weekly dengue cases estimation compared to a local model trained only on the department’s data. Additionally, the forecast errors of the national RF model were smaller to those of the national pooled ANN model and were increased with the forecast horizon increasing from one-week-ahead (mean absolute error, MAE: 9.32) to 12-weeks ahead (MAE: 24.56). There was considerable variation in the relative importance of predictors dependent on forecast horizon. The environmental and meteorological predictors were relatively important for short-term dengue forecast horizons while socio-demographic predictors were relevant for longer-term forecast horizons. This study demonstrates the potential of RF in dengue forecasting with a feasible approach of using a national pooled model to forecast at finer spatial scales. Furthermore, including sociodemographic predictors is likely to be helpful in capturing longer-term dengue trends.Author summary: Dengue virus has the highest disease burden of all mosquito-borne viral diseases, infecting 390 million people annually in 128 countries. Forecasting is an important warning mechanism that can help with proactive planning and response for clinical and public health services. In this study, we compare two different machine learning approaches to dengue forecasting: random forest (RF) and artificial neural networks (ANN). National (pooling across all departments) and local (department-specific) models were compared and used to predict future dengue cases in Colombia. In Colombia, the departments are administrative divisions formed by a grouping of municipalities. The results demonstrated that the counts of future dengue cases were more accurately estimated by RF than by ANN. It was also shown that environmental and meteorological predictors were more important for forecast accuracy for shorter-term forecasts while socio-demographic predictors were more important for longer-term forecasts. Finally, the national pooled model applied to local data was more accurate in dengue forecasting compared to the department-specific model. This research contributes to the field of disease forecasting and highlights different considerations for future forecasting studies.

Suggested Citation

  • Naizhuo Zhao & Katia Charland & Mabel Carabali & Elaine O Nsoesie & Mathieu Maheu-Giroux & Erin Rees & Mengru Yuan & Cesar Garcia Balaguera & Gloria Jaramillo Ramirez & Kate Zinszer, 2020. "Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 14(9), pages 1-16, September.
  • Handle: RePEc:plo:pntd00:0008056
    DOI: 10.1371/journal.pntd.0008056
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    References listed on IDEAS

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    1. Shalini Gambhir & Sanjay Kumar Malik & Yugal Kumar, 2018. "The Diagnosis of Dengue Disease: An Evaluation of Three Machine Learning Approaches," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 13(3), pages 1-19, July.
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    Cited by:

    1. Mokhalad A. Majeed & Helmi Zulhaidi Mohd Shafri & Zed Zulkafli & Aimrun Wayayok, 2023. "A Deep Learning Approach for Dengue Fever Prediction in Malaysia Using LSTM with Spatial Attention," IJERPH, MDPI, vol. 20(5), pages 1-22, February.
    2. Villi Dane M. Go, 2023. "Communicable disease surveillance through predictive analysis: A comparative analysis of prediction models," HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE - ENGINEERING AND TECHNOLOGY, HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE, HO CHI MINH CITY OPEN UNIVERSITY, vol. 13(2), pages 45-54.
    3. 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.
    4. 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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