A novel model for malaria prediction based on ensemble algorithms

Autoři: Mengyang Wang aff001;  Hui Wang aff001;  Jiao Wang aff001;  Hongwei Liu aff001;  Rui Lu aff001;  Tongqing Duan aff001;  Xiaowen Gong aff001;  Siyuan Feng aff001;  Yuanyuan Liu aff001;  Zhuang Cui aff001;  Changping Li aff001;  Jun Ma aff001
Působiště autorů: Department of Health Statistics, College of Public Health, Tianjin Medical University, Heping District, Tianjin, P.R. China aff001
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0226910


Background and objective

Most previous studies adopted single traditional time series models to predict incidences of malaria. A single model cannot effectively capture all the properties of the data structure. However, a stacking architecture can solve this problem by combining distinct algorithms and models. This study compares the performance of traditional time series models and deep learning algorithms in malaria case prediction and explores the application value of stacking methods in the field of infectious disease prediction.


The ARIMA, STL+ARIMA, BP-ANN and LSTM network models were separately applied in simulations using malaria data and meteorological data in Yunnan Province from 2011 to 2017. We compared the predictive performance of each model through evaluation measures: RMSE, MASE, MAD. In addition, gradient-boosting regression trees (GBRTs) were used to combine the above four models. We also determined whether stacking structure improved the model prediction performance.


The root mean square errors (RMSEs) of the four sub-models were 13.176, 14.543, 9.571 and 7.208; the mean absolute scaled errors (MASEs) were 0.469, 0.472, 0.296 and 0.266 and the mean absolute deviation (MAD) were 6.403, 7.658, 5.871 and 5.691. After using the stacking architecture combined with the above four models, the RMSE, MASE and MAD values of the ensemble model decreased to 6.810, 0.224 and 4.625, respectively.


A novel ensemble model based on the robustness of structured prediction and model combination through stacking was developed. The findings suggest that the predictive performance of the final model is superior to that of the other four sub-models, indicating that stacking architecture may have significant implications in infectious disease prediction.

Klíčová slova:

Algorithms – Deep learning – China – Infectious diseases – Machine learning algorithms – Malaria – Neural networks


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