Abstract
Background and objectiveMost 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.MethodsThe 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.ResultsThe 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.ConclusionsA 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.
Highlights
Malaria is an acute parasitic infection caused by plasmodium, which is mainly transmitted through mosquitoes
The autoregressive integrated moving average (ARIMA), STL+ARIMA, backpropagation artificial neural network (BP-artificial neural network (ANN)) and long short-term memory network (LSTM) network models were separately applied in simulations using malaria data and meteorological data in Yunnan Province from 2011 to 2017
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
Summary
Malaria is an acute parasitic infection caused by plasmodium, which is mainly transmitted through mosquitoes. Many countries have made remarkable progress towards eliminating malaria, the illness remains a serious public health issue, with an estimated 219 million cases globally in 2017[1]. The World Health Organization (WHO) launched the WHO global technical strategy for malaria from 2016–2030 to accelerate progress towards eliminating malaria [2]. In the early 1970s, malaria was still one of the most common infectious diseases, with 2.4 million cases in mainland China. Most previous studies adopted single traditional time series models to predict incidences of malaria. 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
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