Forecast of Dengue Cases in 20 Chinese Cities Based on the Deep Learning Method

Jiucheng Xu, Keqiang Xu, Zhichao Li, Fengxia Meng, Taotian Tu, Lei Xu and Qiyong Liu
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Jiucheng Xu: College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China
Keqiang Xu: College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China
Zhichao Li: Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
Fengxia Meng: State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
Taotian Tu: Institute of Disinfection and Vector Biological Control, Chongqing Center for Disease Control and Prevention, Chongqing 400042, China
Lei Xu: Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
Qiyong Liu: State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China

IJERPH, 2020, vol. 17, issue 2, 1-14

Abstract: Dengue fever (DF) is one of the most rapidly spreading diseases in the world, and accurate forecasts of dengue in a timely manner might help local government implement effective control measures. To obtain the accurate forecasting of DF cases, it is crucial to model the long-term dependency in time series data, which is difficult for a typical machine learning method. This study aimed to develop a timely accurate forecasting model of dengue based on long short-term memory (LSTM) recurrent neural networks while only considering monthly dengue cases and climate factors. The performance of LSTM models was compared with the other previously published models when predicting DF cases one month into the future. Our results showed that the LSTM model reduced the average the root mean squared error (RMSE) of the predictions by 12.99% to 24.91% and reduced the average RMSE of the predictions in the outbreak period by 15.09% to 26.82% as compared with other candidate models. The LSTM model achieved superior performance in predicting dengue cases as compared with other previously published forecasting models. Moreover, transfer learning (TL) can improve the generalization ability of the model in areas with fewer dengue incidences. The findings provide a more precise forecasting dengue model and could be used for other dengue-like infectious diseases.

Keywords: dengue fever; forecast model; long short-term memory; deep learning; transfer learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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