Forecast of Dengue Cases in 20 Chinese Cities Based on the Deep Learning Method
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...
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doaj-54a50dc99b084667b20b32a94f6691c62020-11-25T01:33:22ZengMDPI AGInternational Journal of Environmental Research and Public Health1660-46012020-01-0117245310.3390/ijerph17020453ijerph17020453Forecast of Dengue Cases in 20 Chinese Cities Based on the Deep Learning MethodJiucheng Xu0Keqiang Xu1Zhichao Li2Fengxia Meng3Taotian Tu4Lei Xu5Qiyong Liu6College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, ChinaCollege of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, ChinaMinistry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, ChinaState 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, ChinaInstitute of Disinfection and Vector Biological Control, Chongqing Center for Disease Control and Prevention, Chongqing 400042, ChinaMinistry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, ChinaState 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, ChinaDengue 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.https://www.mdpi.com/1660-4601/17/2/453dengue feverforecast modellong short-term memorydeep learningtransfer learning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jiucheng Xu Keqiang Xu Zhichao Li Fengxia Meng Taotian Tu Lei Xu Qiyong Liu |
spellingShingle |
Jiucheng Xu Keqiang Xu Zhichao Li Fengxia Meng Taotian Tu Lei Xu Qiyong Liu Forecast of Dengue Cases in 20 Chinese Cities Based on the Deep Learning Method International Journal of Environmental Research and Public Health dengue fever forecast model long short-term memory deep learning transfer learning |
author_facet |
Jiucheng Xu Keqiang Xu Zhichao Li Fengxia Meng Taotian Tu Lei Xu Qiyong Liu |
author_sort |
Jiucheng Xu |
title |
Forecast of Dengue Cases in 20 Chinese Cities Based on the Deep Learning Method |
title_short |
Forecast of Dengue Cases in 20 Chinese Cities Based on the Deep Learning Method |
title_full |
Forecast of Dengue Cases in 20 Chinese Cities Based on the Deep Learning Method |
title_fullStr |
Forecast of Dengue Cases in 20 Chinese Cities Based on the Deep Learning Method |
title_full_unstemmed |
Forecast of Dengue Cases in 20 Chinese Cities Based on the Deep Learning Method |
title_sort |
forecast of dengue cases in 20 chinese cities based on the deep learning method |
publisher |
MDPI AG |
series |
International Journal of Environmental Research and Public Health |
issn |
1660-4601 |
publishDate |
2020-01-01 |
description |
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. |
topic |
dengue fever forecast model long short-term memory deep learning transfer learning |
url |
https://www.mdpi.com/1660-4601/17/2/453 |
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