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

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  • Jiucheng Xu

    (College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China
    Engineering Technology Research Center for Computing Intelligence and Data Mining, Xinxiang 453007, China
    Engineering Lab of Intelligence Business & Internet of Things, Xinxiang 453007, China)

  • Keqiang Xu

    (College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China
    Engineering Lab of Intelligence Business & Internet of Things, Xinxiang 453007, China)

  • Zhichao Li

    (Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
    Center for Healthy Cities, Institute for China Sustainable Urbanization, 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
    Center for Healthy Cities, Institute for China Sustainable Urbanization, Tsinghua University, Beijing 100084, China
    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)

  • 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)

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.

Suggested Citation

  • Jiucheng Xu & Keqiang Xu & Zhichao Li & Fengxia Meng & Taotian Tu & Lei Xu & Qiyong Liu, 2020. "Forecast of Dengue Cases in 20 Chinese Cities Based on the Deep Learning Method," IJERPH, MDPI, vol. 17(2), pages 1-14, January.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:2:p:453-:d:307201
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    1. Samir Bhatt & Peter W. Gething & Oliver J. Brady & Jane P. Messina & Andrew W. Farlow & Catherine L. Moyes & John M. Drake & John S. Brownstein & Anne G. Hoen & Osman Sankoh & Monica F. Myers & Dylan , 2013. "The global distribution and burden of dengue," Nature, Nature, vol. 496(7446), pages 504-507, April.
    2. Anderson-Cook, Christine M., 2007. "Generalized Additive Models: An Introduction With R. Simon N. Wood," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 760-761, June.
    3. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    4. Sangwon Chae & Sungjun Kwon & Donghyun Lee, 2018. "Predicting Infectious Disease Using Deep Learning and Big Data," IJERPH, MDPI, vol. 15(8), pages 1-20, July.
    5. Xiaopeng Qi & Yong Wang & Yue Li & Yujie Meng & Qianqian Chen & Jiaqi Ma & George F Gao, 2015. "The Effects of Socioeconomic and Environmental Factors on the Incidence of Dengue Fever in the Pearl River Delta, China, 2013," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 9(10), pages 1-13, October.
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    Cited by:

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    2. 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.
    3. Zhao, Xinxing & Li, Kainan & Ang, Candice Ke En & Cheong, Kang Hao, 2023. "A deep learning based hybrid architecture for weekly dengue incidences forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    4. Vicente Navarro Valencia & Yamilka Díaz & Juan Miguel Pascale & Maciej F. Boni & Javier E. Sanchez-Galan, 2021. "Assessing the Effect of Climate Variables on the Incidence of Dengue Cases in the Metropolitan Region of Panama City," IJERPH, MDPI, vol. 18(22), pages 1-18, November.
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