Journal of Shandong University (Health Sciences) ›› 2022, Vol. 60 ›› Issue (2): 96-101.doi: 10.6040/j.issn.1671-7554.0.2021.0707

Previous Articles     Next Articles

Comparison of prediction effects of DLNM and LSTM neural network on the incidence of hand, foot and mouth disease in Linyi City

FENG Yiping1,2, SUN Dapeng3, WANG Xianjun3, JI Yiman1,2, LIU Yunxia1,2   

  1. 1. Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong, China;
    2. Institute for Medical Research, Cheeloo College of Medical, Shandong University, Jinan 250012, Shandong, China;
    3. Shandong Center for Disease Control and Prevention, Jinan 250014, Shandong, China
  • Published:2022-01-25

Abstract: Objective To analyze and predict the incidence trend of hand, foot and mouth disease(HFMD)in Linyi City, Shandang Province by using the distributed lag non-linear model(DLNM)and long-short term memory(LSTM)neural network, and to provide reference for effective prevention and control of the disease. Methods The daily incidence data from Jan. 1, 2011 to Dec. 31, 2015 were collected to establish the DLNM and LSTM neural network, respectively. The daily incidence data from Jan. 1, 2016 to Dec. 31, 2017 were used to test and compare the prediction effects of the two models. Results A total of 25,999 HFMD cases were reported during Jan. 1, 2011 to Dec. 31, 2017. The root mean square error(RMSE)of DLNM and LSTM neural network extrapolation prediction from Jan. 1, 2016 to Dec. 31, 2017 were 11.93 and 5.74, respectively, and the mean absolute deviation(MAE)were 7.93 and 3.60, respectively, indicating the prediction accuracy of LSTM was better than that of DLNM, and the prediction results were basically consistent with the actual situation. Conclusion LSTM neural network has a good fitting and prediction effect on the incidence trend of HFMD in Linyi City, which can provide guidance for the prediction and warning of the disease.

Key words: Hand, foot and mouth disease, Prediction, Distributed lag non-linear model, Long-short term memory neural network

CLC Number: 

  • R181.3
[1] Guerra AM, Orille E, Waseem M. Hand foot and mouth disease [DB/OL].(2021-09-20)[2021-04-27]. https://www.ncbi.nlm.nih.gov/books/NBK431082/.
[2] 崔金朝, 聂陶然, 任敏睿, 等. 2008-2018年中国5岁及以下儿童手足口病死亡病例流行病学特征[J]. 中华流行病学杂志, 2020, 41(7): 1041-1046. CUI Jinchao, NIE Taoran, REN Minrui, et al. Epidemiological charact-eristics of fatal cases of hand, foot, and mouth disease in children under 5 years old in China, 2008-2018 [J]. Chinese Journal of Epidemiology, 2020, 41(7): 1041-1046.
[3] 《手足口病诊疗指南(2018版)》编写专家委员会. 手足口病诊疗指南(2018年版)[J].中华传染病杂志,2018, 36(5): 257-263. Expert Committee for Guidelines for the diagnosis and treatment of hand, foot, and mouth disease. Guidelines for the diagnosis and treatment of hand, foot, and mouth disease(2018 Edition)[J]. Chinese Journal of Infectious Diseases, 2018, 36(5): 257-263.
[4] He X, Zhang M, Zhao C, et al. From monovalent to multivalent vaccines, the exploration for potential preventive strategies against hand, foot, and mouth disease(HFMD)[J]. Virol Sin, 2021, 36(2): 167-175.
[5] Fang CY, Liu CC. Recent development of Enterovirus a vaccine candidates for the prevention of hand, foot, and mouth disease [J]. Expert Rev Vaccines, 2018, 17(9): 819-831.
[6] Liu Z, Meng Y, Xiang H, et al. Association of short-term exposure to meteorological factors and risk of hand, foot, and mouth disease: a systematic review and meta-analysis [J]. Int J Environ Res Public Health, 2020, 17(21): 8017.
[7] Wu X, Hu S, Kwaku AB, et al. Spatio-temporal clustering analysis and its determinants of hand, foot and mouth disease in Hunan, China, 2009-2015 [J]. BMC Infect Dis, 2017, 17(1): 645.
[8] Gasparrini A. Distributed lag linear and non-linear models in R: the package dlnm [J]. J Stat Softw, 2011, 43(8): 1-20.
[9] Gasparrini A, Armstrong B, Kenward MG. Multivariate meta-analysis for non-linear and other multi-parameter associations [J]. Stat Med, 2012, 31(29): 3821-3839.
[10] Cheng Q, Bai L, Zhang Y, et al. Ambient temperature, humidity and hand, foot, and mouth disease: a systematic review and meta-analysis [J]. Sci Total Environ, 2018, 625: 828-836. doi: 10.1016/j.scitotenv.2018.01.006.
[11] Chen S, Liu X, Wu Y, et al. The application of meteorological data and search index data in improving the prediction of HFMD: a study of two cities in Guangdong Province, China [J]. Sci Total Environ, 2019, 652: 1013-1021. doi: 10.1016/j.scitotenv.2018.10.304.
[12] Gu S, Huang R, Yang J, et al. Exposure-lag-response association between sunlight and schizophrenia in Ningbo, China [J]. Environ Pollut, 2019, 247: 285-292. doi: 10.1016/j.envpol.2018.12.023.
[13] Gu J, Liang L, Song H, et al. A method for hand-foot-mouth disease prediction using GeoDetector and LSTM model in Guangxi, China [J]. Sci Rep, 2019, 9(1): 17928. doi:10.1038/s41598-019-54495-2.
[14] Xu X, Yoneda M. Multitask air-quality prediction based on LSTM-autoencoder model [J]. IEEE Trans Cybern, 2021, 51(5): 2577-2586.
[15] Rahman M, Islam D, Mukti RJ, et al. A deep learning approach based on convolutional LSTM for detecting diabetes [J]. Comput Biol Chem, 2020, 88: 107329. doi: 10.1016/j.compbiolchem.2020.107329.
[16] Liao J, Qin Z, Zuo Z, et al. Spatial-temporal mapping of hand foot and mouth disease and the long-term effects associated with climate and socio-economic variables in Sichuan Province, China from 2009 to 2013 [J]. Sci Total Environ, 2016, 563/564: 152-159. doi: 10.1016/j.scitotenv.2016.03.159.
[17] Yang F, Ma Y, Liu FF, et al. Short-term effects of rainfall on childhood hand, foot and mouth disease and related spatial heterogeneity: evidence from 143 cities in mainland China [J]. BMC Public Heal, 2020, 20(1): 1528.
[18] 祁海萍. 2009-2013年甘肃省三市手足口病报告病例流行特征与趋势分析[D]. 兰州: 兰州大学, 2015.
[19] 刘涛, 王显军, 姜宝法, 等. SARIMA模型预测山东省手足口病发病趋势[J]. 中国卫生统计, 2013, 30(5): 697-700. LIU Tao, WANG Xianjun, JIANG Baofa, et al. Prediction of hand-foot-mouth disease incidence using SARIMA model in Shandong Province [J]. Chinese Journal of Health Statistics, 2013, 30(5): 697-700.
[20] 党德建, 朱杰辉, 张超, 等. 炎症因子在手足口病重症和重症天数判断中的应用[J]. 现代预防医学, 2017, 44(20): 3817-3821. DANG Dejian, ZHU Jiehui, ZHANG Chao, et al. Application of the inflammatory factor in determination of severe hand-foot-and-mouth disease cases and duration [J]. Modern Preventive Medicine, 2017, 44(20): 3817-3821.
[21] 马晓梅, 徐学琴, 闫国立, 等. BP神经网络和决策树分析在重症手足口病临床早期预警指标中的应用[J]. 中国卫生统计, 2019, 36(3): 381-383.
[22] 段清浩,李廷荣,杨连建.重庆市沙坪坝区手足口病发病数预测模型的构建与评价[J]. 西南国防医药, 2018, 28(10): 979-981. DUAN Qinghao, LI Tingrong, YANG Lianjian. Construction and evaluation of prediction model of hand foot and mouth disease incidence in Shapingba District of Chongqing [J]. Medical Journal of National Defending Forces in Southwest China, 2018, 28(10): 979-981.
[23] Xiang J, Hansen A, Liu Q, et al. Association between dengue fever incidence and meteorological factors in Guangzhou, China, 2005-2014 [J]. Environ Res, 2017, 153: 17-26. doi: 10.1016/j.envres.2016.11.009.
[24] Gao Y, Niu Y, Sun W, et al. Climate factors driven typhus group rickettsiosis incidence dynamics in Xishuangbanna Dai autonomous prefecture of Yunnan Province in China, 2005-2017 [J]. Environ Health, 2020, 19(1): 3.
[25] Pathan RK, Biswas M, Khandaker MU. Time series prediction of COVID-19 by mutation rate analysis using recurrent neural network-based LSTM model [J]. Chaos Solitons Fractals, 2020, 138: 110018. doi: 10.1016/j.chaos.2020.110018.
[26] Guo Y, Feng Y, Qu F, et al. Prediction of hepatitis E using machine learning models [J]. PLoS One, 2020, 15(9): e0237750. doi: 10.1371/journal.pone.0237750.
[27] 祝寒松, 陈思, 王明斋, 等. 厦门市2013-2017年手足口病发病与气象因素影响分析[J]. 中华流行病学杂志, 2019, 40(5): 531-536. ZHU Hansong, CHEN Si, WANG Mingzhai, et al. Analysis on association between incidence of hand foot and mouth disease and meteorological factors in Xiamen, 2013-2017 [J]. Chinese Journal of Epidemiology, 2019, 40(5): 531-536. doi: 10.3760/cma.j.issn.0254-6450.2019.05.008.
[28] Lu J, Bu P, Xia X, et al. A new deep learning algorithm for detecting the lag effect of fine particles on hospital emergency visits for respiratory diseases [J]. IEEE Access, 2020, 8: 145593-600. doi:https://doi.org/10.1109/ACCESS.2020.3013543.
[1] HE Shiqing, LI Wanwan, DONG Shuqing, MOU Jingyi, LIU Yuying, WEI Siyu, LIU Zhao, ZHANG Jiaxin. Construction of a prognostic risk model of pyroptosis-related genes in breast cancer based on database [J]. Journal of Shandong University (Health Sciences), 2022, 60(8): 34-43.
[2] HAO Qiang, GAO Qi, ZHAO Ran, WANG Haitao, LIU Zhidong, JIANG Baofa. Effects of ambient temperature and relative humidity on rotavirus diarrhea among children under 5 years old in Shenzhen City during 2014-2016 [J]. Journal of Shandong University (Health Sciences), 2022, 60(2): 89-95.
[3] ZHAO Jie, LI Yan, LI Ming, YU Dexin. Spiral CT in differentiating benign and malignant tumors with myxoid degeneration [J]. Journal of Shandong University (Health Sciences), 2021, 59(4): 100-107.
[4] ZHANG Bei, ZHANG Xiulei, BASANG Pianduo, NIMA Ciren, SHI Dachun, CIREN Jiabu, YIN Tingting, HU Jun. Spatial epidemiological characteristics and prediction of tuberculosis in Shigatse City from 2011 to 2018 [J]. Journal of Shandong University (Health Sciences), 2021, 59(2): 108-113.
[5] XIAO Yang, TAO Yu, WANG Fangyi, LIANG Yuxiu, ZHANG Jin, JI Xiaokang, WANG Zhiping. Association between PM2.5 and PM10 exposure with gestational diabetes mellitus in certain areas of Shandong Province [J]. Journal of Shandong University (Health Sciences), 2021, 59(12): 101-109.
[6] XIAO Yufei, FENG Jianing, WANG Xiaoxuan, MAO Qian, SHI Fuyan, WANG Suzhen. Prediction of the prognosis of 312 cirrhosis patients using the joint models and database [J]. Journal of Shandong University (Health Sciences), 2020, 1(9): 71-76.
[7] Qiang WU,Zekun HE,Ju LIU,Xiaomeng CUI,Shuang SUN,Wei SHI. A research on multi-modal MRI analysis based on machine learning for brain glioma [J]. Journal of Shandong University (Health Sciences), 2020, 1(8): 81-87.
[8] XU Yuanyou, YANG Yachao, WANG Chunxia, MA Xiaotian, XUE Fuzhong, LIU Yanxun, WANG Ping. A risk prediction model of gastritis based on examination cohort [J]. Journal of Shandong University (Health Sciences), 2019, 57(6): 112-116.
[9] CHEN Ou, LI Guoyong, LIU Aihong, ZHU Xiaobo, CHEN Shaojie, WANG Yibiao. Anti-inflammatory mechanism of ephedra treatment of asthma based on network pharmacology [J]. Journal of Shandong University (Health Sciences), 2019, 57(1): 55-61.
[10] LIU Xiaodi, MA Jie, XIU Jingwei, CUI Qingxia, LI Wangchen,WANG Zaixiang. Application of multiple seasonal model in prediction of tuberculosis epidemic [J]. Journal of Shandong University (Health Sciences), 2018, 56(9): 71-76.
[11] WANG Peizhu, ZHENG Zhaolei, LI Runzi, XU Qinqin, KANG Fengling, XU Qing, LI Xiujun. Effect of diurnal temperature range on incidence of measles in Jinan City [J]. Journal of Shandong University (Health Sciences), 2018, 56(8): 101-106.
[12] LI Xinyang, LI Sujuan, LIU Xiaodi, FAN Antong, YAN Ping, LIU Hongqing. Application of multiple seasonal autoregressive integrated moving average model in the prediction of viral hepatitis A [J]. Journal of Shandong University (Health Sciences), 2018, 56(12): 103-108.
[13] XU Qinqin, LI Runzi, LIU Yafei, SUN Yuanying, ZHENG Zhaolei, WANG Peizhu, WANG Zhiqiang, LI Xiujun. Effects of temperature on hemorrhagic fever with renal syndrome based on the distributed lag non-linear models in Qingdao City [J]. Journal of Shandong University (Health Sciences), 2018, 56(1): 90-96.
[14] SU Ping, YANG Yachao, YANG Yang, JI Jiadong, DAYIMU Alimu, LI Min, XUE Fuzhong, LIU Yanxun. Prediction models on the onset risks of type 2 diabetes among the health management population [J]. JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES), 2017, 55(6): 82-86.
[15] ZHANG Guang, WANG Guangyin, WU Hongyan, ZHANG Hongyu, WANG Tingting, LI Jiqing, LI Min, KANG Fengling, LIU Yanxun, XUE Fuzhong. A prediction model of hyperlipidemia risk based on the health management population [J]. JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES), 2017, 55(6): 72-76.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] LYU Longfei, LI Lin, LI Shuhai, QI Lei, LU Ming, CHENG Chuanle, TIAN Hui. Application of laparoscopic fine needle catheter jejunostomy in minimally invasive McKeown resection of esophageal cancer[J]. Journal of Shandong University (Health Sciences), 2020, 1(7): 77 -81 .
[2] SHAO Haigang, WANG Xuan, WANG Qing. Anatomy of the root canal system of mandibular first premolar in population of Shandong Province[J]. JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES), 2014, 52(9): 85 -89 .
[3] SHI Shuang, LI Juan, MI Qi, WANG Yunshan, DU Lutao, WANG Chuanxin. Construction and application of a miRNAs prognostic risk assessment model of gastric cancer[J]. Journal of Shandong University (Health Sciences), 2020, 1(7): 47 -52 .
[4] HUANG Fei,WANG Huaijing,XING Yi,GAO Wei,LI Yonggang,XING Ziying,LI Zhenzhong. Protective effects of NGF and GM1 on primary sensory neurons in SD rat with sciatic nerve injury[J]. JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES), 2006, 44(4): 332 -335 .
[5] JIANG Bao-dong,MA Xiang-xing,WANG Qing,WANG Qian,FENG Xiao-yuan,LI Ke,YU Fu-hua. Imaging parameters of multislice spiral CT venography in cerebral veins[J]. JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES), 2008, 46(11): 1084 -1086 .
[6] LI Yu-liang,WANG Yong-zheng,WANG Xiao-hua,ZHANG Fu-jun,ZHU Li-dong,ZHANG Wang-ming,LI Zheng,LI Zhen-jia,ZHANG Kai-xian. I implantation combined with Gemcitabine in the treatment of advanced pancreatic cancer[J]. JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES), 2007, 45(4): 393 -396 .
[7] TANG Fang1,2, ZHANG Yingqian3, WANG Zhiqiang4, KANG Dianmin4,
WANG Jiezhen1, XUE Fuzhong1. A 2D minimal spanning tree model of the spatial structures of natural focal disease[J]. JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES), 2009, 47(01): 106 -110 .
[8] LI Wei,LI Dao-wei,YE Qian,GAO Shun-cui,JIANG Shu-juan.

Diagnostic value of transbronchial needle aspiration in paratracheal mediastinal lesions

[J]. JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES), 2008, 46(11): 1063 -1065 .
[9] WANG Xiaoju1，　WANG Mingming2，　XU Wansu2,　ZHAO Shengmei3，CUI Sunan2，　LI Xiaoying2，　LIU Chunhua1
. Expression of peripheral blood lymphocyte ubiquitin mRNA in chronic HBV infection with active inflammation and its clinical significance[J]. JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES), 2009, 47(02): 58 -61 .
[10] ZHU Xiaoli1, GUO Shuling1, SU Lei1, FENG Yuxin2, YUAN Fangshu1. Extraction of total proteins from demodex and qualification of their molecular weights[J]. JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES), 2014, 52(5): 58 -62 .
Copyright 2018 © Editorial office of Journal of Shandong University (Health Sciences)
Supported by Beijing Magtech Co.Ltd