Application of ARIMA model and LSTM model in predicting the incidence trend of brucellosis

Abstract

Abstract: Objective To analyze the incidence trend of brucellosis in Xinjiang Production and Construction Corps,and explore the application of autoregressive integrated moving average model（ARIMA） and long short-term memory（LSTM） model in the prediction of brucellosis incidence. Methods Based on monthly reported cases of brucellosis from 2010 to 2022,ARIMA and LSTM models were established to fit and predict the incidence of brucellosis in the Xinjiang Production and Construction Corps. The prediction performance of the models was evaluated by comparing the root mean square error（RMSE）,mean absolute error（MAE）,and coefficient of determination（R²）. Results The RMSE and MAE fitted and predicted by ARIMA（1,0,1）（0,1,2）₁₂ model were 19.72,16.40,12.02 and 8.16,respectively,and the fitted R² was 82.51. The RMSE and MAE fitted and predicted by LSTM neural network model were 13.37,10.88,10.26 and 7.71,respectively,and the fitted R² was 84.70. LSTM model had better fitting and prediction effect than ARIMA（1,0,1）（0,1,2）₁₂. The ARIMA model and LSTM model predict 773 and 789 cases of brucellosis in the Xinjiang Production and Construction Corps from 2023 to 2024,respectively. Conclusion In the past 13 years,the incidence of brucellosis in the Xinjiang Production and Construction Corps had shown an overall upward trend,and there was a certain seasonal trend. LSTM model can better fit and predict the incidence and trend of brucellosis,and the model effect is better than ARIMA（1,0,1）（0,1,2）₁₂ model,which can improve the prediction accuracy to a certain extent and provide reference for the prevention and control of brucellosis.

Key words: Brucellosis, Autoregressive integrated moving average model, Long short-term memory model, Disease prediction

CLC Number:

R181.2

FAN Ben, WANG Tongmin, ZHAO Qian, YANG Liugen, MA Xiaoling, LI Fanka. Application of ARIMA model and LSTM model in predicting the incidence trend of brucellosis. [J]OCCUPATION AND HEALTH, 2024, 40(17): 2377-2382.

References

[1] JIA P,JOYNER A.Human brucellosis occurrences in inner mongolia,China:A spatio-temporal distribution and ecological niche modeling approach[J].BMC Infect Dis,2015,15:36.
[2] ZHENG R,XIE S,LU X,et al.A systematic review and meta-analysis of epidemiology and clinical manifestations of human brucellosis in China[J].Biomed Res Int,2018,2018:5712920.
[3] 康育慧,崔咏梅,曹文君.2011—2017年我国人间布鲁氏菌病发病的长期趋势和季节性研究[J].中国卫生统计,2018,35(6):895-897.
[4] FRANC K A,KRECEK R C,HSLEr B N,et al.Brucellosis remains a neglected disease in the developing world:a call for interdisciplinary action[J].BMC Public Health,2018,18(1):125.
[5] 张翀. 长短期记忆网络深度学习模型对人布鲁氏菌病预测研究[D].沈阳:中国医科大学,2021.
[6] ZHAO J,DENG F,CAI Y,et al.Long short-term memory-fully connected(LSTM-FC) neural network for PM_2.5 concentration prediction[J].Chemosphere,2019,220:486-492.
[7] PATHAN R K,BISWAS M,KHANDAKER M.Time series prediction of COVID-19 by mutation rate analysis using recurrent neural network-based LSTM model[J].Chaos Solitons Fractals,2020,138:110018.
[8] LIAO Z Y,ZHANG X N,ZHANG Y H,et al.Seasonality and trend forecasting of tuberculosis incidence in Chongqing,China[J].Interdiscip Sci,2019,11(1):77-85.
[9] TIAN C W,WANG H,LUO X M.Time-series modelling and forecasting of hand,foot and mouth disease cases in China from 2008 to 2018[J].Epidemiol Infect,2019,147:e82.
[10] VALENCIA V N,DIAZ Y,PASCALE J M,et al.Assessing the effect of climate variables on the incidence of dengue cases in the metropolitan region of panama city[J].Int J Environ Res Public Health,2021,18(22):12108.
[11] MOLINA L L,ANGÓN E,GARCíA A,et al.Time series analysis of bovine venereal diseases in La Pampa,Argentina[J].PLoS One,2018, 13(8):e0201739.
[12] 李沁林,刘雪娜.2008—2018年中国甲乙类传染病流行病学特征[J].泰山医学院学报,2021,42(1):13-16.
[13] LAI S J,CHEN Q L,LI Z J.Human brucellosis:An ongoing global health challenge[J].China CDC Wkly,2021,3(6):120-123.
[14] 向泽林,顾伟玲,富小飞,等.2010—2021年嘉兴市布鲁氏菌病流行特征[J].预防医学,2023,35(1):41-43.
[15] 陈礼朋,张淼,李新生,等.我国人畜间布鲁氏菌病流行状况[J].中国动物检疫,2018,35(10):1-5.
[16] 于维君,王子江,毛玲玲,等.2016—2020年辽宁省人间布鲁氏菌病流行特征分析[J].疾病监测,2021,36(12):1257-1260.
[17] 包红红,孟巧玲,韩俊锋.灰色模型预测河南省安阳市人布鲁菌病发病趋势[J].中华地方病学杂志,2017,36(10):706-708.
[18] 彭阳. ARIMA模型在预测南阳市人间布鲁氏菌病发病风险中的运用[J].江苏预防医学,2021,32(4):431-433.
[19] 付全. 锡林郭勒盟布鲁氏菌病流行特征分析和ARIMA模型的建立[D].呼和浩特:内蒙古医科大学,2021.
[20] 倪茹玉,胡婉,张恒川,等.ARIMA乘积季节模型与LSTM神经网络模型对我国麻疹发病数预测效果的比较[J].现代预防医学,2023, 50(1):177-182.
[21] TSAN Y T,CHEN D Y,LIU P Y,et al.The prediction of influenza-like illness and respiratory disease using LSTM and ARIMA[J].Int J Environ Res Public Health,2022,19(3):1858.
[22] 高秋菊,周宇畅,赵树青,等.ARIMA乘积季节模型和LSTM深度神经网络对石家庄市手足口病疫情预测效果的比较[J].中华疾病控制杂志,2020,24(1):73-78.
[23] 马晓梅,史鲁斌,其木格,等.基于ARIMA乘积季节模型和Holt-Winters季节模型的梅毒月发病率预测[J].郑州大学学报(医学版),2018,53(1):79-84.
[24] 杨敏雪,于斐,王培生,等.基于ARIMA和LSTM神经网络的乌鲁木齐市乙型肝炎发病预测研究[J].现代预防医学,2022,49(16):2903-2907.