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Influenza incidence prediction based on ARIMAX model including meteorological factors |
LÜ Xiaoli*, ZHU Yi, ZHU Junwei
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*Department of Public Health Mergency,Yuhang Center for Disease Control and Prevention, Hangzhou,Zhejiang 311100, China |
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Abstract Objective To evaluate the feasibility of autoregressive integrated moving average with explanatory variables ( ARIMAX ) model including meteorological factors on the prediction of influenza-like illness ( ILI ), so as to provide a basis for the monitoring and early warning of influenza. Methods The ILI data reported by four sentinel hospitals in Yuhang District of Hangzhou from the 1st week of 2014 to the 26th week of 2018 was collected, as well as the meteorological data during the same period. The ARIMAX model was established using the percentage of ILI cases in total outpatients ( ILI% ) data from the 1st week of 2014 to the 52nd week of 2017 and the meteorological factors selected by Lasso regression model. The ILI% from the 1st to 26th week of 2018 was predicted and compared with the actual values to verify the ARIMAX model. Results From the 1st week of 2014 to the 26th week of 2018, a total of 60 419 cases of ILI were reported by the four sentinel hospitals of Yuhang District, with ILI% of 1.29%. Lasso regression analysis showed that there was a positive correlation between weekly average absolute humidity and ILI% ( r=27.769 ), and a negative correlation between weekly average temperature and ILI% ( r=-0.117 ). The ARIMAX (1, 0, 0) ( 1, 0, 0 )12 with weekly average temperature and absolute humidity was selected as the optimal model, with the Bayesian information criterion (BIC) value of 81.30 and the mean absolute percentage error (MAPE) value of 15.77%. The MAPE value of the ARIMAX model predicting the ILI% from 1st to 26th week of 2018 were 43.75%. Conclusion The ARIMAX model including meteorological factors can be used to predict the prevalence of ILI, but the accuracy needs to be promoted.
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Received: 04 February 2021
Revised: 08 June 2021
Published: 10 August 2021
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[1] 高福.流感百年:推进流感防控和研究全球合作,中国在行动[J].中华实用儿科临床杂志,2019,34(2):81-82. [2] 中华人民共和国国家免疫规划技术工作组流感疫苗工作组.中国流感疫苗预防接种技术指南(2020—2021)[J].中华流行病学杂志,2020,41(10):1555-1576. [3] 余昭,孙琬琬,刘社兰,等.呼吸道传染病监测体系运行状况分析[J].预防医学,2021,33(1):101-103. [4] 沈冰,沈磊,倪晓芬,等.上海市原静安区成人流感样病例就诊百分比预测的自回归求和滑动平均模型构建与应用[J].上海预防医学,2017,29(5):346-350. [5] 黄智峰,刘晓剑,杨连朋,等.流行性感冒预警方法及其应用[J].疾病监测,2016,31(12):989-994. [6] 陈会杰,陈叶.2015—2017沈阳市流感样病例与气象因素相关性分析[J].预防医学论坛,2019,25(9):676-677,682. [7] 丁彦红,范俊杰,聂清,等.2017—2018年潍坊市流感样病例与气象因素相关性分析[J].实用预防医学,2020,27(5):554-557. [8] 刘欣,康敏,马文军,等.广州市气象因素与流感样病例关系的时间序列研究[J].环境卫生学杂志,2018,8(5):374-380. [9] 龚风云,王凯,樊旭成,等.乌鲁木齐市流感样病例与气象因素的ARIMAX模型预测分析[J].公共卫生与预防医学,2020,31(2):4-8. [10] 中华人民共和国国家卫生健康委员会.全国流感监测方案(2017年版)[EB/OL].[2021-06-08].http://www.nhc.gov.cn/cms-search/xxgk/getManuscriptXxgk.htm?id=ed1498d9e64144738cc7f8db61a39506. [11] 翟红楠. 深圳市流感与大气环境的关系研究及其预测模型的建立[D].武汉:中国地质大学,2009. [12] 王燕. 时间序列分析:基于R[M].北京:人民大学出版社,2015. [13] 王晨,郭倩,周罗晶.基于R语言的ARIMA模型对流感样病例发病趋势的预测[J].中华疾病控制杂志,2018,22(9):957-960. [14] 傅伟杰,谢昀,曾志笠,等.三种模型在江西省流感样病例预测中的应用与比较[J].中华疾病控制杂志,2019,23(1):101-105. [15] 赵棋锋,马珊珊,王吉玲,等.指数平滑法与ARIMA模型对流感样病例流行趋势的预测效果比较[J].预防医学,2020,32(4):381-383,387. [16] 沈钰钢. 嵊州市流感样病例监测结果及ARIMA模型预测[D].杭州:浙江大学,2017. |
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