四川省泸州市流行性腮腺炎时空流行病学特征

目的 描述2010—2020年泸州市流行性腮腺炎的时间和空间流行病学特征,构建时间序列ARIMA模型进行短期预测,为泸州市流行性腮腺炎的综合防控提供参考依据。 方法 采用Microsoft Excel 2020整理疫情数据并绘制统计图,ArcGIS 10.6构建空间分布地图并进行空间自相关分析,Eviews 10构建月度发病数ARIMA时间序列模型,对泸州市2010—2020年流行性腮腺炎进行时空流行病学特征分析和发病趋势短期预测。 结果 2010—2020年泸州市7 个区县均有流行性腮腺炎病例报告,每年的高发区县不相同,各年份均不存在空间自相关性;年均报告发病率22.17/10万,高发年份为2012年(37.51/10万)、2013年(35.72/10万)和2019年(31.80/10万),发病低谷在2015年(10.90/10万);整体上有4—7月和11月至次年1月两个季节高峰,以4—7月为主;构建的ARIMA(1,1,1)(1,1,2)₁₂模型是最佳模型,预测2021年4—12月报告发病数稍低于2020年同期。 结论 2010—2020年泸州市流行性腮腺炎发病率总体呈下降趋势,不存在空间聚集性,ARIMA(1,1,1)(1,1,2)₁₂模型能够较好地进行时间序列拟合和短期预测,呈现的时空分布特征及其发病趋势能够为疾病预防控制工作提供参考。

Temporal and spatial epidemiological characteristics of mumps in Luzhou City,Sichuan Province

Objective To describe the temporal and spatial epidemiological characteristics of mumps in Luzhou City from 2010 to 2020, to construct the ARIMA time series model for short-term prediction, and to provide a reference basis for comprehensive prevention and control of mumps. Methods Microsoft Excel 2020 was used to sort the epidemic data and draw the statistical map. ArcGIS 10.6 was employed to construct a spatial distribution map, and the spatial autocorrelation was analyzed. Eviews 10 was applied to constructing the ARIMA time series model of monthly incidence. The temporal and spatial epidemiological characteristics of mumps in Luzhou City from 2010 to 2020 were analyzed, and the short-term incidence trend was predicted. Results Mumps cases were reported in 7 districts and counties in Luzhou City from 2010 to 2020. Districts and counties with a high incidence of mumps were different in each year, and there was no spatial autocorrelation in each year. The annual average reported incidence rate was 22.17/100,000. A high incidence of mumps was found in 2012 (37.51/100,000), 2013 (35.72/100,000) and 2019 (31.80/100,000), but a low incidence was found in 2015 (10.90/100,000). On the whole, there were two seasonal peaks from April to July and from November to January of the following year, and the main onset months were from April to July. The ARIMA (1,1,1)(1,1,2)₁₂ model constructed was the best model, which predicted that the number ofreported cases from April to December in 2021 was slightly lower than that of the corresponding period in 2020. Conclusion The incidence rates of mumps in Luzhou City from 2010 to 2020 generally showed a downward trend, and there was no spatial aggregation. The ARIMA(1,1,1)(1,1,2)₁₂ model can be used for time series fitting and short-term prediction. The presented spatiotemporal distribution characteristics and incidence trend can provide references for prevention and control of the disease.