中国痢疾发病率的时空分析及短期预测

张孟媛; 吕媛; 刘桃成; 易尚辉; 查文婷

doi:10.16462/j.cnki.zhjbkz.2019.08.005

中国痢疾发病率的时空分析及短期预测

doi: 10.16462/j.cnki.zhjbkz.2019.08.005

410013 长沙, 湖南师范大学医学院分子流行病学湖南省重点实验室

基金项目:

湖南省教育厅 XiangJiaotong [2017]452

湖南省教育厅 XiangJiaotong [2017]451

详细信息

通讯作者:
查文婷. E-mail: 1509946865@qq.com

中图分类号: R155.3
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- 文章访问数: 353
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- 被引次数: 0
出版历程
- 收稿日期: 2019-03-18
- 修回日期: 2019-06-17
- 刊出日期: 2019-08-10

Spatio-temporal analysis and short-term prediction of the incidence of dysentery in China

Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha 410013, China

Funds:

Hunan education department XiangJiaotong [2017]452

Hunan education department XiangJiaotong [2017]451

More Information

Corresponding author: ZHA Wen-ting. E-mail: 1509946865@qq.com

摘要

摘要: 目的分析我国大陆31个省、市、自治区2004-2016年间痢疾发病率的时空相关性，预测全国痢疾短期发病率的效果。方法获取我国2004-2016年痢疾发病率资料。使用Arcgis10.5和Geoda软件（2018稳定版）制作可视化发病率分级地图并分析空间相关性，使用自回归移动平均（auto-regressive integrated moving average，ARIMA）模型预测2017年全国痢疾发病率并评价模型效果。结果我国2004-2016年痢疾发病率逐年降低，西部地区痢疾发病率普遍高于东部地区，但北京、天津发病率依然较高。发病率基本不存在全局相关，但存在局部聚集。青海由高高聚集转为低高聚集，内蒙古和山西由无局部聚集转为低高聚集，陕西长期呈高高聚集，东南沿海地区长期处于低低聚集。预测全国痢疾月发病率的模型为ARIMA（1，0，0）（2，1，1）₁₂模型，实际发病率均落在预测区间内。结论 2004-2016年痢疾发病率在空间上没有明显的移动性但有聚集性，北京、天津、陕西及西部地区发病情况依然严峻。使用ARIMA模型可以很好的预测短期痢疾月发病率，应根据发病趋势和聚集情况以及短期预测结果综合制定防控措施。
- 痢疾 /
- 分级地图 /
- 空间相关性 /
- ARIMA模型
Abstract: Objective The aim is to analyze the spatial-temporal correlation of dysentery incidence in 31 provinces, municipalities and autonomous regions in China from 2004 to 2016, and to predict the short-term incidence of dysentery in China. Methods Data about the incidence of dysentery from 2004 to 2016 was collected. Arcgis and Geoda were used to create visualized grading maps and analyze spatial correlation. The auto-regressive integrated moving average model (ARIMA)was used to predict the incidence of dysentery in 2017 and evaluate the prediction accuracy of the model. Results The incidence of dysentery in China declined with each passing year from 2004 to 2016. The incidence of dysentery in the western region was significantly higher than the eastern region, except high incidence rate in Beijing and Tianjin. There was no significantly global correlation in the incidence rate, but there was local aggregation. Qinghai had turned from high-level aggregation to low-level accumulation. Inner Mongolia and Shanxi had changed from no local aggregation to low-high accumulation. Shaanxi has long been high-high, and the southeast coastal areas had been low-low accumulation for a long time. The optimal model ARIMA (1, 0, 0) (2, 1, 1)₁₂ was established to predict the incidence of dysentery, and the prediction results were roughly consistent with the observations. Conclusion The incidence of dysentery from 2004 to 2016 is not spatially mobile but clustered. The incidence of dysentery in Beijing, Tianjin, Shaanxi and most of the western regions is severe. The ARIMA model is suitable for forecasting the incidence of short-term dysentery. And our analysis may help prevent and control the incidence of dysentery in China.
- Dysentery /
- Hierarchical map /
- Spatial correlation /
- ARIMA model

HTML全文

图 1 2004-2016年全国痢疾发病率时间分布图

Figure 1. Time distribution of dysentery incidence in china from 2004 to 2016

下载: 全尺寸图片幻灯片

图 2 2004-2016年全国痢疾发病率分级地图(/10万)

Figure 2. The incidence of dysentery from 2004 to 2016 in China (/100 000)

下载: 全尺寸图片幻灯片

图 3 2004-2016年全国痢疾发病率空间局部相关(LISA)

Figure 3. Local spatial autocorrelation ofdysentery incidence from 2004 to 2016 in China(LISA)

下载: 全尺寸图片幻灯片

图 4 2004-2016年部分省份与全国痢疾年发病时间分布图

Figure 4. Time distribution of dysentery incidence in china from 2004 to 2016

下载: 全尺寸图片幻灯片

图 5 2004年1月-2016年12月全国痢疾发病率时间序列图

Figure 5. Time series plots of monthly dysentery incidence from January 2004 to December 2016

下载: 全尺寸图片幻灯片

图 6 2017年1-12月全国痢疾发病率预测曲线

Figure 6. Prediction of dysentery incidence from January to December in 2017

下载: 全尺寸图片幻灯片

表 1 2004-2016年全局空间自相关分析结果

Table 1. Global spatial autocorrelation of dysentery incidence from 2004 to 2016

年份	Moran’s I	Z值	P值
2004	0.008	1.377	0.077
2005	0.014	0.651	0.203
2006	0.015	0.628	0.208
2007	0.023	0.783	0.162
2008	0.013	0.575	0.221
2009	0.056	1.141	0.105
2010	0.002	0.545	0.238
2011	0.087	1.643	0.045
2012	0.101	1.715	0.044
2013	0.079	1.356	0.066
2014	0.083	1.483	0.068
2015	0.047	0.992	0.128
2016	0.034	0.859	0.167

下载: 导出CSV

表 2 ARIMA模型参数估计

Table 2. The estimation of ARIMA model parameters

参数	估计值	S_x	t值	P值
非季节参数
AR(1)	0.659	0.065	10.138	＜0.001
季节性参数
SAR(1)	-0.934	0.345	-2.707	0.007
SAR(2)	-0.363	0.126	-2.881	0.004
SMA(1)	0.537	0.263	2.041	0.042

下载: 导出CSV

表 3 ARIMA模型拟合结果

Table 3. ARIMA model fitting results

模型	统计量		Box-Ljung
模型	BIC	AIC	χ²值	P值
ARIMA (1, 0, 0) (2, 1, 1)₁₂	-329.70	-347.52	0.60	0.438

下载: 导出CSV

表 4 2017年痢疾实际发病率与预测值比较(/10万)

Table 4. Comparison of observations and predicted values of dysentery in 2017 (/100 000)

月份(月)	实际值	预测值	预测区间	绝对误差	相对误差(%)
1	0.371 01	0.364 5	[0.314 8, 0.422 2]	0.006 5	1.75
2	0.378 03	0.353 6	[0.289 7, 0.431 4]	0.024 4	6.46
3	0.425 54	0.445 7	[0.352 7, 0.563 3]	0.020 2	4.74
4	0.518 84	0.563 4	[0.434 6, 0.730 5]	0.044 6	8.59
5	0.793 73	0.853 1	[0.645 2, 1.128 0]	0.059 4	7.48
6	1.024 73	1.065 5	[0.793 4, 1.430 9]	0.040 8	3.98
7	1.180 00	1.201 9	[0.884 0, 1.634 3]	0.021 9	1.86
8	1.107 54	1.215 2	[0.884 9, 1.668 9]	0.107 7	9.72
9	0.801 18	0.990 4	[0.715 3, 1.371 2]	0.189 2	23.62
10	0.617 41	0.768 4	[0.551 3, 1.071 1]	0.150 9	24.46
11	0.441 81	0.484 6	[0.345 8, 0.679 1]	0.042 8	9.69
12	0.358 28	0.420 2	[0.298 5, 0.591 6]	0.061 9	17.28

下载: 导出CSV

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