Spatial aggregation and prediction model of other infectious diarrhea in China from 2010 to 2017
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摘要:
目的 了解2010-2017年全国其他感染性腹泻发病时空分布,并建立其他感染性腹泻疾病发病的预测模型。 方法 基于2010-2017年中国公共卫生科学数据中心(https://www.phsciencedata.cn/Share)平台报告的其他感染性腹泻数据,进行空间自相关分析并构建自回归移动平均混合(autoregressive integrated moving average, ARIMA)模型。 结果 全局自相关结果可得2014年、2015年、2016年全国其他感染性腹泻发病呈全局空间正相关,局部自相关结果显示中国2010-2014年和2016-2017年全国均出现“高-高”区域。模型测试结果显示季节乘积ARIMA(0, 1, 2) (0, 1, 1)12模型适用于构建模型的预测。ARIMA模型对该病短期预测有较好的效果。 结论 中国其他感染性腹泻发病总体呈上升趋势,发病在空间分布上有聚集性且有明显的季节性,需加强该病区域防控预防工作。 Abstract:Objective To investigate the spatiotemporal distribution of other infectious diarrhea incidences in China from 2010 to 2017, and to establish the prediction model for the incidence of other infectious diarrhea. Methods Based on other infectious diarrhea data reported by the China Public Health Science Data Center Platform (https://www.phsciencedata.cn/Share) from 2010 to 2017, spatial autocorrelation analysis was performed and autoregressive integrated moving average model (ARIMA) was constructed. Results According to the global autocorrelation results, the incidence of other infectious diarrhea in China in 2014, 2015, and 2016 showed a global spatial positive correlation. Local autocorrelation results showed that there were high-high regions in China in 2010-2014 and 2016-2017. The model test results showed that the seasonal ARIMA (0, 1, 2) (0, 1, 1)12 model was suitable for the prediction of model construction. ARIMA model had a good effect on the short-term prediction of the disease. Conclusions The incidence of other infectious diarrhea in China shows an upward trend, the spatial distribution of the incidence is clustered, and the incidence has obvious seasonality. It is necessary to strengthen the prevention and control of this disease in the region. -
Key words:
- Other infectious diarrhea /
- Spatial autocorrelation /
- Prediction of onset
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表 1 2010-2017年全国其他感染性腹泻发病全局空间自相关分析
Table 1. Global spatial autocorrelation analysis of the incidence of other infectious diarrhea in China from 2010 to 2017
年份(年) Moran’s I值 Z值 P值 空间自相关性 2010 0.147 -1.046 0.128 无 2011 0.158 -1.165 0.095 无 2012 0.136 -1.656 0.066 无 2013 0.112 -0.758 0.233 无 2014 0.197 2.176 0.029 有 2015 0.216 2.282 0.019 有 2016 0.238 2.387 0.017 有 2017 0.031 0.497 0.301 无 表 2 2010-2017年全国其他感染性腹泻发病局部空间自相关分析
Table 2. Local spatial autocorrelation analysis of other infectious diarrhea in China from 2010 to 2017
年份(年) 高-高区域 高-低区域 低-高区域 低-低区域 2010 1 1 3 3 2011 1 1 3 3 2012 2 1 4 3 2013 3 0 2 5 2014 2 0 4 4 2015 0 0 5 3 2016 3 0 5 5 2017 2 0 6 3 表 3 模型比较表
Table 3. Model comparison table
模型参数 AIC值 RMSE值 Ljung-Box P值 (0, 0, 2) (0, 1, 1)12 -57.31 0.147 0.742 (1, 1, 0) (0, 1, 1)12 -39.86 0.166 0.918 (1, 1, 2) (0, 1, 1)12 -53.18 0.144 0.951 (0, 1, 2) (0, 1, 2)12 -48.11 0.151 0.273 (0, 1, 2) (1, 1, 1)12 -48.42 0.150 0.281 (0, 1, 2) (1, 1, 2)12 -46.44 0.150 0.278 表 4 模型预测结果
Table 4. Model prediction results
预测月份 预测值 实际值 (95% CI)值 2017年7月 122 331.5 119 857.0 75 580.6~169 082.5 2017年8月 125 109.6 115 032.0 57 950.6~192 268.6 2017年9月 104 455.8 84 730.0 25 540.2~183 371.3 2017年10月 108 382.9 75 167.0 18 132.4~198 633.4 2017年11月 118 084.6 96 036.0 15 844.9~220 324.3 2017年12月 123 980.7 183 493.0 9 746.9~238 214.5 -
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