The dynamic response relationship between the incidence of Japanese encephalitis and qitameteorological factors in Gansu Province from 2014 to 2018
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摘要:
目的 运用向量自回归(vector autoregressive, VAR)模型分析甘肃省流行性乙型脑炎(简称乙脑)发病与气象因素的动态响应关系，为不同气象条件下乙脑的防控提供科学依据。 方法 用2014年1月-2018年12月乙脑发病率和同期气象数据建立VAR模型，通过脉冲响应和方差分解定量分析气象因素对乙脑发病的影响。 结果 VAR(2)模型的拟合优度为0.95，调整后拟合优度为0.95，希尔不等系数(Theil inequality coefficient, TIC)为0.06。平均气温、日照时数和降雨量对乙脑发病率变化的贡献到第6期分别为9.52%、2.13%和38.62%。 结论 VAR(2)模型可用于分析乙脑发病与气象因素的动态关系，乙脑的防控及预测预警可考虑结合当地气象因素。 Abstract:Objective To use a vector autoregressive(VAR) model to analyze the dynamic response relationship between the incidence of Japanese encephalitis(JE) and meteorological factors in Gansu Province, and to provide a scientific basis for the prevention and control of JE in different weather conditions. Methods The incidence of JE from January 2014 to December 2018 and the meteorological data of the same period were used to establish a multivariate VAR model, and impulse response and variance decomposition were used to analyze the impact of meteorological factors on the incidence of JE quantitatively. Results The goodness of fit of the VAR(2) model was 0.95, the adjusted goodness of fit was 0.95, and the theil inequality coefficient was 0.06. The contribution of average temperature, hours of sunshine and rainfall to the change of JE incidence was 9.52%、2.13% and 38.62%, respectively, to the six period. Conclusions The VAR(2) model can be used to analyze the dynamic relationship between the incidence of JE and meteorological factors. The prevention and control of JE, the prediction and early warning can be combined with local meteorological factors. -
Key words:
- Japanese encephalitis /
- Incidence /
- Meteorological factors /
- Vector autoregressive model
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图 1 2014-2018年甘肃省乙脑月发病率及气象因素基本情况
Figure 1. The basic information of monthly incidence of JE and meteorological factors in Gansu Province from 2014 to 2018
表 1 VAR模型最优滞后阶数的确定
Table 1. Determine the optimal lag order of the VAR model
Lag LogL LR FPE AIC SC HQ 0 -376.34 NA 11.93 13.83 13.98 13.89 1 -297.00 144.26 1.19 11.53 12.26 11.81 2 -251.24 76.54 a 0.41 a 10.45 11.76 a 10.95 a 3 -235.54 23.98 0.42 10.46 12.35 11.18 4 -219.73 21.84 0.45 10.46 12.94 11.42 5 -202.41 21.42 0.46 10.41 a 13.48 11.60 注: a根据各准则选择的滞后阶数。 
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表 2 Granger因果检验结果
Table 2. The results of Granger causality test
零假设H0 f值 P值 结论 AT不是INC的Granger成因 4.44 0.01 拒绝H0 HS不是INC的Granger成因 3.26 0.04 拒绝H0 RF不是INC的Granger成因 8.90 0.00 拒绝H0
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表 3 VAR(2)模型方差分解分析
Table 3. Variance decomposition analysis of VAR(2) model
滞后期 sx AT HS RF INC 1 26.77 3.91 0.34 27.29 68.46 2 29.12 3.04 0.57 43.09 53.30 3 30.78 4.69 0.80 41.51 53.00 4 30.97 7.79 1.56 39.64 51.01 5 31.22 9.26 1.97 38.81 49.96 6 31.99 9.52 2.13 38.62 49.73
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