滑动平均自回归差分模型在蚊虫密度监测数据中的应用

谢博; 顾盈培; 冯磊; 刘汉昭; 刘俊; 郝莉鹏

doi:10.19428/j.cnki.sjpm.2020.19185

滑动平均自回归差分模型在蚊虫密度监测数据中的应用

Mosquito density monitoring data by ARIMA model

摘要

摘要:
目的分析预测上海市浦东新区蚊虫密度指数的变化趋势，为虫媒疾病疫情风险控制以及处置措施提供数据支持。
方法整理2011—2015年上海市浦东新区市级监测点蚊虫人工小时法的监测结果，使用滑动平均自回归差分模型(ARIMA)分析上海市浦东新区人工小时法密度指数的变化趋势。
结果2011—2015年，浦东新区市级监测点人工小时法共开展监测135次，蚊虫密度指数平均值为6.17只/h(人工)，标准差为4.93，最小值为0，最大值为18只/h(人工)。指数呈明显的周期性，每年最高峰均出现在7—8月。ARIMA拟合最优模型为ARIMA(2，0，1)，模型拟合统计量R²为0.808，Q检验值为19.632，显著性检验结果为0.186，不能拒绝残差为白噪声的结果。模型自回归参数AR₁为1.866，AR₂为－0.907，滑动平均参数MA为0.999。
结论ARIMA模型可用于蚊虫密度监测数据的预测，但监测频率低、循环周期不固定对预测结果影响较大。

Abstract:
ObjectiveTo forecast the trend of mosquito density index in Pudong New Area, Shanghai so as to provide evidence for disease control and risk-control measures for vector-borne diseases.
MethodsMosquito monitoring data was collected in Pudong New Area between 2011 and 2015 at the city-level monitoring sites for analysis on the trend of the mosquito density index in Pudong New Area of Shanghai by using the Autoregressive Integrated Moving Average Model (ARIMA).
ResultsFrom 2011 to 2015, a total of 135 times labor-hour monitoring were carried out at the city-level monitoring points in Pudong New Area.The mosquito density index averaged 6.17/labor-hour with a standard deviation at 4.93, S=0, 18/labor-hour.Using ARIMA to analyze the change trend of mosquito density index in Pudong New Area, ARIMA(2, 0, 1)became the final fitting model, with R²=0.808.In the model, the Ljung-Box Q test value was 19.632(AR₁=1.866, AR₂=-0.907), and MA parameter was 0.999.
ConclusionARIMA model can be used to predict mosquito density monitoring data, but low monitoring frequency and irregular cycle length will affect the prediction results.

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