XIE Bo, GU Ying-pei, FENG Lei, LIU Han-zhao, LIU Jun, HAO Li-peng. Mosquito density monitoring data by ARIMA model[J]. Shanghai Journal of Preventive Medicine, 2020, 32(12): 983-987. DOI: 10.19428/j.cnki.sjpm.2020.19185
Citation: XIE Bo, GU Ying-pei, FENG Lei, LIU Han-zhao, LIU Jun, HAO Li-peng. Mosquito density monitoring data by ARIMA model[J]. Shanghai Journal of Preventive Medicine, 2020, 32(12): 983-987. DOI: 10.19428/j.cnki.sjpm.2020.19185

Mosquito density monitoring data by ARIMA model

  • 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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