Application of multivariable grey model (1, n) in prediction of aedes albopictus density

Objective To make a short-term prediction about aedes albopictus density by multivariable grey model (MGM) (1,n), based on the aedes trap monitoring data and climate data. Methods Field investigation on aedes albopictus density at a village in Guangzhou was performed using aedes trap monitoring and traditional larval monitoring, and meteorological data were collected simultaneously. MGM (1,n) was established based on the variables with maximum slope degree of grey incidence, which was calculated by grey relational analysis utilizing mosquito and oviposition positive index (MOI), and five items of meteorological data and breteau index(BI), from July to November, 2014. The data in December was used to verify the prediction effect of the model. Results MOI had the maximum slope degree of grey incidence, then relative humidity, maximum mean temperature, precipitation, mean temperature and minimum mean temperature followed. MGM (1, 2) was developed by BI and MOI. The average absolute errors were 9.14 and 2.04 for BI and MOI, respectively. The average relative errors were 34.73% and 21.44%. When forecasting the density of aedes in December, the average absolute errors were 1.23 and 1.43 for BI and MOI, respectively. Conclusions MGM (1, 2) can be used to make a short-term prediction for aedes albopictus density.