关键词: 白纹伊蚊, 布雷图指数, 人诱停落法, 网格化监测

Abstract: Objectve To explore a grid-based model for vector mosquito surveillance, and to provide a scientific basis for surveillance and control of mosquito-borne diseases. Methods From July to October 2019, three subdistricts (including the natural paths within the subdistricts) in the north, middle, and south of Jing'an district, Shanghai, China were divided into adjacent surveillance blocks with an area of 400 m×400 m. The Breteau index (BI) and human-baited landing method were used to conduct on-site surveillance in blocks. The effectiveness of grid-based surveillance was evaluated by comparing the results of different geographical locations and surveillance blocks. Excel 2016 and SPSS 16.0 softwares were used for data processing, and statistical analyses such as Kruskal-Wallis rank sum test and Spearman correlation analysis were performed. Results A total of 21 surveillance blocks were demarcated and 8 rounds of surveillance were conducted. The langding index (LI) of Aedes albopictus was 4.06 mosquitoes/person·hour, with the highest value of 9.58 mosquitoes/person·hour in late July, followed by 7.62 mosquitoes/person·hour in mid-July and 5.62 mosquitoes/person·hour in mid-August; there was no significant difference in the LI between north, middle, and south areas (Z=0.587, P=0.746). The mean BI was 27.21, with the highest value of 40.64 in mid-July, followed by 37.90 in late July and 35.56 in mid-August; the changes over time were basically the same as those of LI, with a significant difference between north, middle, and south areas (Z=47.161, P<0.001). The LI and BI were counted in blocks, with significant differences between surveillance blocks; the most significant inter-block difference was observed in mid-July; the mean LI was 7.62 mosquitoes/person·hour, with the highest value of 29.00 mosquitoes/person·hour and the lowest value of 0 mosquito/person·hour; the mean BI was 44.07, with the highest value of 178.57 and the lowest value of 0; the difference between the surveillance blocks decreased gradually with decreasing density. There were significant differences in both LI and BI between different places (Z=18.747 and 18.722, respectively, P=0.001 and 0.001, respectively), with the highest value in schools, followed by residential areas. Conclusion Mosquito density is affected by multiple factors such as the environment and the implementation of control measures. There are also differences in mosquito breeding in different places and regions. Therefore, it is recommended to expand the surveillance coverage by adopting grid-based surveillance model in vector mosquito surveillance, which will make the results more representative.

Key words: Aedes albopictus, Breteau index, Human-baited landing method, Grid-based surveillance