Application of circular statistics in temporal distribution of adult mosquitoes in Qingdao, Shandong Province, China, 2021–2023

Abstract

Abstract Background Analyses of the temporal distribution of mosquitoes are presented in statistical charts, but it is difficult to prove in statistics whether differences in peak periods exist among different years or habitats. This study aimed to investigate the application of circular statistics in determining the peak period and a comparison of differences. Methods Surveillance of adult mosquitoes was conducted twice a month by light traps in five different habitats from March to November for 3 years (2021–2023) in Qingdao, Shandong Province, China. The Kruskal–Wallis test was performed to determine the differences in mosquito density among different years and habitats. Circular statistics and line charts were employed to determine the peak period and a comparison of differences. Results Among a total of 14,834 adult mosquitoes comprising five mosquito species from four genera, Culex pipiens pallens was dominant and accounted for 89.6% of the specimens identified. Aedes albopictus, Armigeres subalbatus, and Anopheles sinensis made up 5.7%, 4.2%, and 0.5%, respectively. Culex tritaeniorhynchus accounted for less than 0.1%. The mean mosquito density (females/trap night) for the trapping period was 10.3 in 2021, 5.6 in 2022, and 3.6 in 2023. Among five habitats, the highest mosquito density was 8.9 in livestock sheds, followed by 6.8 in parks, 5.9 in rural dwellings, 5.5 in urban dwellings, and 5.4 in hospitals. No statistically significant differences were found among different years (H = 1.96, d.f. 2, P = 0.376) and habitats (H = 0.45, d.f. 4, P = 0.978). Overall, the peak period of mosquito activity fell in the months from June to September. The peak period among 3 years differed significantly (F (2,7022) = 119.17, P 0.05). Conclusion Circular statistics could be effectively combined with statistical charts to elucidate the peak period of mosquitoes and determine the differences in statistics among different years and habitats. These findings will provide valuable information for mosquito control and public health management. Graphical Abstract

Keywords

• Light trap
• Mosquito
• Surveillance
• Seasonality
• Circular statistics