Application of Structural Equation Models for Elucidating the Ecological Drivers of Anopheles sinensis in the Three Gorges Reservoir

Abstract

ObjectiveTo identify the major ecological drivers for malaria vector density using the structural equation model (SEM) in the Three Gorges Reservoir.MethodAn 11-year longitudinal surveillance of malaria vector as well as its related ecological factors was carried out in the Three Gorges Reservoir. The Delphi method was used to identify associated ecological factors. The structural equation model was repeatedly corrected and improved by the corrected index, combined with the actual situation. The final model was defined by relative simplicity, best fitting as well as the practicality.ResultThe final model indicated that the direct effects of temperature, livestock, humidity, and breeding on the vector were 0.015, −0.228, 0.450, 0.516 respectively, their total effects on the vector were 0.359, −0.112, 0.850, and 0.043 through different pathways.ConclusionSEM was effective and convenient in elucidating the mechanism by which malaria vector dynamics operated in this study. It identified that the breeding had the highest direct effect on vector and played a key role for mediating effect of temperature and humidity.

Spatio-temporal analysis of malaria vectors in national malaria surveillance sites in China

“…According to WHO recommendations [ 8 ], human landing catches were made by two adult volunteers from the local population working beside the bed-net with one sleeping person. Mosquitoes coming to bite the collectors or sleeping person were detected using a flashlight, collected with glass tubes (CDC backpack aspirator: John W. Hock Co., Florida, USA) and placed in the screened pint-sized containers.…”

Smart Disease Prevention App: Informing the Public in Their Own Geographic Location

“…Further, an article comparing the use of multiple regression and structural equation modeling (Pugesek & Tomer, 1995) influenced our analyses and model development. One additional article outlining the development of a health index gave insights into the possibilities and examples of measures that could be directly applied in health application development (Yanuar, Ibrahim, & Jemain, 2010).…”

Association of Anopheles sinensis average abundance and climate factors: Use of mosquito surveillance data in Goyang, Korea

“…In Korea, the monthly average temperature and monthly amount of precipitation were significantly correlated with average abundance of mosquitoes on Jeju Island [10]. In China, mosquito average abundance was significantly correlated with the monthly average temperature (r = 0.359, p < 0.05) and monthly average relative humidity (r = 0.850, p < 0.05) at the Three Gorges Reservoir; with the monthly average temperature (r = 0.958, p < 0.001), monthly average relative humidity (r = 0.746, p = 0.005), and monthly average precipitation (r = 0.725, p = 0.008) at Ningbo city; and the daily average relative humidity (r = 0.859, p = 0.029) at Yongcheng city [11][12][13].…”